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The influence of anthropogenic CO2 emissions and emissions scenarios on the carbon–climate system is the primary driver of ocean and terrestrial sinks as the major negative feedbacks that determine the atmospheric CO2 levels, which then drive climate feedbacks through radiative forcing (Figure 5.2) (Friedlingstein et al., 2006; Jones et al., 2013; Jones and Friedlingstein, 2020). Biogeochemical feedbacks follow as an outcome of both carbon and climate forcing on the physics and the biogeochemical processes of the ocean and terrestrial carbon cycles (Figure 5.2) (Katavouta et al., 2018; Williams et al., 2019; Jones and Friedlingstein, 2020). Together, these carbon–climate feedbacks can amplify or suppress climate change by altering the rate at which CO2 builds up in the atmosphere through changes in the land and ocean sources and sinks (Figure 5.2; C.D. Jones et al., 2013; Raupach et al., 2014; Williams et al., 2019). These changes depend on the, often non-linear, interaction of the drivers (CO2 and climate) and processes in the ocean and land as well as the emissions scenarios (Figure 5.2; Sections 5.4 and 5.6) (Raupach et al., 2014; Schwinger et al., 2014; Williams et al., 2019). There is high confidence that carbon–climate feedbacks and their century scale evolution play a critical role in two linked climate metrics that have significant climate and policy implications: (i) the fraction of anthropogenic CO2 emissions that remains in the atmosphere, the so-called airborne fraction of CO2 (AF; Section 5.2.1.2, Figures 5.2 and 5.7, and FAQ 5.1); and (ii) the quasi-linear trend characteristic of the transient temperature response to cumulative CO2 emissions (TCRE; Section 5.5; MacDougall, 2016; Williams et al., 2016; Jones and Friedlingstein, 2020) and other GHGs (CH4 and N2O). This chapter assesses the implications of these issues from the perspective of carbon cycle processes (Figure 5.2) in Section 5.2 (historical and contemporary), Section 5.3 (changing carbonate chemistry), Section 5.4 (future projections), Section 5.5 (remaining carbon budget) and Section 5.6 (response to carbon dioxide removal and solar radiation modification).

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The airborne fraction is an important constraint for adjustments in carbon–climate feedbacks and reflects the partitioning of CO2 emissions between reservoirs by the negative feedbacks, which were 31% on land and 23% in the ocean for the decade 2010–2019 and also dominated the historical period (Figure 5.2; Table 5.1) (Friedlingstein et al., 2020). During the period 1959–2019, the airborne fraction has largely followed the growth in anthropogenic CO2 emissions with a mean of 44% and a large interannual variability (Ballantyne et al., 2012; Ciais et al., 2019; Friedlingstein et al., 2020, Section 5.2.1.2; Table 5.1). The negative feedback to CO2 concentrations is associated with its impact on the air–sea and air–land CO2 exchange through strengthening of partial pressure of CO2 (pCO2) gradients as well as the internal processes that enhance uptake. Two of these key processes are the buffering capacity of the ocean and the CO2 fertilization effect on gross primary production (Sections 5.4.1–5.4.4).

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Paleoclimatic proxy records extend beyond the variability of recent decadal climate oscillations and thus provide an independent perspective on feedbacks between climate and carbon cycle dynamics. According to reconstructions, these past changes were slower than the current anthropogenic ones, so they cannot provide an unequivocal comparison. Nonetheless, they can help appraise sensitivities and point towards potentially dominant mechanisms of change (Tierney et al., 2020) on (sub)centennial to (multi)millennial time scales.

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In the past, atmospheric CO2 concentrations reached much higher levels than present day (Cross-Chapter Box 2.1 and Figure 5.3). In particular, the Paleocene–Eocene thermal maximum (PETM), 55.9–55.7 Ma (Figure 5.3), provides some level of comparison with the current and projected anthropogenic increase in CO2 emissions (Chapter 2). Atmospheric CO2 concentrations increased from about 900 to around 2000 ppm in 3–20 kyr as a result of geological carbon release to the ocean–atmosphere system (Zeebe et al., 2016; Gutjahr et al., 2017; Cui and Schubert, 2018; Kirtland Turner, 2018). There is low to medium confidence in evaluations of the total amount of carbon released during the PETM, as proxy data constrained estimates vary from around 3000 to more than 7000 PgC, with methane hydrates, volcanic emissions, terrestrial and/or marine organic carbon, or some combination thereof, as the probable sources of carbon (Zeebe et al., 2009; Cui et al., 2011; Gutjahr et al., 2017; Elling et al., 2019; Jones et al., 2019; Haynes and Hönisch, 2020). Methane emissions related to hydrate/permafrost thawing and fossil carbon oxidation may have acted as positive feedbacks (Lunt et al., 2011; Armstrong McKay and Lenton, 2018; Lyons et al., 2019), as the inferred increase in atmospheric CO2 can only account for approximately half of the reported warming (Zeebe et al., 2009). The estimated, time-integrated carbon input is broadly similar to the RCP8.5 extension scenario, although CO2 emissions rates (0.3–1.5 Pg yr–1) and by inference the rate of CO2 accumulation in the atmosphere (4–42 ppm per century) during the PETM were at least 4–5 lower than during the modern era (from 1995 to 2014; Table 2.1; Zeebe et al., 2016; Gingerich, 2019).

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The Antarctic ice core record covering the past 800 kyr provides an important archive to explore the carbon–climate feedbacks prior to anthropogenic perturbations (Brovkin et al., 2016). Polar ice cores represent the only climatic archive from which past GHG concentrations can be directly measured. Major GHGs, CH4, N2O and CO2 generally co-vary on orbital time scales (Loulergue et al., 2008; Lüthi et al., 2008; Schilt et al., 2010b; Chapter 2), with consistently higher atmospheric concentrations during warm intervals of the past, pointing to a strong sensitivity to climate (Figure 5.4). Modelling work suggests that the carbon cycle contributed to globalise and amplify changes in orbital forcing, which are pacing glacial–interglacial climate oscillations (Ganopolski and Brovkin, 2017), with ocean biogeochemistry and physics, terrestrial vegetation, peatland, permafrost and exchanges with the lithosphere including chemical weathering, volcanic activity, sediment burial and marine calcium carbonate compensation all playing a role in modulating the concentration of atmospheric GHGs.

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Atmospheric GHG concentrations were much less variable during the pre-industrial Holocene (from 11.7 ka to 1750 CE). Atmospheric CH4 concentrations decreased at the beginning of the Holocene, consistent with a general weakening of boreal sources (Yang et al., 2017; Beck et al., 2018) and further decline during the mid-Holocene owing to a reduction in Southern Hemisphere emissions concomitant with a southward shift of the ITCZ (Singarayer et al., 2011; Beck et al., 2018). Atmospheric CH4 concentrations increased about 5 ka, which prompted the hypothesis of an early anthropogenic influence related to land-use changes in South East Asia (Ruddiman et al., 2016). However, stable isotope compositions on CH4 extracted from Greenland and Antarctic ice (Beck et al., 2018) reveal that natural emissions located in the southern tropics were responsible for the rise in atmospheric CH4 concentrations, in line with model simulations (Singarayer et al., 2011) thus disputing the early anthropogenic influence on the global CH4 budget. Atmospheric N2O concentrations increased slightly (20 ppb) across the Holocene, associated with a gradual decline in its nitrogen stable isotope composition (H. Fischer et al., 2019). The combined signal is consistent with a small increase in terrestrial emissions, offset by a reduction in marine emissions (Schilt et al., 2010b; Fischer et al., 2019).

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The early Holocene decrease in CO2 concentration by about 5 ppm (Schmitt et al., 2012) has been attributed to post-glacial regrowth in terrestrial biomass and a gradual increase in peat reservoirs over the Holocene, resulting in the sequestration of several hundred PgC (Yu et al., 2010; Nichols and Peteet, 2019). Peat accumulation rates in boreal and temperate regions were higher under warmer summer conditions in the early to mid-Holocene (Loisel et al., 2014; Stocker et al., 2017). The 20 ppm gradual increase of atmospheric CO2 starting 7 ka has been attributed to a decrease in natural terrestrial biomass due to climate change, carbonate compensation and enhanced shallow water carbonate deposition (Menviel and Joos, 2012; Brovkin et al., 2016), consistent with stable carbon isotope measurements on CO2 extracted from Antarctic ice (Elsig et al., 2009; Schmitt et al., 2012). These isotopic measurements do not support an early anthropogenic influence on atmospheric CO2 due to land-use change and forest clearing (Ruddiman et al., 2016). Recent paleoceanographic evidence suggests that remineralized carbon outgassing associated with increased Southern Ocean circulation and upwelling (Studer et al., 2018), possibly promoted by stronger Southern Hemisphere westerly winds (Saunders et al., 2018), could have additionally contributed to the late Holocene increase in atmospheric CO2 concentrations. However, the role of these mechanisms remained insignificant in transient Holocene ESM simulations (Brovkin et al., 2019). Overall, as in AR5 (WGI, Chapter 5), there is medium confidence in the key drivers of the CO2 increase between the early Holocene and the beginning of the industrial era, yet there is low confidence in the relative contributions of these drivers due to insufficient quantitative constraints on particular processes.

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This section assesses the trends and variability in atmospheric accumulation of the three main greenhouse gases (GHGs) – CO2, CH4 and N2O – their ocean and terrestrial sources and sinks as well as their budgets during the Industrial Era (1750–2019). Emphasis is placed on the more recent contemporary period (1959–2019) where understanding is increasingly better constrained by atmospheric, ocean and land observations. The section also assesses our increased understanding of the anthropogenic forcing and processes driving the trends, as well as how variability at the seasonal to decadal scales provide insights on the mechanism governing long-term trends and emerging biogeochemical–climate feedbacks with their regional characteristics.

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There are two anthropogenic sources of carbon dioxide (CO2): fossil emissions and net emissions (including removals) resulting from land-use change and land management (also shown in this chapter as LULUCF: land use, land-use change, and forestry; in previous IPCC reports it has been termed forestry and other land use, FOLU). Fossil CO2 emissions include the combustion of the fossil fuels coal, oil and gas, covering all sectors of the economy (electricity, transport, industrial, and buildings), fossil carbonates such as in cement manufacturing, and other industrial processes such as the production of chemicals and fertilizers (Figure 5.5a). Fossil CO2 emissions are estimated by combining economic activity data and emissions factors, with different levels of methodological complexity (tiers) or approaches (e.g., IPCC Guidelines for National Greenhouse Gas Inventories). Several organizations or groups provide estimates of fossil CO2 emissions, with each dataset having slightly different system boundaries, methods, activity data, and emissions factors (Andrew, 2020). Datasets cover different time periods, which can dictate the datasets and methods that are used for a particular application. The data reported here is from an annually updated data source that combines multiple sources to maximise temporal coverage (Friedlingstein et al., 2020). The uncertainty in global fossil CO2 emissions is estimated to be ±5% (1 standard deviation).

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Fossil CO2 emissions have grown continuously since the beginning of the industrial era (Figure 5.5) with short intermissions due to global economic crises or social instability (Peters et al., 2012; Friedlingstein et al., 2020). In the most recent decade (2010–2019), fossil CO2 emissions reached an average 9.6 ± 0.5 PgC yr–1 and were responsible for 86% of all anthropogenic CO2 emissions. In 2019, fossil CO2 emissions were estimated to be 9.9 ±0.5 PgC yr–1excluding carbonation (Friedlingstein et al., 2020), the highest on record. These estimates exclude the cement carbonation sink of around 0.2 PgC yr–1. Fossil CO2 emissions grew at 0.9% yr–1 in the 1990s, increasing to 3.0% yr–1 in the 2000s, and reduced to 1.2% from 2010 to 2019. The slower growth in fossil CO2 emissions in the last decade is due to a slowdown in growth from coal use. CO2 emissions from coal use grew at 4.8% yr–1 in the 2000s, but slowed to 0.4% yr–1 in the 2010s. CO2 emissions from oil use grew steadily at 1.1% yr–1 in both the 2000s and 2010s. CO2 emissions from gas use grew at 2.5% yr–1 in the 2000s and 2.4% yr–1 in 2010s, but has shown signs of accelerated growth of 3% yr–1 since 2015 (Peters et al., 2020). Direct CO2 emissions from carbonates in cement production are around 4% of total fossil CO2 emissions, and grew at 5.8% yr–1 in the 2000s but a slower 2.4% yr–1 in the 2010s. The uptake of CO2 in cement infrastructure (carbonation) offsets about one half of the carbonate emissions from current cement production (Friedlingstein et al., 2020). These results are robust across the different fossil CO2 emissions datasets, despite minor differences in levels and rates, as expected given the reported uncertainties (Andrew, 2020). During 2020, the COVID-19 pandemic led to a rapid, temporary decline in fossil CO2 emissions, estimated to be around 7% based on a synthesis of four estimates. (Cross-Chapter Box 6.1; Forster et al., 2020; Friedlingstein et al., 2020; Le Quéré et al., 2020; Liu et al., 2020).

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For the decade 2010–2019, average emissions were estimated at 1.6 ± 0.7 PgC yr–1 (mean ± standard deviation, 1 sigma; Friedlingstein et al., 2020). Alikely general upward trend since 1850 is reversed during the second part of the 20th century (Figure 5.5b). Trends since the 1980s have low confidence because they differ between estimates, which is related, among other things, to Houghton and Nassikas (2017) using a different land-use forcing than Hansis et al. (2015) and the DGVMs. Higher emissions estimates are expected from DGVMs run under transient environmental conditions compared to bookkeeping estimates, because the DGVM estimate includes the loss of additional sink capacity. Because the transient setup requires a reference simulation without land-use change to separate anthropogenic fluxes from natural land fluxes, LULUCF estimates by DGVMs include the sink forests that would have developed in response to environmental changes on areas that in reality have been cleared (Pongratz et al., 2014). The agricultural areas that replaced these forests have a reduced residence time of carbon, lacking woody material, and thus provide a substantially smaller additional sink over time (Gitz and Ciais, 2003). The loss of additional sink capacity is growing in particular with atmospheric CO2 and increases DGVM-based LULUCF flux estimates relative to bookkeeping estimates over time (Figure 5.5).

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More evidence on the pre-industrial LULUCF flux has emerged since AR5 in the form of new estimates of cumulative carbon losses until today, and of a better understanding of natural carbon cycle processes over the Holocene (Ciais et al., 2013). Cumulative carbon losses by land-use activities since the start of agriculture and forestry (pre-industrial and industrial era) have been estimated at 116 PgC based on global compilations of carbon stocks for soils (Sanderman et al., 2017) with about 70 PgC of this occurring prior to 1750, and for vegetation as 447 PgC (inner quartiles of 42 calculations: 375–525 PgC) (Erb et al., 2018). Emissions prior to 1750 can be estimated by subtracting the post-1750 LULUCF flux from Table 5.1 from the combined soil and vegetation losses until today; they would then amount to 328 (161–501) PgC assuming error ranges are independent. A share of 353 (310–395) PgC from prior to 1800 has indirectly been suggested as the difference between net biosphere flux and terrestrial sink estimates, which is compatible with ice-core records due to a low airborne fraction of anthropogenic emissions in pre-industrial times (Erb et al., 2018; see also Section 5.1.2.3). Low confidence is assigned to pre-industrial emissions estimates.

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Since AR5, evidence emerged that the LULUCF flux might have been underestimated as DGVMs include anthropogenic land cover change, but often ignore land management practices not associated with a change in land cover; land management is more widely captured by bookkeeping models through use of observation-based carbon densities (Ciais et al., 2013; Pongratz et al., 2018). Sensitivity studies show that practices such as wood and crop harvesting increase global net LULUCF emissions (Arneth et al., 2017) and explain about half of the cumulative loss in biomass (Erb et al., 2018).

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Over the past six decades, the fraction of anthropogenic CO2 emissions that has accumulated in the atmosphere (referred to as airborne fraction) has remained near constant at approximately 44% (Figure 5.7) (Ballantyne et al., 2012; Ciais et al., 2019; Gruber et al., 2019b; Friedlingstein et al., 2020). This suggests that the land and ocean CO2 sinks have continued to grow at a rate consistent with the growth rate of anthropogenic CO2 emissions, albeit with large interannual and sub-decadal variability dominated by the land sinks (Figure 5.7).

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Since AR5, an alternative observable diagnostic to the airborne fraction has been proposed to understand the trends in land and ocean sinks in response to its driving atmospheric CO2 concentrations (Raupach et al., 2014; Bennedsen et al., 2019). It is the sink rate that is defined as the combined ocean and land sink flux per unit of atmospheric excess of CO2 above pre-industrial levels (Raupach et al., 2014). The sink rate has declined over the past six decades, which indicates that the combined ocean and land sinks are not growing as fast as the growth in atmospheric CO2 (Raupach et al., 2014; Bennedsen et al., 2019). Possible explanations for the sink rate decline are that the land and/or ocean CO2 sinks are no longer responding linearly with CO2 concentrations or that anthropogenic emissions are slower than exponential (Figure 5.7 and Sections 5.2.1.3 and 5.2.1.4; Gloor et al., 2010; Raupach et al., 2014; Bennedsen et al., 2019). In addition, both diagnostics are influenced by major climate modes (e.g., ENSO) and volcanic eruptions that contribute to high interannual variability (Gloor et al., 2010; Frölicher et al., 2013; Raupach et al., 2014), suggesting high sensitivity to future climate change. Uncertain land-use change fluxes (Section 5.2.1.2) influence the robustness of the trends. Based on the airborne fraction (AF), it is concluded with medium confidence that both ocean and land CO2 sinks have grown consistent with the rising of anthropogenic emissions. Further research is needed to understand the drivers of changes in the CO2 sink rate.

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Since AR5 and SROCC, major advances in globally coordinated ocean CO2 observations (Surface Ocean CO2 Atlas, SOCAT; and Global Ocean Data Analysis Project, GLODAP), the harmonization of ocean and coastal-observation-based products, atmospheric and oceanic inversion models and forced global ocean biogeochemical models (GOBMs) have increased the level of confidence in the assessment of trends and variability of air–sea fluxes and storage of CO2 in the ocean during the historical period (1960–2018; see also Supplementary Materials 5.SM.1; Ciais et al., 2013; Bakker et al., 2016; Landschützer et al., 2016, 2020; Bindoff et al., 2019; DeVries et al., 2019; Gregor et al., 2019; Gruber et al., 2019a, b; Tohjima et al., 2019; Friedlingstein et al., 2020; Hauck et al., 2020; Olsen et al., 2020). A major advance since SROCC is that, for the first time, all six published observational product fluxes used in this assessment, are made more comparable using a common ocean and sea ice cover area, integration of climatological coastal fluxes scaled to increasing atmospheric CO2 and an ensemble mean of ocean fluxes calculated from three re-analysis wind products (Supplementary Materials 5.SM.2; Landschützer et al., 2014, 2020; Rödenbeck et al., 2014; Zeng et al., 2014; Denvil-Sommer et al., 2019; Gregor et al., 2019; Iida et al., 2021). From a process point of view, the ocean uptake of anthropogenic carbon is a two-step set of abiotic processes that involves the exchange of CO2, first across the air–sea boundary into the surface mixed layer, followed by its transport into the ocean interior where it is stored for decades to millennia, depending on the depth of storage (Gruber et al., 2019b). Two definitions of air–sea fluxes of CO2 are used in this assessment for both observational products and models: Socean is the global mean ocean CO2 sink and Fnet denotes the net spatially varying CO2 fluxes (Hauck et al., 2020). Adjustment of the mean global Fnet for the pre-industrial sea-to-air CO2 flux associated with land-to-ocean carbon flux term makes Fnet comparable to Socean (Jacobson et al., 2007; Resplandy et al., 2018; Hauck et al., 2020).

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There are multiple lines of observational and modelling evidence that support with high confidence the finding that, in the historical period (1960–2018), air–sea fluxes and storage of anthropogenic CO2 are largely influenced by atmospheric CO2 concentrations, physical ocean processes and physicochemical carbonate chemistry, which determines the unique properties of CO2 in seawater (Chapter 9 and Cross-Chapter Box 5.3; Wanninkhof, 2014; DeVries et al., 2017; McKinley et al., 2017, 2020, Gruber et al., 2019a, b; Hauck et al., 2020). Here we assess three different approaches (Figures 5.8a,b and 5.9) that together provide high confidence that, during the historical period (1960–2018), the ocean carbon sink (Socean) and its associated ocean carbon storage have grown in response to global anthropogenic CO2 emissions (Gruber et al., 2019a; Hauck et al., 2020; McKinley et al., 2020).

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In the first assessment approach, the mean global multi-decadal (1960–2019) trends in the ocean sink (Socean) for CO2 show a high degree of coherence across the nine GOBMs and sixpCO2 -based observational product reconstructions (1987–2018) which, despite a temporary slowdown (or ‘hiatus’) in the 1990s, is also quasi-linear over that period (Figure 5.8a; Gregor et al., 2019; Hauck et al., 2020). This coherence between the GOBMs and observations-based reconstructions (1987–2018; r2=0.85) provides high confidence that the ocean sink (Socean in Section 5.2.1.5) evaluated from GOBMs (1960–2019) grew quasi-linearly from 1.0 ± 0.3 PgC yr–1 to 2.5 ± 0.6 PgC yr–1 between the decades 1960–1969 and 2010–2019 in response to global CO2 emissions (Figure 5.8a; Table 5.1; Friedlingstein et al., 2020; Hauck et al., 2020). The cumulative ocean CO2 uptake (105 ± 20 PgC) is 23% of total anthropogenic CO2 emissions (450 ± 50 PgC) for the same period (Friedlingstein et al., 2020). Notwithstanding the high confidence in the magnitude of the annual to decadal trends for Socean, this assessment is moderated to mediumconfidence by the low confidence in the currently inadequately constrained uncertainties in the pre-industrial land-to-ocean carbon flux, the uncertain magnitude of winter outgassing from the Southern Ocean, and the uncertain effect of the ocean surface cool-skin, the effect of data sparsity, differences between wind products and the uncertain contribution from the changing land–ocean continuum on global and regional fluxes (Jacobson et al., 2007; Resplandy et al., 2018; Roobaert et al., 2018; Bushinsky et al., 2019; Hauck et al., 2020; Watson et al., 2020; Gloege et al., 2021). However, both GOBMs and pCO2 -based observational products independently reveal a slowdown or ‘hiatus’ of the ocean sink in the 1990s, which provides a valuable constraint for model verification and leads to greater confidence in the model outputs (Figure 5.8a; Landschützer et al., 2016; Gregor et al., 2018; DeVries et al., 2019; Hauck et al., 2020). A number of studies point to the role of the Southern Ocean in the global ‘1990s hiatus’ in air–sea CO2 fluxes, but provide different process-based explanations linking ocean temperature, mixing and meridional overturning circulation (MOC) responses to variability in large-scale climate systems, wind stress and volcanic activity, as well as the sensitivity of the air–sea CO2 flux to small changes in the atmospheric forcing from anthropogenic CO2 (Landschützer et al., 2016; DeVries et al., 2017; Bronselaer et al., 2018; Gregor et al., 2018; Gruber et al., 2019a; Keppler and Landschützer, 2019; McKinley et al., 2020; Nevison et al., 2020). Data sparsity in the Southern Ocean could also be a factor amplifying the global decadal perturbation of the 1990s (Gloege et al., 2021). Therefore, while there is high confidence in the 1990s hiatus of the global ocean sink for anthropogenic CO2, and that the Southern Ocean makes an observable contribution to it, there is still low confidence in the attribution for the processes behind the 1990s hiatus (Section 5.2.1.3.2). Observed increases in the amplitude of the seasonal cycle of oceanpCO2 and reductions in the mean global buffering capacity provide high confidence that the growing CO2 sink is also beginning to drive observable large-scale changes in ocean carbonate chemistry (Jiang et al., 2019). However, there is medium confidence that these changes which, depending on the emissions scenario, could drive future ocean feedbacks, are still too small to emerge from the historical multi-decadal observed growth rate of Socean (Sections 5.1.2; 5.3.2 and 5.4.2, and Figure 5.8a; SROCC (Section 5.2.2.3.2; Bates et al., 2014; Sutton et al., 2016; Fassbender et al., 2017; Landschützer et al., 2018; Jiang et al., 2019). A recent model-based study suggests that re-emergence of previously stored anthropogenic CO2 is changing the buffering capacity of the mixed layer and reducing the ocean sink for anthropogenic CO2 during the historical period (Rodgers et al., 2020). This trend is not reflected in observations-based products (Figure 5.8a), so we attribute a low confidence.

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Here we provide a third comparative assessment approach depicting the spatial coherence of ocean air–sea fluxes and storage rates of CO2 as well as a quantitative assessment of both fluxes for the same period (1994–2007; Figure 5.9). Observation-based pCO2 flux products show that emissions of natural CO2 occur mostly in the tropics and high-latitude Southern Ocean, and that the uptake and storage of anthropogenic CO2 occurs predominantly in the mid-latitudes (Chapter 9, Figure 5.9 and Cross-Chapter Box 5.3). Strong ocean CO2 sink regions are those in the mid-latitudes associated with the cooling of poleward flowing subtropical surface waters as well as equatorward flowing sub-polar surface waters, both of which contribute to the formation of Mode, Intermediate and Deep water masses that transport anthropogenic CO2 into the ocean interior on time scales of decades to centuries in both hemispheres (Section 9.2.2.3 and Figure 5.9; DeVries, 2014; Gruber et al., 2019b; Wu et al., 2019). The mean decadal scale magnitude and uncertainties of Socean from net air sea fluxes (Fnet ) were calculated from an ensemble of six observational-based product reconstructions (Figure 5.9a) and the storage rates in the ocean interior derived from multiple ocean interior CO2 datasets (Gruber et al., 2019b; Figure 5.9b). The cumulative CO2 stored in the ocean interior from 1800 to 2007 has been estimated at 140 ±18 PgC (Gruber et al., 2019b). As reported in SROCC (Section 5.2.2.3.1; IPCC, 2019b), the net ocean CO2 storage between 1994–2007 was 29 ± 4 PgC, which corresponds to a mean storage of 26 ± 5% of anthropogenic CO2 emissions for that period (Gruber et al., 2019b). The resulting net annual storage rate of anthropogenic CO2, equivalent to Socean for the period mid-1994 to mid-2007 is 2.2 ± 0.3 PgC yr–1, which is in very close agreement with the top-down air–sea flux estimate of Socean of 2.1 ± 0.5 PgC yr–1 from GOBMs and 1.9 ± 0.3PgC yr–1 frompCO2 -based observational products with the steady river carbon flux correction of 0.62 PgC yr–1 for the same time period (Gruber et al., 2019b; Hauck et al., 2020). This close agreement between these independent ocean CO2 sink estimates derived from air–sea fluxes and storage rates in the ocean interior support the medium confidence assessment that the ocean anthropogenic carbon storage rates continue to be determined by the ocean sink (Socean) in response to growing CO2 emissions (Figure 5.9; McKinley et al., 2020).

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Regional decadal-scale anomalies in the variability of ocean CO2 storage have also emerged, probably associated with changes in the MOC, which may influence the global variability in Fnet (Chapter 9; DeVries et al., 2017). In the interior of the Indian and Pacific sectors of the Southern Ocean, and the North Atlantic, the increase in the CO2 inventory from 1994 to 2007 was about 20% smaller than expected from the atmospheric CO2 increase during the same period and the anthropogenic CO2 inventory in 1994 (Sabine eta al., 2004; Gruber et al., 2019a). There is medium confidence that the ocean CO2 inventory strengthened again in the decade 2005–2015 (DeVries et al., 2017). In the North Atlantic, a low rate of anthropogenic CO2 storage at 1.9 ± 0.4 PgC per decade during the time period of 1989–2003 increased to 4.4 ± 0.9 PgC per decade during 2003–2014. This is associated with changing ventilation patterns driven by the North Atlantic Oscillation (Woosley et al., 2016). In the Pacific sector of the Southern Ocean, the rate of anthropogenic CO2 storage also increased from 8.8 ± 1.1 (1σ) PgC per decade during 1995–2005 to 11.7 ± 1.1 PgC per decade during 2005–2015 (Carter et al., 2019). However, in the Subantarctic Mode Water of the Atlantic sector of the Southern Ocean, the storage rate of the anthropogenic CO2 was rather lower after 2005 than before (Section 9.2.3.2; Tanhua et al., 2017; Bindoff et al., 2019). These changes have been predominantly ascribed to the impact of changes in the MOC on the transport of anthropogenic CO2 into the ocean interior due to regional climate variability, in addition to the increase in the atmospheric CO2 concentration (Section 9.2.3.1; Wanninkhof et al., 2010; Pérez et al., 2013; DeVries et al., 2017, 2019; Gruber et al., 2019b; McKinley et al., 2020). However,the low frequency of carbon observations in the interior of the vast ocean leads to medium confidence in the assessment of temporal variability in the rate of regional ocean CO2 storage and its controlling mechanisms.

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In summary, multiple lines of observational and modelling evidence provide high confidence in the finding that the ocean sink for anthropogenic CO2 has increased quasi-linearly over the past 60 years in response to growing global emissions of anthropogenic CO2, with a mean fraction of 23% of total emissions. The high confidence assessment is moderated to medium confidence due to a number of ocean CO2 flux terms yet to be adequately constrained. Observed changes in the variability of oceanpCO2 and observed reductions in the mean global buffering capacity provide high confidence that the growing CO2 sink is also beginning to drive observable large-scale changes in ocean carbonate chemistry. However, there is medium confidence that these changes which, depending on the emissions scenario, could drive future ocean feedbacks, are still too small to emerge from the historical multi-decadal observed growth rate of Socean.

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The global CO2 budget (Figure 5.12) encompasses all natural and anthropogenic CO2 sources and sinks. Table 5.1 shows the perturbation of the global carbon mass balance between reservoirs since the beginning of the industrial era, circa 1750.

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Over the past decade (2010–2019), 10.9 ± 0.9 PgC yr–1 were emitted from human activities, which were distributed between three Earth system components: 46% accumulated in the atmosphere (5.1 ± 0.02 PgC yr–1), 23% was taken up by the ocean (2.5 ± 0.6 PgC yr–1) and 31% was stored by vegetation in terrestrial ecosystems (3.4 ± 0.9 PgC yr–1) (Table 5.1). There is a budget imbalance of 0.1 PgCyr–1 which is within the uncertainties of the other terms. Over the industrial era (1750–2019), the total cumulative CO2 fossil fuel and industry emissions were 445 ± 20 PgC, and the LULUCF flux (= net land-use change in Figure 5.12) was 240 ± 70 PgC (medium confidence). The equivalent total emissions (685 ± 75 PgC) was distributed between the atmosphere (285 ± 5 PgC), oceans (170 ± 20 PgC) and land (230 ± 60 PgC; Table 5.1), with a budget imbalance of 20 PgC. This budget (Table 5.1) does not explicitly account for source/sink dynamics due to carbon cycling in the land–ocean aquatic continuum comprising freshwaters, estuaries, and coastal areas. Natural and anthropogenic transfers of carbon from soils to freshwater systems are significant (2.4–5.1 PgC yr–1) (Regnier et al., 2013; Drake et al., 2018). Some of the carbon is buried in freshwater bodies (0.15 PgC) (Mendonça et al., 2017), and a significant proportion returns to the atmosphere via outgassing from lakes, rivers and estuaries (Raymond et al., 2013; Regnier et al., 2013; Lauerwald et al., 2015). The net export of carbon from the terrestrial domain to the open oceans is estimated to be 0.80 PgC yr–1 (medium confidence), based on the average of (Jacobson et al., 2007; Resplandy et al., 2018) and corrected to account for 0.2 PgC buried in ocean floor sediments. These terms are included in Figure 5.12. Inclusion of other smaller fluxes could further constrain the carbon budget (Ito, 2019; Friedlingstein et al., 2020).

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Methane is a much more powerful greenhouse gas than CO2 (Chapter 7) and participates in tropospheric chemistry (Chapter 6). The CH4 variability in the atmosphere is mainly the result of the net balance between the sources and sinks on the Earth’s surface and chemical losses in the atmosphere. Atmospheric transport evens out the regional CH4 differences between different parts of the Earth’s atmosphere. The steady-state lifetime is estimated to be 9.1 ± 0.9 years (Section 6.3.1 and Table 6.2). About 90% of the loss of atmospheric CH4 occurs in the troposphere by reaction with hydroxyl radical (OH), 5% by bacterial soil oxidation, and the rest 5% by chemical reactions with OH, excited state oxygen (O1D), and atomic chlorine (Cl) in the stratosphere (Saunois et al., 2020). Methane has large emissions from natural and anthropogenic origins, but a clear demarcation of their nature is difficult because of the use and conversions of the natural ecosystem for human activities. The largest natural sources are from wetlands, freshwater and geological process, while the largest anthropogenic emissions are from enteric fermentation and manure treatment, landfills and waste treatment, rice cultivation and fossil fuel exploitation (Table 5.2). In the past two centuries, CH4 emissions have nearly doubled, predominantly human driven since 1900, and persistently exceeded the losses (virtually certain), thereby increasing the atmospheric abundance as evidenced from the ice core and firn air measurements (Ferretti et al., 2005; Ghosh et al., 2015).

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The positive gradient between CH4 at Cape Grim, Australia (41°S) and Trinidad Head, USA (41°N), and the bigger difference between Trinidad Head and global mean CH4 compared to that between global mean CH4 and Cape Grim, strongly suggest that the Northern Hemisphere is the dominant origin of anthropogenic CH4 emissions (Figure 5.13). The loss rate of CH4 in troposphere does not produce a large positive north–south hemispheric gradient in CH4 due to parity in hemispheric mean OH concentration (Patra et al., 2014), or in the case of greater OH concentrations in the northern rather than the Southern Hemisphere as simulated by the chemistry-climate models (Naik et al., 2013). Coal mining contributed about 35% of the total CH4 emissions from all fossil fuel-related sources. Top-down estimates of fossil fuel emissions (106 Tg yr–1) are smaller than bottom-up estimates (115 Tg yr–1) during 2008–2017 (Table 5.2). Inventory-based estimates suggest that CH4 emissions from coal mining increased by 17 Tg yr–1 between the periods 2002–2006 and 2008–2012, with a dominant contribution from China (Peng et al., 2016; Crippa et al., 2020; Höglund-Isaksson et al., 2020). Inventory-based estimates suggest that CH4 emissions from coal mining increased by 17 Tg yr–1 between the periods 2002–2006 and 2008–2012, with a dominant contribution from China (Peng et al., 2016; Crippa et al., 2020; Höglund-Isaksson et al., 2020). Recent country statistics and detailed inventory-based estimates show that CH4 emissions from coal mining in China declined between 2012 and 2016 (Sheng et al., 2019; Gao et al., 2020), while atmospheric-based estimates suggest a continuation of CH4 emissions growth but at a slower rate to the year 2015 (Miller et al., 2019) and 2016 (Chandra et al., 2021). Emissions from oil and gas extraction and use decreased in the 1980s and 1990s, but increased in the 2000s and 2010s (Dlugokencky et al., 1994; Stern and Kaufmann, 1996; Howarth, 2019; Crippa et al., 2020). The attribution to multiple CH4 sources using spatially aggregated atmospheric d13C data remained underdetermined to infer the global total emissions from the fossil fuel industry, biomass burning and agriculture (Rice et al., 2016; Schaefer et al., 2016; Schwietzke et al., 2016; Worden et al., 2017; Thompson et al., 2018).

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Biomass burning and biofuel consumption (including natural and anthropogenic processes) caused at least 30 Tg yr–1emissions during 2008–2017 and constituted up to about 5% of global anthropogenic CH4 emissions. Methane emissions from open biomass burning decreased during the past two decades mainly due to reduction of burning in savanna, grassland and shrubland (van der Werf et al., 2017; Worden et al., 2017). There is recent evidence from the tropics that fire occurrence is non-linearly related to precipitation, implying that severe droughts will increase CH4 emissions from fires, particularly from the degraded peatlands (Field et al., 2016).

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A few studies emphasize the role of chemical destruction by hydroxyl (OH; the primary sink of methane), in driving changes in the growth of atmospheric methane abundance, in particular after 2006 (Rigby et al., 2017; Turner et al., 2017). Studies applying three-dimensional atmospheric inversion (McNorton et al., 2018), simple multi-species inversion (Thompson et al., 2018), as well as empirical methods using a variety of observational constraints based on OH chemistry (Nicely et al., 2018; Patra et al., 2021), do not find trends in OH large enough to explain the methane changes post-2006. On the contrary, global chemistry–climate models based on fundamental principles of atmospheric chemistry and known emissions trends of anthropogenic non-methane short-lived climate forcers simulate an increase in OH over this period (Zhao et al., 2019; Stevenson et al., 2020; see Section 6.2.3). These contrasting lines of evidence suggest that OH changes may have had a small moderating influence on methane growth since 2007 (low confidence).

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Cross-Chapter Box 5.2 Figure 2 shows that modelled wetland emissions anomalies for all regions did not exhibit statistically significant trends (high agreement between models, medium evidence). Thus, the inter-decadal difference of total CH4 emissions derived from inversion models and wetland emissions, arises mainly from anthropogenic activities. The time series of regional emissions suggest that progress towards atmospheric CH4 quasi-equilibrium was primarily driven by reductions in anthropogenic (fossil fuel exploitation) emissions in Europe, Russia and temperate North America over 1988–2000. In the global totals, emissions equalled loss in the early 2000s. The growth since 2007 is driven by increasing agricultural emissions from East Asia (1997–2017), West Asia (2005–2017), Brazil (1988–2017) and Northern Africa (2005–2017), and fossil fuel exploitations in temperate North America (2010–2017; Lan et al., 2019; Crippa et al., 2020; Höglund-Isaksson et al., 2020; Jackson et al., 2020; Chandra et al., 2021).

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There is evidence from emissions inventories at country level and regional scale inverse modelling that CH4 growth rate variability between 1988 and 2017 is closely linked to anthropogenic activities (medium agreement). Isotopic composition observations and inventory data suggest that concurrent emissions changes from both fossil fuels and agriculture are playing roles in the resumed CH4 growth since 2007 (high confidence). Shorter-term decadal variability is predominantly driven by the influence of El Niño–Southern Oscillation on emissions from wetlands and biomass burning (Cross-Chapter Box 5.2, Figure 2), and loss due to OH variations (medium confidence), but lacking quantitative contribution from each of the sectors. By synthesizing all available information regionally from a priori (bottom-up) emissions, satellite and surface observations, including isotopic information, and inverse modelling (top-down), the capacity to track and explain changes in, and drivers of, natural and anthropogenic CH4 regional and global emissions has improved since AR5, but fundamental uncertainties related to OH variations remain unchanged.

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The human perturbation of the natural nitrogen cycle through the use of synthetic fertilizers and manure, as well as nitrogen deposition resulting from land-based agriculture and fossil fuel burning has been the largest driver of the increase in atmospheric N2O of 31.0 ± 0.5 ppb (10%) between 1980 and 2019 (robust evidence, high agreement) (Tian et al., 2020). The long atmospheric lifetime of N2O implies that it will take more than a century before atmospheric abundances stabilize after the stabilization of global emissions. The rise of atmospheric N2O is of concern, not only because of its contribution to the anthropogenic radiative forcing (Chapter 7) but also because of the importance of N2O in stratospheric ozone loss (Ravishankara et al., 2009; Fleming et al., 2011; W. Wang et al., 2014).

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The AR5 (WGI, Section 6.4.3) and SRCCL (Section 2.3.3) concluded that agriculture is the largest anthropogenic source of N2O emissions. Since SRCCL (2.3.3), a new synthesis of inventory-based and modelling studies shows that the widespread use of synthetic fertilizers and manure on cropland and pasture, manure management and aquaculture resulted in 3.8 (2.5–5.8) TgN yr–1 (average 2007–2016) (robust evidence, high agreement) (Table 5.3; Winiwarter et al., 2018; FAO, 2019; Janssens-Maenhout et al., 2019; Tian et al., 2020). Observations from field-measurements (Song et al., 2018), inventories (Wang et al., 2020) and atmospheric inversions (Thompson et al., 2019) further corroborate the assessment of SRCCL that there is a non-linear relationship between N2O emissions and nitrogen input, implying an increasing fraction of fertilizer lost as N2O with larger fertilizer excess (medium evidence, high agreement). Several studies using complementary methods indicate that agricultural N2O emissions have increased by more than 45% since the 1980s (high confidence) (Figure 5.16 and Table 5.3; Davidson, 2009; Winiwarter et al., 2018; Janssens-Maenhout et al., 2019; Tian et al., 2020), mainly due to the increased use of nitrogen fertilizer and manure. N2O emissions from aquaculture are among the fastest rising contributors of N2O emissions, but their overall magnitude is still small in the overall N2O budget (Tian et al., 2020).

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The principal non-agricultural anthropogenic sources of N2O are industry, specifically chemical processing, wastewater, and the combustion of fossil fuels (Table 5.3). Industrial emissions of N2O mainly due to nitric and adipic acid production have decreased in North America and Europe since the widespread installation of abatement technologies in the 1990s (Pérez-Ramrez et al., 2003; Lee et al., 2011; Janssens-Maenhout et al., 2019). There is still considerable uncertainty in industrial emissions from other regions of the world with contrasting trends between inventories (Thompson et al., 2019). Globally, industrial emissions and emissions from fossil fuel combustion by stationary sources, such as power plants, as well as smaller emissions from mobile sources (e.g., road transport and aviation) have remained nearly constant between the 1980s and 2007–2016 (medium evidence, medium agreement) (Winiwarter et al., 2018; Janssens-Maenhout et al., 2019; Tian et al., 2020). Wastewater N2O emissions, including those from domestic and industrial sources, have increased from 0.2 (0.1–0.3) TgN yr–1 to 0.35 (0.2–0.5) TgN yr–1 between the 1980s and 2007–2016 (Tian et al., 2020).

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Biomass burning from crop residue burning, grassland, savannah and forest fires, as well as biomass burnt in household stoves, releases N2O during the combustion of organic matter. Updated inventories since AR5 (WGI, Section 6.4.3) result in a lower range of the decadal mean emissions of 0.6 (0.5–0.8) TgN yr–1 (van der Werf et al., 2017; Tian et al., 2020). The attribution of grassland, savannah or forest fires to natural or anthropogenic origins is uncertain, preventing a separation of the biomass burning source into natural and anthropogenic.

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Atmospheric deposition of anthropogenic N on oceans can stimulate marine productivity and influence ocean emissions of N2O. New ocean model analyses since AR5 (WGI, 6.4.3), suggest a relatively modest global potential impact of 0.01–0.32 TgN yr–1 (pre-industrial to present-day) equivalent to 0.5–3.3% of the global ocean N2O source (Suntharalingam et al., 2012; Jickells et al., 2017; Landolfi et al., 2017). However, larger proportionate impacts are predicted in nitrogen-limited coastal and inland waters downwind of continental pollution outflow, such as the Northern Indian Ocean (Jickells et al., 2017; Suntharalingam et al., 2019).

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Inland waters and estuaries are generally sources of N2O as a result of nitrification and denitrification of dissolved inorganic nitrogen, however, they can serve as N2O sinks in specific conditions (Webb et al., 2019). Since AR5 (WGI, 6.4.3), improved emissions factors, including their spatio-temporal scaling, and consideration of transport within the aquatic system allows for better constraint of these emissions (Murray et al., 2015; Hu et al., 2016; Lauerwald et al., 2019; Maavara et al., 2019; Kortelainen et al., 2020; Yao et al., 2020). Despite uncertainties because of the side effects of canals and reservoirs on nutrient cycling, these advances permit attribution of a fraction of inland water N2O emissions to anthropogenic sources (Tian et al., 2020), which contributes to the increased anthropogenic share of the global N2O source in this report compared to AR5 (Ciais et al., 2013). As an indirect consequence of agricultural nitrogen use and waste-water treatment, the anthropogenic emissions from inland waters have increased by about a quarter (0.1 TgN yr–1) between the 1980s and 2007–2016 (Tian et al., 2020).

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The synthesis of bottom-up estimates of N2O sources (Sections 5.2.3.2–5.2.3.4 and Figure 5.17) yields a global source of 17.0 (12.2 to 23.5) TgN yr–1 for the years 2007–2016 (Table 5.3). This estimate is comparable to AR5, but the uncertainty range has been reduced primarily due to improved estimates of ocean and anthropogenic N2O sources. Since AR5 (WGI, Section 6.4.3), improved capacity to estimate N2O sources from atmospheric N2O measurements by inverting models of atmospheric transport provides a new and independent constraint for the global N2O budget (Saikawa et al., 2014; Thompson et al., 2019; Tian et al., 2020). The decadal mean source derived from these inversions is remarkably consistent with the bottom-up global N2O budget for the same period, however, the split between land and ocean sources based on atmospheric inversions is less constrained, yielding a smaller land source of 11.3 (10.2 to 13.2) TgN yr–1 and a larger ocean source of 5.7 (3.4 to 7.2) TgN yr–1, respectively, compared to bottom-up estimates.

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Supported by multiple studies and extensive observational evidence (Sections 5.2.3.2–5.2.3.4 and Figure 5.17), anthropogenic emissions contributed about 40% (7.3; uncertainty range: 4.2 to 11.4 TgN yr–1) to the total N2O source in 2007–2016 (high confidence). This estimate is larger than in AR5 (WGI, 6.4.3) due to a larger estimated effect of nitrogen deposition on soil N2O emissions and the explicit consideration of the role of anthropogenic nitrogen in determining inland water and estuary emissions.

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Based on bottom-up estimates, anthropogenic emissions from agricultural nitrogen use, industry and other indirect effects have increased by 1.7 (1.0 to 2.7) TgN yr–1 between the decades 1980–1989 and 2007–2016, and are the primary cause of the increase in the total N2O source (high confidence). Atmospheric inversions indicate that changes in surface emissions, rather than in the atmospheric transport or sink of N2O, are the cause for the increased atmospheric growth rate of N2O (robust evidence, high agreement) (Thompson et al., 2019). However, the increase of 1.6 (1.4 to 1.7) TgN yr–1 in global emissions between 2000–2005 and 2010–2015 based on atmospheric inversions is somewhat larger than bottom-up estimates over the same period, primarily because of differences in the estimates of land-based emissions.

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The total influence of anthropogenic greenhouse gases (GHGs) on the Earth’s radiative balance is driven by the combined effect of those gases, and the three most important – carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) – were discussed in the previous sections. This section compares the balance of the sources and sinks of these three gases and their regional net flux contributions to the radiative forcing. CO2 has multiple residence times in the atmosphere – from one year to many thousands of years (Box 6.1 in Ciais et al., 2013) – and N2O has a mean lifetime of 116 years. They are both long-lived GHGs, while CH4 has a lifetime of 9.1 years and is considered a short-lived GHG (see Chapter 2 for lifetime of GHGs, Chapter 6 for CH4 chemical lifetime, and Chapter 7 for effective radiative forcing of all GHGs).

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The surface ocean has absorbed a quarter of all anthropogenic CO2 emissions, mainly through physical–chemical processes (McKinley et al., 2016; Gruber et al., 2019b; Friedlingstein et al., 2020). Once dissolved in seawater, CO2 reacts with water and forms carbonic acid, which in turn dissociates, leading to a decrease in the concentration of carbonate (CO3–2) ions, and increasing both bicarbonate (HCO3) and hydrogen (H+) ion concentration. This process has caused a shift in the carbonate chemistry towards a less basic state, commonly referred to as ‘ocean acidification’ (Caldeira and Wickett, 2003; Orr et al., 2005; Doney et al., 2009). Although the societal concern regarding ocean acidification is relatively recent (about the last 20 years), the physical–chemical basis for the ocean absorption (sink) of atmospheric CO2 has been discussed much earlier by Revelle and Suess (1957). The AR5 and SROCC assessments were of robust evidence that the H+ion concentration is increasing in the surface ocean, thereby reducing seawater pH (= -log [H+]) (Section 2.3.4.1; Orr et al., 2005; Feely et al., 2009; Ciais et al., 2013; Bindoff et al., 2019), and there is high confidence that ocean acidification is impacting marine organisms (Bindoff et al., 2019).

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Ocean oxygen decline, or deoxygenation, is driven by changes in ocean ventilation and solubility (Bindoff et al., 2019). It is virtually certain that anthropogenic forcing has made a substantial contribution to the ocean heat content increase over the historical period (Bindoff et al., 2019; IPCC, 2019c; Chapter 9, Section 2.3.3.1), strengthening upper water column stratification. Ocean warming decreases the solubility of dissolved oxygen in seawater, and it contributes to about 15% of the dissolved oxygen decrease in the oceans according to estimates based on solubility and the recent SROCC assessment (medium confidence), especially in sub-surface waters, between 100–600 m depth (Helm et al., 2011; Schmidtko et al., 2017; Breitburg et al., 2018; Oschlies et al., 2018; SROCC, Section 5.3.1). Stratification reduces the ventilation flux into the ocean interior, contributing to most of the remaining ocean deoxygenation (Schmidtko et al., 2017; Breitburg et al., 2018; Section 3.6.2). Deoxygenation may enhance emissions of nitrous oxide, especially from oxygen minimum zones (OMZs) or hypoxic coastal areas (Breitburg et al., 2018; Oschlies et al., 2018). Since SROCC (Bindoff et al., 2019), CMIP6 model simulation results agree with the reported 2% loss (4.8 ± 2.1 Pmoles O2) in total dissolved oxygen in the upper ocean layer (100–600 m) for the 1970–2010 period (Helm et al., 2011; Ito et al., 2017; Schmidtko et al., 2017; Kwiatkowski et al., 2020; Section 2.3.4.2). The response of marine organisms to the coupled effects of ocean warming, acidification and deoxygenation occur at different metabolic levels on different groups, and include respiratory stress and reduction of thermal tolerance (Gruber, 2011; Bindoff et al., 2019; IPCC, 2019c; Kawahata et al., 2019). An assessment of these effects on marine biota is found in WGII AR6 Chapter 2.

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Since the beginning of the industrial period in the mid-19th century, coral δ11B-derived ocean pH has decreased by 0.06–0.24 pH unit in the South China Sea (Liu et al., 2014; Wei et al., 2015) and 0.12 pH unit in the south-west Pacific (H.C. Wu et al., 2018). Since the mid-20th century, a distinct feature of coral δ11B records relates to ocean acidification trends, albeit having a wide range of values: 0.12–0.40 pH unit in the Great Barrier Reef (Wei et al., 2009; D’Olivo et al., 2015), 0.05–0.08 pH unit in the north-west Pacific (Shinjo et al., 2013) and 0.04–0.09 pH unit in the Atlantic Ocean (Goodkin et al., 2015; Fowell et al., 2018). Concurrent coral carbon isotopic ( δ13C) measurements infer ocean uptake of anthropogenic CO2 from the combustion of fossil fuel, based on the lower abundance of13C in fossil fuel carbon. Western Pacific coral records show depleted δ13C trends since the late 19th century that are more prominent since the mid-20th century (high confidence) (Pelejeroet al., 2005; Wei et al., 2009; Shinjo et al., 2013; Liu et al., 2014; Kubota et al., 2017; H.C. Wu et al., 2018).

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Overall, many of the records show a highly variable seawater pH underlaying strong imprints of internal climate variability (high confidence) and, in most instances, superimposed on a decreasing δ11B trend that is indicative of anthropogenic ocean acidification in recent decades (medium confidence). The robustness of seawater pH reconstructions is currently limited by the uncertainty on the calibration of The δ11B proxy in different tropical coral species.

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In the subtropical open oceans, decreases in pH have been reported with avery likely rate range from –0.016 to –0.019 pH units per decade since 1980s, which equates to approximately 4 % increase in hydrogen ion concentration ([H+]) per decade. Accordingly, the saturation state Ω (=[Ca2+][CO32-]/Ksp) of seawater with respect to calcium carbonate mineral aragonite has been declining at rates ranging from –0.07 to –0.12 per decade (González-Dávila et al., 2010; Feely et al., 2012; Bates et al., 2014; Takahashi et al., 2014; Ono et al., 2019; Bates and Johnson, 2020; Supplementary Material Table 5.SM.3). These rates are consistent with the rates expected from the transient equilibration with increasing atmospheric CO2 concentrations, but the variability of rate in decadal time scale has also been detected with robust evidence (Ono et al., 2019; Bates and Johnson, 2020). In the tropical Pacific, its central and eastern upwelling zones exhibited a faster pH decline of –0.022 to –0.026 pH unit per decade due to increased upwelling of CO2 -rich sub-surface waters in addition to anthropogenic CO2 uptake (Sutton et al., 2014; Lauvset et al., 2015). By contrast, warm pools in the western tropical Pacific exhibited slower pH decline of –0.010 to –0.013 pH unit per decade (Supplementary Material Table 5.SM.3; Lauvsetet al., 2015; Ishii et al., 2020). Observational and modelling studies (Nakano et al., 2015; Ishii et al., 2020) consistently suggest that slower acidification in this region is attributable to the anthropogenic CO2 taken up in the extratropics around a decade ago and transported to the tropics via shallow meridional overturning circulations.

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Advances in observations and modelling for ocean physics and biogeochemistry and established knowledge of ocean carbonate chemistry show with very high confidence that anthropogenic CO2 taken up into the ocean surface layer is further spreading into the ocean interior through ventilation processes, including vertical mixing, diffusion, subduction and meridional overturning circulations (Sections 2.3.3.5, 5.2.1.3 and 9.2.2.3; Sallée et al., 2012; Bopp et al., 2015; Nakano et al., 2015; Iudicone et al., 2016; Toyama et al., 2017; Pérez et al., 2018; Gruber et al., 2019b) and is causing acidification in the ocean interior. The net change in oxygen consumption by aerobic respiration of marine organisms further influences acidification by releasing CO2 (Section 5.3.3.2; Chen et al., 2017; Breitburg et al., 2018; Robinson, 2019).

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Observations over past decades of basin-wide and global syntheses of ocean interior carbon show that the extent of acidification due to anthropogenic CO2 invasion tends to diminish with depth (very high confidence) (Section 5.2.1.3.3 and Figure 5.21; Woosley et al., 2016; Carter et al., 2017; Lauvset et al., 2020). The regions of deep convection such as subpolar North Atlantic and Southern Ocean present the deepest acidification detections below 2000 m (medium confidence). Mid-latitudinal zones within the subtropical cells and tropical regions present a relatively deep and shallow detection, respectively. A pH decrease has also been observed on the Antarctic continental shelf (Hauck et al., 2010; Williams et al., 2015). Acidification is also underway in the subsurface to intermediate layers of the Arctic Ocean due to the inflow of ventilated waters from the North Atlantic and the North Pacific (Qi et al., 2017; Ulfsbo et al., 2018).

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A significant increase in acidification resulting from net metabolic CO2 release coupled with ocean circulation changes has been shown with high confidence in large swathes of intermediate waters in the Pacific and Atlantic oceans (Dore et al., 2009; Byrne et al., 2010; Ríos et al., 2015; Chu et al., 2016; Carter et al., 2017; Lauvset et al., 2020). For example, ocean circulation contributes a pH change of –0.013 ± 0.013 to the overall observed change of –0.029 ± 0.014 for 1993–2013 at depths around 1000 m at 30°S–40°S in the South Atlantic ocean (Ríos et al., 2015). Long-term repeated observations in the North Pacific show a decline in dissolved oxygen (–4.0 μmol kg−1 per decade at maximum) being sustained in the intermediate water since the 1980s (Takatani et al., 2012; Sasano et al., 2015). The amplification of acidification associated with the weakening ventilation is thought to have been occurring persistently. In contrast, for the North Pacific subtropical mode water, large decadal variability in pH and aragonite saturation state with amplitudes of about 0.02 and about 0.1, respectively, are superimposed on secular declining trends due to anthropogenic CO2 invasion (Oka et al., 2019). This is associated with the variability in ventilation due to the approximately 50% variation in the formation volume of the mode water that is forced remotely by the Pacific Decadal Oscillation (Qiu et al., 2013; Oka et al., 2015).

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In summary, ocean acidification is spreading into the ocean interior. Its rates at depths are controlled by the ventilation of the ocean interior as well as anthropogenic CO2 uptake at the surface, thereby diminishing with depth (very high confidence) (Figure 5.21). Variability in ocean circulation modulates the trend of ocean acidification at depths through the changes in ventilation and their impacts on metabolic CO2 content. However, the large knowledge gap around ventilation changes leads to low confidence in their impacts in many ocean regions (Sections 5.3.3.2; 9.2.2.3 and 9.3.2).

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In oxygen-depleted waters, microbial processes (denitrification and anammox, i.e., anaerobic ammonium oxidation; Kuypers et al., 2005; Codispoti, 2007; Gruber and Galloway, 2008) remove fixed nitrogen, and when upwelled waters reach the photic zone, primary production becomes nitrogen-limited (Tyrrell and Lucas, 2002). However, in other oceanic regions, increased water-column stratification due to warming may reduce the amount of N2O reaching the surface and thereby decrease N2O flux to the atmosphere. Landolfi et al. (2017) suggest that, by 2100, under the RCP8.5 scenario, total N2O production in the ocean may decline by 5% and N2O emissions be reduced by 24% relative to the pre-industrial era due to decreased organic matter export and anthropogenic-driven changes in ocean circulation and atmospheric N2O concentrations. Projected oxygen loss in the ocean is thought to result in an ocean-climate feedback through changes in the natural emissions of GHGs (low confidence).

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In surface ocean, changes in the amplitude of seasonal variations in pH are also projected to occur with high confidence. ESMs in CMIP6 show +73 ± 12% increase in the amplitude of seasonal variation in hydrogen ion concentration ([H+]) but 10 ± 5% decrease in the seasonal variation in pH (= -log [H+]) from 1995–2014 to 2080–2099 under SSP5-8.5. The simultaneous amplification of [H+] and attenuation of pH seasonal cycles is counterintuitive but is the consequence of a greater increase in the annual mean [H+] due to anthropogenic CO2 invasion than the corresponding increase in its seasonal amplitude. These changes are consistent with the amplification/attenuation of the seasonal variation of +81 ±16% for [H+] and –16 ± 7% for pH from 1990–1999 to 2090–2099 under RCP8.5 in CMIP5 (Kwiatkowski and Orr, 2018).

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The signal of ocean acidification in surface ocean is large and is projected to emerge beyond the range of natural variability within the time scale of a decade in all ocean basins (Schlunegger et al., 2019). There is high agreement among modelling studies that the largest pH decline and large-scale undersaturation of aragonite in surface seawater start to occur first in polar oceans (Orr et al., 2005; Steinacher et al., 2009; Hurd et al., 2018; Jiang et al., 2019). Under SSP5-8.5, the largest surface pH decline, exceeding 0.45 between 1995–2014 and 2080–2099, occurs in the Arctic Ocean (Kwiatkowski et al., 2020). The freshwater input from sea ice melt is an additional factor leading to a faster decline of aragonite saturation level than expected from the anthropogenic CO2 uptake (Yamamoto et al., 2012). The increase in riverine and glacial discharges that provide terrigenous carbon, nutrients and alkalinity as well as freshwater are the other factors modifying the rate of acidification in the Arctic Ocean. However, their impacts have been projected in a limited number of studies with extensive knowledge gaps and model simplifications leading to low confidence in their impacts (Terhaar et al., 2019; Hopwood et al., 2020). In the Southern Ocean, the aragonite undersaturation starts in the 2030s in RCP8.5, and the area that experiences aragonite undersaturation for at least one month per year by 2100 is projected to be more than 95%. Under RCP2.6, short periods (less than one month) of aragonite undersaturation are expected to be found in less than 2% of the area during this century (Sasse et al., 2015; Hauri et al., 2016; Negrete-García et al., 2019). These long term projections are modified at interannual time scales by large-scale climate modes (Ríos et al., 2015) such as the ENSO and the Southern Annular Mode (Conrad and Lovenduski, 2015). In other regions, acidification trends are influenced by a range of processes such as changes in ocean circulation, temperature, salinity, carbon cycling, and the structure of the marine ecosystem. As, at present, models do not resolve fine-scale variability of these processes, current projections do not fully capture the changes that the marine environment will experience in the future (Takeshita et al., 2015; Turi et al., 2016).

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The coastal ocean, from the shoreline to the isobath of 200 m, is highly heterogeneous due to the complex interplay between physical, biogeochemical and anthropogenic factors (Gattuso et al., 1998; Chen and Borges, 2009; Dürr et al., 2011; Laruelle et al., 2014; McCormack et al., 2016). These areas, according to SROCC (Bindoff et al., 2019) are, with high confidence, already affected by ocean acidification and deoxygenation. This section assesses the drivers and spatial variability of acidification and deoxygenation based on new observations and data products.

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Observations and data products including models (Astor et al., 2013; Bakker et al., 2016; Kosugi et al., 2016; Vargas et al., 2016; Laruelle et al., 2017, 2018; Orselli et al., 2018; Roobaert et al., 2019; Cai et al., 2020; H. Sun et al., 2020) confirm the strong spatial and temporal variability in the coastal ocean surface carbonate chemistry and sea-air CO2 fluxes (high agreement, robust evidence). The anthropogenic CO2 -induced acidification is either mitigated or enhanced through biological processes; primary production removes dissolved CO2 from the surface, and respiration adds CO2 and consumes oxygen in the subsurface layers. The relative intensity of these processes is controlled by natural or anthropogenic eutrophication. Other drivers of variability include biological community composition, freshwater input from rivers or melting ice, sea ice cover and calcium carbonate precipitation/dissolution dynamics, coastal upwelling and regional circulation, and seasonal surface cooling (Fransson et al., 2015, 2017; Feely et al., 2018; Roobaert et al., 2019; Cai et al., 2020; Hauri et al., 2020; Monteiro et al., 2020b; H. Sun et al., 2020). Near-shore surface waters are often supersaturated with CO2, regardless of the latitude, especially in highly populated areas receiving substantial amounts of domestic and industrial sewage (Chen and Borges, 2009). Nevertheless, thermal or haline-stratified eutrophic coastal areas may act as net atmospheric CO2 sinks (Chou et al., 2013; Cotovicz Jr. et al., 2015). Continental shelves, excluding near-shore areas, act as CO2 sinks at a rate of 0.2 ± 0.02 PgC yr–1 (Laruelle et al., 2014; Roobaert et al., 2019), considering ice-free areas only. Under increasing atmospheric CO2 and eutrophication, such ecosystems would be more vulnerable to ecological and seawater chemistry changes, impacting the local economy.

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Since SROCC (Bindoff et al., 2019), there is further evidence that anthropogenic eutrophication via continental runoff and atmospheric nutrient deposition, and ocean warming are very likely the main drivers of deoxygenation in coastal areas (Levin and Breitburg, 2015; Levin et al., 2015; Royer et al., 2016; Breitburg et al., 2018; Cocquempot et al., 2019; Fagundes et al., 2020; Limburg et al., 2020). Increasing intensity and frequency of wind-driven upwelling is responsible for longer and more intense coastal hypoxia, fuelled by organic matter degradation from primary production(medium to high agreement, medium evidence) (Rabalais et al., 2010; Bakun et al., 2015; Varela et al., 2015; Fennel and Testa, 2019; Limburg et al., 2020). Locally, submarine groundwater discharge may enhance the eutrophication state (low agreement, limited evidence, Luijendijk et al., 2020). Since AR5 (Ciais et al., 2013) and SROCC (Bindoff et al., 2019) new observations and model studies confirm the trends in increasing coastal hypoxia caused by eutrophication, ocean warming and changes in circulation (Claret et al., 2018; Dussin et al., 2019; Limburg et al., 2020), as well as the ubiquitous impacts on marine organisms and fisheries (AR6 WGII Chapter 3; Carstensen and Conley, 2019; Fennel and Testa, 2019; Osma et al., 2020). Following open ocean deoxygenation trends since the 1950s, more than 700 coastal regions are being reported as hypoxic (dissolved oxygen concentration <2 mg O2L–1) (Limburg et al., 2020). Additionally, deoxygenation or increasing severe hypoxic periods in coastal areas may enhance the sea-to-air fluxes of N2O and CH4 especially through microbial-mediated processes in the water column–sediment interface (medium agreement) (Middelburg and Levin, 2009; Naqvi et al., 2010; Farías et al., 2015; Limburg et al., 2020).

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Observations and models predict an expansion and intensification of low-pH deep water intrusions for the north-east Pacific coastal upwelling area (high agreement, robust evidence) (Hauri et al., 2013; Feely et al., 2016; Cai et al., 2020). Areas such as the California Current System are naturally exposed to intrusions of low‐pH, high pCO2sea deep waters from remineralization processes and anthropogenic CO2 intrusion (Feely et al., 2008, 2010, 2018; Chan et al., 2019; Lilly et al., 2019; Cai et al., 2020).The eastern Pacific coastal upwelling displays seasonality in subsurface aragonite undersaturation as a consequence of the interplay between anthropogenic CO2, respiration and intrusion of upwelling waters (Feely et al., 2008, 2010, 2016, 2018; Hauri et al., 2013; Vargas et al., 2016; Chan et al., 2019; Lilly et al., 2019). The coastal south-east Pacific upwelling combined with low-pH, low-alkalinity, organic matter-rich river inputs display extreme temporal variability in surface seawaterpCO2 and low aragonite saturation (Vargas et al., 2016; Osma et al., 2020).

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Many coastal tropical areas are under heavy anthropogenic eutrophication induced by the effluents from large cities, or receive large riverine inputs of freshwater, nutrients, and organic matter (such as Amazon, Mississippi, Orinoco, Congo, Mekong, or Changjiang rivers). Under strong eutrophication, often sub-surface and bottom waters present pH values lower than average surface open ocean (about 8.0) because increased respiration decreases pH (high agreement, robust evidence), despite a net atmospheric CO2 sink in shallow and vertically stratified coastal areas (Koné et al., 2009; Wallace et al., 2014; Cotovicz Jr. et al., 2015, 2018; Fennel and Testa, 2019; Lowe et al., 2019; Section 5.3.5.1).

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There is medium evidence from observations and models that the coastal north-western Antarctic Peninsula (Southern Ocean) will experience calcium carbonate undersaturation by 2060, considering that anthropogenic emissions reach an atmospheric CO2 concentration of about 500 pm at that date (Lencina-Avila et al., 2018; Monteiro et al., 2020a). The synergies among warming, melt water, sea-air CO2 equilibrium and circulation may, to some extent, offset the coastal ocean acidification trends in Antarctica (Henley et al., 2020). In the coastal western Arctic Ocean, there is increasingrobust evidence that ocean acidification is driven by sea-air CO2 fluxes and sea-ice melt, and increasing intrusions since the 1990s of low-alkalinity Pacific water, lowering aragonite saturation state (Qi et al., 2017, 2020; Cross et al., 2018). The Bering Sea (north-eastern Pacific) shows decreasing trends in calcium carbonate saturation, associated to the increasing atmospheric CO2 uptake combined with riverine freshwater and carbon inputs (high agreement, robust evidence) (Pilcher et al., 2019; H. Sun et al., 2020).

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There is medium agreement (medium evidence) that simply reducing anthropogenic nutrient inputs may lead to less severe coastal hypoxic conditions, as observed in the coastal north-western Adriatic Sea (Djakovac et al., 2015). However, low-oxygen sediments may remain a long-term source of phosphorus and ammonium to the water column, and in this way fuelling primary production (Jokinen et al., 2018; Fennel and Testa, 2019; Limburg et al., 2020).

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Our understanding of the various biological processes that affect the strength of the CO2 fertilization effect on photosynthesis and its impact on carbon storage in vegetation and soils, (in particular regarding the limitations imposed by nitrogen and phosphorus availability), has developed since AR5 (WGI, Box 6.2). Based on consistent behaviour across all CMIP6 ESMs, there is high confidence that CO2 fertilization of photosynthesis acts as an important negative feedback on anthropogenic climate change, by reducing the rate at which CO2 accumulates in the atmosphere. Since AR5 (WGI, Box 6.2), an increasing number of CMIP6 ESMs account for nutrient cycles. The consistent results found in their model projections suggests with high confidence that limited nutrient availability will limit the CO2 fertilization effect (Arora et al., 2020). The magnitude of the direct CO2 effect on land carbon uptake, and its limitation by nutrients, remains uncertain.

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In AR5 (WGI, Section 6.4.2) there was high agreement that CMIP5 ESMs project continued ocean CO2 uptake through to 2100, with higher uptake corresponding to higher concentration or emissions pathways. There has been no significant change in the magnitude of the sensitivity of ocean carbon uptake to increasing atmospheric CO2, or in the inter-model spread, between the CMIP5 and CMIP6 era (Arora et al., 2020). The analysis from emissions and concentration-driven CMIP5 model projections show that the ocean sink stops growing beyond 2050 across all emissions scenarios (Section 5.4.5.3). CMIP6 models also show a similar time evolution of global ocean CO2 uptake to CMIP5 models over the 21st century (Figure 5.25) with decreasing net ocean CO2 uptake ratio to anthropogenic CO2 emissions under SSP5-8.5.

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The projected weakening of ocean carbon uptake is driven by a combination of decreasing carbonate buffering capacity and warming, which are positive feedbacks under weak to no mitigation scenarios (SSP4 and 5). In high mitigation scenarios (SSP1-2.6), weakening ocean carbon uptake is driven by decreasing emissions (Cross-Chapter Box 5.3). The detailed understanding of carbonate chemistry in seawater that has accumulated over more than half a century (e.g., Revelle and Suess, 1957; Egleston et al., 2010), provides high confidence that the excess CO2 dissolved in seawater leads to a non-linear reduction of the CO2 buffering capacity, that is smaller dissolved inorganic carbon (DIC) increase with respect to pCO2 increase along with the increase in cumulative ocean CO2 uptake. Recent studies (Katavouta et al., 2018; Jiang et al., 2019; Arora et al., 2020; Rodgers et al., 2020) suggest with medium confidence that the decrease in the ocean CO2 uptake ratio to anthropogenic CO2 emissions, under low to no mitigation scenarios over the 21st century, is predominantly attributable to the ocean carbon-concentration feedback through the reduction of the seawater CO2 buffering capacity, but with contributions from physical drivers such as warming and wind stress (medium confidence) and biological drivers (low confidence) (Sections 5.2.1.3.3 and 5.4.4).

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Projected increases in ocean DIC due to anthropogenic CO2 uptake amplify the sensitivity of carbonate system variables to perturbations of DIC in the surface ocean, for example via the amplitude of the seasonal cycle of pCO2, which impacts the mean annual air–sea fluxes (Hauck et al., 2015; Fassbender et al., 2018; Landschützer et al., 2018; SROCC, Section 5.2.2.3). A larger amplification of the surface oceanpCO2 seasonality occurs in the subtropics where pCO2 seasonality is dominated by temperature seasonality, with the summer increase in the difference inpCO2 between surface water and the overlying atmosphere reaching 3μatm per decade between 1990 and 2030 under RCP8.5 (Schlunegger et al., 2019; Rodgers et al., 2020). In contrast, the impact of biological production on the seasonal cycle of pCO2 in summer in the Southern Ocean strengthens the drawdown of CO2 (Hauck et al., 2015).

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Overall, there is medium confidence on three outcomes in the ocean from projected CO2 uptake under medium to high CO2 concentration scenarios: (i) a weakening of the buffering capacity, which impacts the airborne fraction via the reduction of the ocean CO2 buffering capacity due to cumulative ocean CO2 uptake, which reduces the net ocean CO2 uptake ratio to anthropogenic CO2 emissions (Katavouta et al., 2018; Arora et al., 2020; Rodgers et al., 2020); (ii) an amplification of the seasonal cycle of CO2 variables, which impacts both the ocean sink and ocean acidification (Hauck et al., 2015); (iii) a decrease in the aragonite and calcite saturation levels in the ocean, which negatively impacts the calcification rates of marine organisms (high confidence) and forms a negative feedback on the uptake of CO2 (McNeil and Sasse, 2016) (Cross-Chapter Box 5.3).

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Peat soils, where thick organic layers build up due to saturated and anoxic conditions, represent another possible source of carbon to the atmosphere. Peats could dry, and decompose or burn as a result of climate change in both high (Chaudhary et al., 2020) and tropical (Cobb et al., 2017) latitudes, and in combination with anthropogenic drainage of peatlands (Warren et al., 2017). Peat carbon dynamics are not included in the majority of CMIP6 ESMs. Soil microbial dynamics shift in response to temperature, giving rise to complex longer-term trophic effects that are more complex than the short-term sensitivity of decomposition to temperature. Such responses are observed in response to long-term warming experiments (Melillo et al., 2017). While most CMIP6 ESMs do not include microbial dynamics, simplified global soil models that do include such dynamics show greater uncertainty in projections of soil carbon changes, despite agreeing more closely with current observations, than the linear models used in most ESMs (Wieder et al., 2013; Guenet et al., 2018).

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The permafrost region was a historic carbon sink over centuries to millennia (high confidence) (Loisel et al., 2014; Lindgren et al., 2018). Currently though, thawing soils due to anthropogenic warming are losing carbon from the decomposition of old frozen organic matter, as found via carbon 14 (14C) signature of respiration at sites undergoing rapid permafrost thaw (Hicks Pries et al., 2013), of dissolved organic carbon in rivers draining watersheds with permafrost thaw (Vonk et al., 2015; Wild et al., 2019), and of methane (CH4) produced in thawing lakes (Walter Anthony et al., 2016).

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The principal contribution to increasing global ocean carbon is the air–sea flux of CO2, which changes the dissolved inorganic carbon (DIC) inventory (Section 5.4.2; Arora et al., 2020). The processes that influence the variability and trends of the ocean carbon–heat nexus are assessed in Cross-Chapter Box 5.3. Climate has three important impacts on the ocean uptake of anthropogenic CO2 : (i) ocean warming reduces the solubility of CO2, which increases pCO2 and increases the stratification of the mixed layer, both acting as positive feedbacks weakening the ocean sink (Section 9.2.1 and Cross-Chapter Box 5.3; Arora et al., 2020); (ii) changing the temporal and spatial characteristics of wind stress and storms alters mixing – entrainment in, and across the bottom of, the mixed layer (Bronselaer et al., 2018); and (iii) warming and wind stress influence the large-scale meridional overturning circulation (MOC) circulation, which modifies the rate of ventilation, storage or outgassing of ocean carbon in the ocean interior (Section 5.2.3.1; Gruber et al., 2019b; Arora et al., 2020). The land-to-ocean riverine flux and the carbon burial in ocean sediments play a minor role (low confidence) (Arora et al., 2020). Based on high agreement of projections by coupled climate models, there is high confidence that the resultant climate–carbon cycle feedbacks are positive, but the extent of the ocean sink weakening is scenario dependent (Arora et al., 2020).

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Regionally, the Southern Ocean is a major sink of anthropogenic CO2 (Figure 5.8a), although challenges in modelling its circulation and Antarctic sea ice transport (Sections 3.4.1.2, 9.2.3.2 and 9.3.2) generate uncertainty in the response of its sink to future carbon–climate feedbacks. Increased freshwater input may cause a slowdown of the lower overturning circulation, leading to increased Southern Ocean biological carbon storage (Ito et al., 2015); alternatively, increased winds may intensify the overturning circulation, reducing the net CO2 sink in the Southern Ocean (Bronselaer et al., 2018; Saunders et al., 2018). On centennial time scales, there is thus low confidence in the overall effect of intensifying winds in the Southern Ocean on CO2 uptake.

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While physical drivers control the present-day anthropogenic carbon sink, biological processes are responsible for the majority of the vertical gradient in DIC (natural carbon storage). A small fraction of the organic carbon fixed by primary production (PP) reaches the sea floor, where it can be stored in sediments on geological time scales, making the biological carbon pump (BCP) an important mechanism for very long-term CO2 storage. Projected reductions in ocean ventilation (Section 9.2.1.4) would lengthen residence time and lead to DIC accumulating in the deep ocean due to organic carbon remineralization.

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ESMs can be driven by anthropogenic CO2 emissions (‘emissions-driven’ runs), in which case atmospheric CO2 concentration is a predicted variable; or by prescribed time-varying atmospheric concentrations (‘concentration-driven’ runs). In concentration-driven runs, simulated land and ocean carbon sinks respond to the prescribed atmospheric CO2 and resulting changes in climate, but do not feed back through changes in the atmospheric CO2 concentration. Concentration-driven runs are used to diagnose the carbon emissions consistent with the Shared Socio-economic Pathways (SSPs) and other prescribed concentration scenarios (Section 5.5). In this subsection we specifically analyse results from concentration-driven ESM projections.

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In summary, oceanic and terrestrial carbon sinks are projected to continue to grow with increasing atmospheric concentrations of CO2, but the fraction of emissions taken up by land and ocean is expected to decline as the CO2 concentration increases (high confidence). In the ensemble mean, ESMs suggest approximately equal global land and ocean carbon uptake for each of the SSP scenarios. However, the range of model projections is much larger for the land carbon sink. Despite the wide range of model responses, uncertainty in atmospheric CO2 by 2100 is dominated by future anthropogenic emissions rather than carbon–climate feedbacks (high confidence).

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The applicability of the linear feedback framework (Section 5.4.5.5) suggests that large-scale biogeochemical feedbacks are approximately linear in the forcing from changes in CO2 and climate. Nevertheless, regionally the biosphere is known to be capable of producing abrupt changes or even ‘tipping points’ (Higgins and Scheiter, 2012; Lasslop et al., 2016). Abrupt change is defined as a change in the system that is substantially faster than the typical rate of the changes in its history (Section 1.4.5). A related matter is a tipping point: a critical threshold beyond which a system reorganizes, often abruptly and/or irreversibly. Possible abrupt changes in the Earth system include those related to ecosystems and biogeochemistry (Lenton et al., 2008; Steffen et al., 2018): tropical and boreal forest dieback; and release of greenhouse gases (GHGs) from permafrost and methane clathrates (Table 5.6). In this section we therefore focus on estimating upper limits on the possible impact of abrupt changes on the evolution of atmospheric GHGs out to 2100, for comparison to the impact of direct anthropogenic emissions.

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There is large uncertainty in release of GHGs from permafrost in the 21st century. The largest of these estimates implies tens to hundreds of gigatons of carbon released in the form of CO2 (Box 5.1) and CH4 emissions up to 100 TgCH4yr–1 (Box 5.1). A carbon dioxide release of such magnitude would lead to an increase in the CO2 accumulation rate in the atmosphere of ≤1 ppm yr–1. These emissions develop at a multi-decadal time scale. Assuming a CH4 lifetime in the atmosphere of the order of 10 years and the associated feedback parameter of 1.34 ± 0.04 (Section 6.2.2.1), this would increase the atmospheric CH4 content by about 500 ppb over the century, corresponding to a rate of ≤10 ppb yr–1. Irrespective of its origin, additional CH4 accumulation of such a magnitude is not expected to modify the temperature response to anthropogenic emissions by more than a few tenths of a °C (Gedney et al., 2004; Eliseev et al., 2008; Denisov et al., 2013). Emissions from permafrost thawing are assessed in Box 5.1.

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Based on the evidence presented in this section, we conclude that abrupt changes and tipping points in the biogeochemical cycles lead to additional uncertainty in 21st century GHG concentrations changes. However, these are very likely to be small compared to the uncertainty associated with future anthropogenic emissions (high confidence).

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Figure 5.30 shows carbon cycle changes to 2300 under three SSP scenarios with long-term extensions: SSP5-8.5, SSP5-3.4-overshoot, and SSP1-2.6, for four CMIP6 ESMs and one EMIC. Under all three scenarios, all five models project a reversal of the terrestrial carbon cycle from a sink to a source. However, the reasons for these reversals under very high emissions and low/negative emissions are very different. Under the SSP5-8.5 scenario, the terrestrial carbon–climate feedback is projected to strengthen, while the carbon-concentration feedbacks weaken after emissions peak at 2100, which together drives the land to become a net carbon source after 2100 (Tokarska et al., 2016). The difference in both timing and magnitude of this transition across the ensemble, leads to an assessment of medium confidence in the likelihood and low confidence in the timing and strength, of the land transitioning from a net sink to a net source under such a scenario. Based on high agreement across all available models, we assess with high confidence that the ocean sink strength would weaken but not reverse under a long-term high emissions scenario. In the SSP5-3.4-overshoot scenario, both the terrestrial and ocean reservoirs act as transient carbon sources during the overshoot period, when net anthropogenic CO2 emissions are negative and CO2 concentrations are falling, and then revert to near-zero (land) or weak sink (ocean) fluxes after stabilization of atmospheric CO2. The SSP1-2.6 scenario, characterized by lower peak CO2 concentrations, a smaller overshoot, and much less carbon loss from land-use change, shows instead a relaxation towards a neutral biosphere on land, and a sustained weak sink in the ocean (see also Section 5.6.2.2.1.2).

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Since AR5, new studies have further expanded the evidence base for estimating the value of TCRE. These studies rely on ESMs or EMICs, observational constraints and concepts of attributable warming, or theoretically derived equations (see Table 5.7 for an overview). Several studies have endeavoured to partition the uncertainty in the value of TCRE into constituent sources. For example, TCRE can be decomposed into terms of TCR and the airborne fraction of anthropogenic CO2 emissions over time (Allen et al., 2009; Matthews et al., 2009). These two terms are assessed individually (see Section 5.4 and Chapter 7, respectively) and allow the integration of evidence assessed elsewhere in the report into the assessment of TCRE (Section 5.5.1.4). Further studies use a variety of methods, including analysing the outputs from CMIP5 (R.G. Williams et al., 2017b) or CMIP6 (Arora et al., 2020; Jones and Friedlingstein, 2020), conducting perturbed parameter experiments with a single model (MacDougall et al., 2017), Monte-Carlo methods applied to a simple climate model (Spafford and Macdougall, 2020), or observations and estimates of the contribution of CO2 and non-CO2 forcers (Matthews et al., 2021). All of the studies agree that uncertainty in climate sensitivity – either equilibrium climate sensitivity (ECS) or transient climate response (TCR) – is among the most important contribution to uncertainty in TCRE, with uncertainty in the strength of the land carbon feedback and ocean heat uptake or ventilation having also been identified as crucial to uncertainty in TCRE (Matthews et al., 2009; Gillett et al., 2013; Ehlert et al., 2017; MacDougall et al., 2017; R.G. Williams et al., 2017a, 2020; Katavouta et al., 2019; Arora et al., 2020; Jones and Friedlingstein, 2020; Spafford and Macdougall, 2020). Finally, internal variability has been shown to affect the maximum accuracy of TCRE estimates by ±0.1°C per 1000 PgC (5–95% range; Tokarska et al., 2020).

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In the past 60 years, the ocean has taken up and stored 23 ± 5% of anthropogenic carbon emissions (medium confidence) (Section 5.2.1.3) as well as more than 90% of the heat that has accumulated in the Earth system (referred to as excess heat) since the 1970s (Sections 7.2.2, 9.2.2 and 9.2.3, and Box 7.2; Frölicher et al., 2015; Talley et al., 2016; Gruber et al., 2019b; Hauck et al., 2020). The interplay between heat and CO2 uptake by the ocean has played a major role in slowing the rate of global warming, and also provides a first order influence in determining the unique properties of a metric of the coupled climate–carbon cycle response – transient climate response to cumulative CO2 emissions (TCRE) – which is critical to setting the future remaining carbon emissions budget (Sections 5.5.1.3 and 5.5.4). This role of the ocean in the uptake of heat and anthropogenic CO2 and related feedbacks is what we term the ‘ocean carbon–heat nexus’. The ocean processes behind this nexus are important in shaping and understanding the near-linear relationship between cumulative CO2 emissions and global warming (TCRE) as well as the uncertainties in future projections of TCRE properties (Zickfeld et al., 2016; Bronselaer and Zanna, 2020; Jones and Friedlingstein, 2020), its path independence (MacDougall, 2017), and the warming commitment after cessation of greenhouse gas emissions – the zero emissions commitment (ZEC; Section 5.5.2; Zickfeld et al., 2016; Ehlert and Zickfeld, 2017). In this box, we assess the role of the ocean and its physical and chemical thermodynamic processes that shape these striking characteristics.

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The air–sea flux of heat and all gases across the ocean interface is driven by a common set of complex and turbulent diffusion and mixing processes that are difficult to observe (Sections 5.2.1.3 and 9.2.1.2; Wanninkhof et al., 2009; Wanninkhof, 2014; Cronin et al., 2019; Watson et al., 2020). These processes are typically simplified into widely verified expressions that link the flux to wind stress, the solubility and the gradient across the air–sea interface (medium confidence). Because the ocean has a higher heat capacity than the atmosphere (the heat capacity of the upper 100 m of the ocean is about 30 times larger than the heat capacity of the atmosphere), the partitioning of heat between the atmosphere and the ocean is primarily influenced by the temperature differences between air and seawater. Similarly, the unique seawater carbonate buffering capacity enables CO2 to be stored in the ocean as dissolved salts, rather than just as dissolved gas; this increases the capacity of seawater to store CO2 by two orders of magnitude beyond the solubility of CO2 gas and approximates the partitioning ratio of heat between the atmosphere and the ocean (Section 9.2.2.1; Zeebe and Wolf-Gladrow, 2009; Bronselaer and Zanna, 2020). The role of the biological carbon pump in influencing the ocean sink of anthropogenic carbon into the ocean interior is assessed to be minimal during the historical period, but this may change, particularly in regional contexts, by 2100 (medium confidence) (Laufkötter et al., 2015; Kwiatkowski et al., 2020). Its role is important in the natural or pre-industrial carbon cycle (medium confidence) (Henson et al., 2016).

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Under climate change, the buffering capacity of the ocean decreases (increasing Revelle Factor), which reflects a decreasing capacity for the ocean to take up additional anthropogenic CO2 and store it in the dissolved inorganic carbon reservoir (Egleston et al., 2010). In contrast to CO2, there is no physical limitation that would reduce the ability of surface ocean temperature to equilibrate with the atmospheric temperature. However, both carbon and heat fluxes depend on air–sea heat fluxes that in turn depend on gradients of characteristics at the air–sea interface. These gradients at the air–sea interface respond to ocean dynamics, such as the volume of the surface mixed-layer that equilibrates with the atmosphere, and ocean circulation that can flush the surface layer with water masses that have not equilibrated with the atmosphere for a long time. Limited recent evidence suggests that the effect of small-scale dynamics absent in climate and Earth system models might be locally important (Bachman and Klocker, 2020). In summary, changes in heat and carbon uptake by the ocean rely on a combination of unique chemical and shared physical processes, any of which have the potential to disrupt the coherence of heat and CO2 change in the ocean.

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The role of the large-scale circulation in shaping these fluxes is to: (i) flush the ocean surface layer with deep waters that are relatively cold and with weak or no anthropogenic CO2 and heat content because they have been isolated from the atmosphere for centuries; and (ii) transport the anthropogenic CO2 and heat at depth, away from the atmosphere (Frölicher et al., 2015; Marshall et al., 2015; Armour et al., 2016). For instance, in the Southern Ocean, upwelled water masses take up a large amount of anthropogenic CO2 and heat (Cross-Chapter Box 5.3, Figure 1), which are then exported northward by the circulation to be stored at depth in the Southern Hemisphere subtropical gyres (Cross-Chapter Box 5.3, Figure 1; Figure 9.7). In the North Atlantic, the signature of the Atlantic meridional overturning circulation (AMOC) is also clearly visible, with large amounts of heat and carbon being stored beneath the North Atlantic subtropical gyre at 1 km depth (Cross-Chapter Box 5.3, Figure 1). In summary, the net air–sea fluxes of anthropogenic CO2 and heat depend on large-scale circulation, which is associated with upper ocean stratification, mixed-layer depth, and water-mass formation, transport and mixing (Sections 9.1–9.3).

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Future projections of the ocean carbon–heat nexus in the second half of the 21st century, particularly those under weak or no mitigation scenarios, are characterized by the strengthening of the two largest positive feedbacks: weakening surface ocean CO2 buffering capacity (increasing Revelle Factor) and warming that further reduces CO2 solubility and strengthens ocean stratification, which reduces exchange between the ocean surface and interior (Jiang et al., 2019; Bronselaer and Zanna, 2020). These are offset by a growing but scenario-dependent negative feedback from increasing carbon and heat air–sea fluxes towards the ocean, due to increased atmospheric temperature and CO2 concentrations (Talley et al., 2016; Jiang et al., 2019; McKinley et al., 2020). The Southern Ocean in particular is one of the regions where the projected feedback can be largest and where inter-model differences are strongest (Roy et al., 2011; Frölicher et al., 2015; Hewitt et al., 2016; Mongwe et al., 2018). These projected trends in ocean carbonate chemistry (Section 5.4.2), together with surface ocean warming (Section 9.2.1.1), explain the slow down and long-term reduction of the ocean sink for anthropogenic CO2 even as emissions continue to rise beyond 2050 under weak-to-no-mitigation scenarios (Figures 2.7.1 and 5.25, and Technical Summary TS Box 7). Projected change in the North Atlantic and Southern Ocean overturning circulation also impact air–sea fluxes of heat and carbon. The very likely decline in AMOC in the 21st century for all shared socio-economic pathways (SSP) scenarios (Section 9.2.3.1) tends to reduce heat and carbon uptake, resulting in a positive feedback. In contrast, in the Southern Ocean, the future 21st century projected increase in upper ocean overturning circulation (low confidence) – due to increasing wind forcing projected for all scenarios, except those with large mitigation (SSP1-2.6) – produces a negative feedback, with increasing heat and carbon uptake and storage despite the increasing stratification and outgassing of natural CO2 in the upwelling zone (Sections 9.2.3.2 and 5.2.1.3).

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In summary, a combination of unique chemical properties of seawater carbonate combined with shared physical ocean processes explain the coherence and scaling in the uptake and storage of both CO2 and heat in the ocean, which is the basis for the carbon–heat nexus (high confidence). In this way, the processes of the ocean carbon-heat nexus help understand the quasi-linear and path independence of properties of TCRE, which forms the basis for the zero emissions commitment (ZEC; Section 5.5) (medium confidence). Future projections under low or no mitigation indicate with high confidence that carbon chemistry and warming will strengthen the positive feedback to climate change by reducing ocean carbon uptake, and medium confidence that ocean circulation may partially compensate that positive feedback by slightly increasing anthropogenic carbon storage. Increasing ocean warming and stratification may decrease exchanges between the surface and subsurface ocean, which could reduce the path independence of TCRE, though this effect can be partially counterbalanced regionally by increasing circulation associated with increasing winds (low confidence).

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The AR6 Glossary (Annex VII) defines remaining carbon budgets as the maximum amount of cumulative net global anthropogenic CO2 emissions expressed from a recent specified date that would result in limiting global warming to a given level with a given probability, taking into account the effect of other anthropogenic climate forcers, consistent with the definition used inSR1.5 (Rogelj et al., 2018b). Studies, however, apply a variety of definitions that result in published remaining carbon budget estimates informing to cumulative emissions at the time when global-mean temperature increase would reach, exceed, avoid, or peak at a given warming level with a given probability (M. Collins et al., 2013; T.F. Stocker et al., 2013; Clarke et al., 2014; Friedlingstein et al., 2014a; IPCC, 2014; Rogelj et al., 2016; Millar et al., 2017b).

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The impact of non-CO2 emissions on remaining carbon budgets is assessed with emulators (Meinshausen et al., 2009; Millar et al., 2017b; Gasser et al., 2018; Goodwin et al., 2018; Rogelj et al., 2018b; C.J. Smith et al., 2018; Matthews et al., 2021) that incorporate synthesized climate and carbon-cycle knowledge (Cross-Chapter Box 7.1). The estimated implied non-CO2 warming can subsequently be applied to reduce the remaining allowable warming for estimating the remaining carbon budget (Figure 5.31; Rogelj et al., 2018b, 2019). Alternative methods estimate the non-CO2 fraction of total anthropogenic forcing (Matthews et al., 2021), or do not correct for non-CO2 warming directly. The latter methods instead consider CO2 and non-CO2 warming together to define a CO2 -forcing equivalent carbon budget from which eventual non-CO2 contributions expressed in CO2 -forcing-equivalent emissions have to be subtracted to obtain a remaining carbon budget (Jenkins et al., 2018; Matthews et al., 2020). These studies also use emulators to invert a specified evolution of non-CO2 forcing to a corresponding amount of equivalent CO2 emissions (Matthews et al., 2020), or alternatively use empirical relationships linking changes in non-CO2 greenhouse gas emissions to warming (Cain et al., 2019). Methods to express non-CO2 emissions in CO2 equivalence are assessed in Section 7.6, yet their applicability and related uncertainties for remaining carbon budgets have not yet been covered in-depth in the literature.

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For historical warming, SR1.5 used GSAT increase between 1850–1900 and 2006–2015 of 0.97°C as its main starting point, while also providing values for other temperature metrics. Remaining carbon budgets were expressed starting from 1 January 2018 by accounting for historical emissions emitted from 1 January 2011 until the end of 2017. AR6 uses anthropogenic (human-induced) warming until the 2010–2019 period, which is assessed at the 0.8-1.3°C range, with a best estimate of 1.07°C (Table 3.1), and subsequently accounts for historical emissions from 1 January 2015 until the end of 2019 to express remaining carbon budget estimates from 1 January 2020 onwards. The human-induced warming between the 1850–1900 and 2006–2015 periods used in SR1.5 was assessed by AR6 at 0.97°C (Table 3.1). In a like-with-like comparison, the combined effect of data and methodological updates in historical warming estimates thus results in no shift in estimated remaining carbon budgets between SR1.5 and AR6. However, the emissions of the years passed since SR1.5 reduce the remaining carbon budget by about 85 GtCO2. Note that AR6 also updated its GSAT assessment for total warming between the 1850–1900 and 2006–2015 periods, reporting 0.94°C of warming. On a like-with-like basis, this would have resulted in slightly larger remaining carbon budgets compared to SR1.5 (Cross-Chapter Box 2.3).

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Carbon dioxide removal (CDR) refers to anthropogenic activities that seek to remove CO2 from the atmosphere and durably store it in geological, terrestrial or ocean reservoirs, or in products (Glossary). CO2 is removed from the atmosphere by enhancing biological or geochemical carbon sinks or by direct capture of CO2 from air and storage. Solar radiation modification (SRM) refers to the intentional, planetary-scale modification of the Earth’s radiative budget with the aim of limiting global warming. Most proposed SRM methods involve reducing the amount of incoming solar radiation reaching the surface, but others also act on the longwave radiation budget by reducing optical thickness and cloud lifetime (Glossary). SRM does not fall within the IPCC definitions of mitigation and adaptation (Glossary). CDR and SRM are referred to as ‘geoengineering’ in some of the literature, and are considered separately in this report.

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Coastal wetlands and seagrass meadows store significant amounts of carbon and are among the most productive ecosystems per unit area (Griscom et al., 2017, 2020; Ortega et al., 2019; Serrano et al., 2019). These rates could be reduced in the future, since these habitats are vulnerable to changing conditions, such as temperature, salinity, sediment supply, storm severity and continued coastal development (Bindoff et al., 2019; NASEM, 2019). These ecosystems are under threat from anthropogenic conversion and degradation and are being lost at rates between 0.7% and 7% per annum with consequent CO2 emissions (e.g., Atwood et al., 2017; Howard et al., 2017; Hamilton and Friess, 2018; Sasmito et al., 2019). Although sea level rise might lead to greater carbon sequestration in coastal wetlands (Rogers et al., 2019), there is high confidence that the frequency and intensity of marine heatwaves will increase (Cross-Chapter Box 9.1; Frölicher and Laufkötter, 2018; Laufkötter et al., 2020),which poses a more immediate threat to the integrity of coastal carbon stocks (Smale et al., 2019). Blue carbon restoration seeks to increase the rate of carbon sequestration, although restoration may be challenging, because of ongoing use of coastal land for human settlement, conversion to agriculture and aquaculture, shoreline hardening and port development.

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Nakano, H., M. Ishii, K.B. Rodgers, H. Tsujino, and G. Yamanaka, 2015: Anthropogenic CO2 uptake, transport, storage, and dynamical controls in the ocean imposed by the meridional overturning circulation: A modeling study. Global Biogeochemical Cycles, 29(10), 1706–1724, doi: 10.1002/2015gb005128.

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Orr, J.C. et al., 2005: Anthropogenic ocean acidification over the twenty-first century and its impact on calcifying organisms. Nature, 437(7059), 681–686, doi: 10.1038/nature04095.

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Peng, S. et al., 2016: Inventory of anthropogenic methane emissions in mainland China from 1980 to 2010. Atmospheric Chemistry and Physics, 16(22), 14545–14562, doi: 10.5194/acp-16-14545-2016.

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Rodgers, K.B. et al., 2020: Reemergence of Anthropogenic Carbon Into the Ocean’s Mixed Layer Strongly Amplifies Transient Climate Sensitivity. Geophysical Research Letters, 47(18), doi: 10.1029/2020gl089275.

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Ruddiman, W.F. et al., 2016: Late Holocene climate: Natural or anthropogenic?Reviews of Geophysics, 54(1), 93–118, doi: 10.1002/2015rg000503.

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Sabine, C.L. et al., 2004: The Oceanic Sink for Anthropogenic CO2. Science, 305(5682), 367–371, doi: 10.1126/science.1097403.

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Saeki, T. and P.K. Patra, 2017: Implications of overestimated anthropogenic CO2 emissions on East Asian and global land CO2 flux inversion. Geoscience Letters, 4(1), 9, doi: 10.1186/s40562-017-0074-7.

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Sallée, J.-B., R.J. Matear, S.R. Rintoul, and A. Lenton, 2012: Localized subduction of anthropogenic carbon dioxide in the Southern Hemisphere oceans. Nature Geoscience, 5(8), 579–584, doi: 10.1038/ngeo1523.

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Wang, Y., I. Hendy, and T.J. Napier, 2017: Climate and Anthropogenic Controls of Coastal Deoxygenation on Interannual to Centennial Timescales. Geophysical Research Letters, 44(22), 11528–11536, doi: 10.1002/2017gl075443.

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Global mean surface temperature (GMST) is a key indicator of the changing state of the climate system. Earth’s mean temperature history during the current geological era (Cenozoic, beginning 66 Ma (66 million years ago)) can be broadly characterized as follows (Cross-Chapter Box 2.1, Figure 1): (i) transient warming during the first 15 Myr (15 million years) of the Cenozoic, punctuated by the Paleocene–Eocene Thermal Maximum; (ii) a long-term cooling over tens of millions of years beginning around 50 Ma, driven by (among other factors) the slow drift of tectonic plates, which drove mountain building, erosion and volcanism, and reconfigured ocean passages, all of which ultimately moved carbon from the atmosphere to other reservoirs and led to the development of the Antarctic Ice Sheet (AIS) about 35–30 Ma; (iii) the intensification of cooling by climate feedbacks involving interactions among tectonics, ice albedo, ocean circulation, land cover and greenhouse gases, causing ice sheets to develop in the Northern Hemisphere (NH) by about 3 Ma; (iv) glacial-interglacial fluctuations paced by slow changes in Earth’s astronomical configuration (orbital forcing) and modulated by changes in the global carbon cycle and ice sheets on time scales of tens to hundreds of thousands of years, with particular prominence during the last 1 Myr; (v) a transition with both gradual and abrupt shifts from the Last Glacial Maximum to the present interglacial epoch (Holocene), with sporadic ice-sheet breakup disrupting ocean circulation; (vi) continued warming followed by minor cooling following the mid-Holocene, with superposed centennial- to decadal-scale fluctuations caused by volcanic activity, among other factors; (vii) recent warming related to the build-up of anthropogenic greenhouse gases (Sections 2.2.3 and 3.3.1).

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This section assesses the magnitude and rates of changes in both natural and anthropogenically mediated climate drivers over a range of time scales. First, changes in insolation (orbital and solar; Section 2.2.1), and volcanic stratospheric aerosol (Section 2.2.2) are assessed. Next, well-mixed greenhouse gases (GHGs; CO2, N2O and CH4) are covered in Section 2.2.3, with climate feedbacks and other processes involved in the carbon cycle assessed in Chapter 5. The section continues with the assessment of changes in halogenated GHGs (Section 2.2.4), stratospheric water vapour, stratospheric and tropospheric ozone (Section 2.2.5), and tropospheric aerosols (Section 2.2.6). Short-lived climate forcers (SLCFs), their precursor emissions and key processes are assessed in more detail in Chapter 6. Section 2.2.7 assesses the effect of historical land cover change on climate, including biophysical and biogeochemical processes. Section 2.2.8 summarizes the changes in the Earth’s energy balance since 1750 using the comprehensive assessment of effective radiative forcing (ERF) performed in Section 7.3. For some SLCFs with insufficient spatial or temporal observational coverage, ERFs are based on model estimates, but also reported here for completeness and context. Tabulated global mixing ratios of all well-mixed GHGs and ERFs from 1750–2019 are provided in Annex III.

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In this section and for calculation of ERF, surface global averages are determined from measurements representative of the well-mixed lower troposphere. Global averages that include sites subject to significant anthropogenic activities or influenced by strong regional biospheric emissions are typically larger than those from remote sites, and require weighting accordingly (Table 2.2). This section focusses on global mean mixing ratios estimated from networks with global spatial coverage, and updated from the CMIP6 historical dataset (Meinshausen et al., 2017) for periods prior to the existence of global networks.

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Other radiatively important gases with predominantly anthropogenic sources also continue to increase in abundance. SF6, used in electrical distribution systems, magnesium production, and semi-conductor manufacturing, increased from 7.3 ppt in 2011 to 10.0 ppt in 2019 (+36%). Alternatives to SF6 or SF6-free equipment for electrical systems have become available in recent years, but SF6 is still widely in use in electrical switch gear (Simmonds et al., 2020). The global lifetime of SF6 has been revised from 3200 years to about 1000 years (Kovács et al., 2017; Ray et al., 2017) with implications for climate emissions metrics (Section 7.6.2). NF3, which is used in the semi-conductor industry, increased 147% over the same period to 2.05 ppt in 2019. Its contribution to ERF remains small, however, at 0.0004 W m–2. The atmospheric abundance of SO2 f2, which is used as a fumigant in place of ozone-depleting methyl bromide, reached 2.5 ppt in 2019, a 46% increase from 2011. Its ERF also remains small at 0.0005 W m–2.

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Perfluorocarbons CF4 and C2 f6, which have exceedingly long global lifetimes, showed modest increases from 2011 to 2019. CF4, which has both natural and anthropogenic sources, increased 8.2% to 85.5 ppt, and C2 f6 increased 16.3% to 4.85 ppt. cC4F8, which is used in the electronics industry and may also be generated during the production of polytetrafluoroethylene (PTFE, also known as ‘Teflon’) and other fluoropolymers (Mühle et al., 2019), has increased 34% since 2011 to 1.75 ppt, although its ERF remains below 0.001 W m–2. Other PFCs, present at mixing ratios <1 ppt, have also been quantified (Droste et al., 2020; see Annex III).

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Anthropogenic aerosol is predominantly found in the fraction of particles with radii <1 µm that comprise the fine-mode AOD (AODf; Figure 2.9d; Kinne, 2019). A significant decline in AODf of more than 1.5% per year from 2000 to 2019 has occurred over Europe and North America, while there have been positive trends of up to 1.5% per year over Southern Asia and East Africa. The global-scale trend in AODf of –0.03% per year (Figure 2.9) is significant. The results are consistent with trend estimates from an aerosol reanalysis (Bellouin et al., 2020), and the trends in satellite-derived cloud droplet number concentrations are consistent with the aerosol trends (Cherian and Quaas, 2020). Cloudiness and cloud radiative properties trends are, however, less conclusive possibly due to their large variability (Norris et al., 2016; Cherian and Quaas, 2020). Further details on aerosol-cloud interactions are assessed in Section 7.3.3.2.

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The atmospheric concentrations of WMGHGs (Section 2.2.3) have continuously increased since the early 19th century, with CO2 contributing the largest share of the positive ERF. Compared to the last two decades of the 20th century, the growth rate of CO2 in the atmosphere increased in the 21st century, showed strong fluctuations for CH4, and was about constant for N2O. Mixing ratios of the most abundant CFCs declined (Section 2.2.4). Mixing ratios of HCFCs increased, but growth rates are starting to decelerate. Mixing ratios of HFCs and some other human-made components are increasing (Section 2.2.4). The ERF for CO2 alone is stronger than for all the other anthropogenic WMGHGs taken together throughout the industrial period, and its relative importance has increased in recent years (Figures 2.10 and 7.6).

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Carter, B.R. et al., 2019: Pacific Anthropogenic Carbon Between 1991 and 2017. Global Biogeochemical Cycles, 33(5), 597–617, doi: 10.1029/2018gb006154.

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Chai, Y. et al., 2020: Homogenization and polarization of the seasonal water discharge of global rivers in response to climatic and anthropogenic effects. Science of the Total Environment, 709, 136062, doi: 10.1016/j.scitotenv.2019.136062.

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Chan, D. and Q. Wu, 2015: Significant anthropogenic-induced changes of climate classes since 1950. Scientific Reports, 5(13487), doi: 10.1038/srep13487.

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Fischer, H. et al., 2018: Palaeoclimate constraints on the impact of 2°C anthropogenic warming and beyond. Nature Geoscience, 11(7), 474–485, doi: 10.1038/s41561-018-0146-0.

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Ghimire, B. et al., 2014: Global albedo change and radiative cooling from anthropogenic land cover change, 1700 to 2005 based on MODIS, land use harmonization, radiative kernels, and reanalysis. Geophysical Research Letters, 41(24), 9087–9096, doi: 10.1002/2014gl061671.

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Gingerich, P.D., 2019: Temporal Scaling of Carbon Emission and Accumulation Rates: Modern Anthropogenic Emissions Compared to Estimates of PETM-Onset Accumulation. Paleoceanography and Paleoclimatology, 34(3), 329–335, doi: 10.1029/2018pa003379.

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He, F. et al., 2014: Simulating global and local surface temperature changes due to Holocene anthropogenic land cover change. Geophysical Research Letters, 41(2), 623–631, doi: 10.1002/2013gl058085.

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Jeong, D., L. Sushama, and M. Naveed Khaliq, 2017: Attribution of spring snow water equivalent (SWE) changes over the northern hemisphere to anthropogenic effects. Climate Dynamics, 48, 3645–3658, doi: 10.1007/s00382-016-3291-4.

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Jongeward, A.R., Z. Li, H. He, and X. Xiong, 2016: Natural and Anthropogenic Aerosol Trends from Satellite and Surface Observations and Model Simulations over the North Atlantic Ocean from 2002 to 2012. Journal of the Atmospheric Sciences, 73(11), 4469–4485, doi: 10.1175/jas-d-15-0308.1.

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Li, F. et al., 2020: Towards quantification of Holocene anthropogenic land-cover change in temperate China: A review in the light of pollen-based REVEALS reconstructions of regional plant cover. Earth-Science Reviews, 203, 103119, doi: 10.1016/j.earscirev.2020.103119.

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Mitchell, L., E. Brook, J.E. Lee, C. Buizert, and T. Sowers, 2013: Constraints on the late Holocene anthropogenic contribution to the atmospheric methane budget (2013b). Science, 342(6161), 964–966, doi: 10.1126/science.1238920.

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Rhein, M., R. Steinfeldt, D. Kieke, I. Stendardo, and I. Yashayaev, 2017: Ventilation variability of Labrador Sea Water and its impact on oxygen and anthropogenic carbon: a review. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 375(2102), 20160321, doi: 10.1098/rsta.2016.0321.

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Sapart, C.J. et al., 2012: Natural and anthropogenic variations in methane sources during the past two millennia. Nature, 490, 85–88, doi: 10.1038/nature11461.

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Woosley, R.J., F.J. Millero, and R. Wanninkhof, 2016: Rapid anthropogenic changes in CO2 and pH in the Atlantic Ocean: 2003–2014. Global Biogeochemical Cycles, 30(1), 70–90, doi: 10.1002/2015gb005248.

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Wu, P., N. Christidis, and P. Stott, 2013: Anthropogenic impact on Earth’s hydrological cycle. Nature Climate Change, 3(9), 807–810, doi: 10.1038/nclimate1932.

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Zeebe, R.E., A. Ridgwell, and J.C. Zachos, 2016: Anthropogenic carbon release rate unprecedented during the past 66 million years. Nature Geoscience, 9(4), 325–329, doi: 10.1038/ngeo2681.

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Zheng, B. et al., 2018: Trends in China’s anthropogenic emissions since 2010 as the consequence of clean air actions. Atmospheric Chemistry and Physics, 18(19), 14095–14111, doi: 10.5194/acp-18-14095-2018.

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This chapter assesses the extent to which the climate system has been affected by human influence and to what extent climate models are able to simulate observed mean climate, changes and variability. This assessment is the basis for understanding what impacts of anthropogenic climate change may already be occurring and informs our confidence in climate projections. Moreover, an understanding of the amount of human-induced global warming to date is key to assessing our status with respect to the Paris Agreement goals of holding the increase in global average temperature to well below 2°C above pre-industrial levels and pursuing efforts to limit the temperature increase to 1.5°C (UNFCCC, 2016).

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This chapter starts with a brief description of methods for detection and attribution of observed changes in Section 3.2, which builds on the more general introduction to attribution approaches in the Cross-Working Group Box on Attribution in Chapter 1. In this chapter we assess the detection of anthropogenic influence on climate on large spatial scales and long temporal scales, a concept related to, but distinct from, that of the emergence of anthropogenically-induced climate change from the range of internal variability on local scales and shorter time scales (Section 1.4.2.2). The following sections address the climate system component by component, in each case assessing human influence and evaluating climate models’ simulations of the relevant aspects of climate and climate change. This chapter assesses the evaluation and attribution of global, hemispheric, continental and ocean basin-scale indicators of climate change in the atmosphere and at the Earth’s surface (Section 3.3, cryosphere (Section 3.4, ocean (Section 3.5, and biosphere (Section 3.6, and the evaluation and attribution of modes of variability (Section 3.7), the period of slower warming in the early 21st century (Cross-Chapter Box 3.1) and large-scale changes in extremes (Cross-Chapter Box 3.2). Model evaluation and attribution on sub-continental scales are not covered here, since these are assessed in the Atlas and in Chapter 10, and extreme event attribution is not covered since it is assessed in Chapter 11. Section 3.8 assesses multivariate attribution and integrative measures of model performance based on multiple variables, as well as process representation in different classes of models. The chapter structure is summarized in Figure 3.1.

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Considering the difficulty in accounting for climate modelling uncertainties in the regression-based approaches, Ribes et al. (2017) introduced a new statistical inference framework based on an additivity assumption and likelihood maximization, which estimates climate model uncertainty based on an ensemble of opportunity and tests whether observations are inconsistent with internal variability and consistent with the expected response from climate models. The method was further developed by Ribes et al. (2021), who applied it to narrow the uncertainty range in the estimated human-induced warming. Hannart and Naveau (2018), on the other hand, extended the application of standard causal theory (Pearl, 2009) to the context of detection and attribution by converting a time series into an event, and calculating the probability of causation, an approach which maximizes the causal evidence associated with the forcing. On the other hand, Schurer et al. (2018) employed a Bayesian framework to explicitly consider climate modelling uncertainty in the optimal regression method. Application of these approaches to attribution of large-scale temperature changes supports a dominant anthropogenic contribution to the observed global warming.

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Climate change signals can vary with time and discriminant analysis has been used to obtain more accurate estimates of time-varying signals, and has been applied to different variables such as seasonal temperatures (Jia and DelSole, 2012) and the South Asian monsoon (Srivastava and DelSole, 2014). The same approach was applied to separate aerosol forcing responses from other forcings (X. Yan et al., 2016) and results using climate model output indicated that detectability of the aerosol response is maximized by using a combination of temperature and precipitation data. Paeth et al. (2017) introduced a detection and attribution method applicable for multiple variables based on a discriminant analysis and a Bayesian classification method. Finally, a systematic approach has been proposed to translating quantitative analysis into a description of confidence in the detection and attribution of a climate response to anthropogenic drivers (Stone and Hansen, 2016).

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Surface temperature change is the aspect of climate in which the climate research community has had most confidence over past IPCC assessment reports. This confidence comes from the availability of longer observational records compared to other indicators, a large response to anthropogenic forcing compared to variability in the global mean, and a strong theoretical understanding of the key thermodynamics driving its changes (Collins et al., 2010; Shepherd, 2014). The AR5 assessed that it was extremely likely that human activities had caused more than half of the observed increase in global mean surface temperature from 1951 to 2010, and virtually certain that internal variability alone could not account for the observed global warming since 1951 (Bindoff et al., 2013). The AR5 also assessed with very high confidence that climate models reproduce the general features of the global-scale annual mean surface temperature increase over 1850–2011 and with high confidence that models reproduce global and Northern Hemisphere temperature variability on a wide range of time scales (Flato et al., 2013). This section assesses the performance of the new generation CMIP6 models (see Table AII.5) in simulating the patterns, trends, and variability of surface temperature, and the evidence from detection and attribution studies of human influence on large-scale changes in surface temperature.

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In addition, new literature suggests that anthropogenic forcing itself may locally increase or decrease variability in surface temperatures (Screen et al., 2014; Qian and Zhang, 2015; Brown et al., 2017; Park et al., 2018; Santer et al., 2018; Weller et al., 2020). These studies imply limitations in the use of pre-industrial control simulations to quantify the role of unforced variability over the historical period. Some recent attribution studies (Gillett et al., 2021; Ribes et al., 2021) have estimated variability from ensembles of forced simulations instead, which would be expected to resolve any such changes in variability.

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The previous paragraph took an ensemble-mean view of model performance, but individual models disagree on unforced variability. Figure 3.6 illustrates the large differences in GSAT variability in unforced CMIP6 pre-industrial control simulations, following the method of Parsons et al. (2020). Surface temperatures in pre-industrial conditions are especially variable in the ten models highlighted in Figure 3.6a, and some models substantially exceed the variability seen in CMIP5 models (Parsons et al., 2020). Figure 3.6b shows that the distribution of warming trends simulated by CMIP6 models in historical simulations is clearly distinct from that simulated in unforced pre-industrial control simulations. Still, the unforced variability of the five most variable models approaches half that observed over the historical period under anthropogenically forced conditions (Figure 3.6c; Parsons et al., 2020; Ribes et al., 2021). For the Centre National de la Recherche Météorologique (CNRM) models, which are among the most variable, the large, low-frequency variability is attributed to strong simulated Atlantic Multi-decadal Variability (Séférian et al., 2019; Voldoire et al., 2019b), which is difficult to rule out because of the short observational record (Section 3.7.7; Cassou et al., 2018). But, importantly, patterns of temperature variability simulated by even the most variable models differ from the pattern of forced temperature change (Parsons et al., 2020). Taken together, this discussion and Figures 3.2, 3.5 and 3.6 indicate that the statistics of internal variability in models compare well in most cases to observational estimates and temperature proxy reconstructions, though some CMIP6 models appear to have higher multi-decadal variability than CMIP5 models or proxy reconstructions. When used in attribution studies, models with overestimated variability would increase estimated uncertainties and make results statistically conservative.

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Looking at periods preceding the instrumental record, AR5 assessed with high confidence that the 20th century annual mean surface temperature warming reversed a 5000-year cooling trend in Northern Hemisphere mid- to high latitudes caused by orbital forcing, and attributed the reversal to anthropogenic forcing with high confidence (see also (Section 2.3.1.1). Since AR5, the combined response to solar, volcanic and greenhouse gas forcing was detected in all Northern Hemisphere continents (PAGES 2k-PMIP3 group, 2015) over the period 864 to 1840. In contrast, the effect of those forcings was not detectable in the Southern Hemisphere (Neukom et al., 2018). Global and Northern Hemisphere temperature changes from reconstructions over this period have been attributed mostly to volcanic forcing (Schurer et al., 2014; McGregor et al., 2015; Otto-Bliesner et al., 2016; PAGES 2k Consortium, 2019; Büntgen et al., 2020), with a smaller role for changes in greenhouse gas forcing, and solar forcing playing a minor role (Schurer et al., 2014; PAGES 2k Consortium, 2019).

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In contrast, studies published since AR5 indicate that closely constraining the separate contributions of greenhouse gas changes and aerosol changes to observed temperature changes remains challenging. Nonetheless, attribution of warming to greenhouse gas forcing has been found as early as the end of the 19th century (Schurer et al., 2014; Owens et al., 2017; PAGES 2k Consortium, 2019). Hegerl et al. (2019) found that volcanism cooled global temperatures by about 0.1°C between 1870 and 1910, then a lack of volcanic activity warmed temperatures by about 0.1°C between 1910 and 1950, with anthropogenic aerosols cooling temperatures throughout the 20th century, especially between 1950 and 1980 when the estimated range of aerosol cooling was about 0.1°C to 0.5°C. Jones et al. (2016) attributed a warming of 0.87 to 1.22°C per century over the period 1906 to 2005 to greenhouse gases, partially offset by a cooling of −0.54°C to −0.22°C per century attributed to aerosols. But they also found that detection of the greenhouse gas or the aerosol signal often fails, because of uncertainties in modelled patterns of change and internal variability. That point is illustrated by Figure 3.7, which shows two- and three-way fingerprinting regression coefficients for 13 CMIP6 models and the corresponding attributable warming ranges, derived using HadCRUT4 (Gillett et al., 2021). Regression coefficients with an uncertainty range that includes zero mean that detection has failed. Models with regression coefficients significantly less than one significantly overpredict the temperature response to the corresponding forcing. Conversely, models with regression coefficients significantly greater than one underpredict the response to these forcings. While estimates of warming attributable to anthropogenic influence derived using individual models are generally consistent, estimates of warming attributable to greenhouse gases and aerosols separately based on individual models are not all consistent, and detection of the aerosol influence fails more often than that of greenhouse gases. Hence, results of recent studies emphasize the need to use multi-model means to better constrain estimates of GSAT changes attributable to greenhouse gas and aerosol forcing (Schurer et al., 2018; Gillett et al., 2021; Ribes et al., 2021).

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Figure 3.8 compares attributable changes in globally complete GSAT for the period 2010–2019 relative to 1850–1900 from three detection and attribution studies, two of which use CMIP6 multi-model means (Gillett et al., 2021; Ribes et al., 2021), and an estimate based on assessed effective radiative forcing and transient and equilibrium climate sensitivity (see Section 7.3.5.3). The reference period 1850–1900 is used to assess attributable temperature changes because this is when the earliest gridded surface temperature records start, this is when the CMIP6 historical simulations start, this is the earliest base period used in attribution literature, and this is a reference period used in IPCC SR1.5 and earlier reports. It should, however, be noted that Cross-Chapter Box 1.2 assesses with medium confidence that there was an anthropogenic warming with a likely range of 0.0°C–0.2°C between 1750 and 1850–1900. Figure 3.8 also shows the GSAT changes directly simulated in response to these forcings in thirteen CMIP6 models. In spite of their different methodologies and input datasets, the three attribution approaches yield very similar results, with the anthropogenic attributable warming range encompassing observed warming, and the natural attributable warming being close to zero. The warming driven by greenhouse gas increases is offset in part by cooling due to other anthropogenic forcing agents, mostly aerosols, although uncertainties in these contributions are larger than the uncertainty in the net anthropogenic warming, as discussed above. Estimates based on physical understanding of forcing and ECS made by (Chapter 7 are close to estimates from attribution studies, despite being the products of a different approach. This agreement enhances confidence in the magnitude and causes of attributable surface temperature warming.

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The AR5 found high confidence for a major role for anthropogenic forcing in driving warming over each of the inhabited continents, except for Africa where they found only medium confidence because of limited data availability (Bindoff et al., 2013). At the hemispheric scale, Friedman et al. (2020) and Bonfils et al. (2020) detected an anthropogenically forced response of inter-hemispheric contrast in surface temperature change, which has a complex time evolution but shows the Northern Hemisphere cooling relative to the Southern Hemisphere until around 1975 but then warming after that. Bonfils et al. (2020) attribute the Northern Hemisphere reversal to a combination of reduced aerosol forcing and greenhouse gas induced warming of Northern Hemisphere land masses. Friedman et al. (2020) found that CMIP5 models simulate the correct sign of the inter-hemispheric contrast when forced with all forcings but underestimate its magnitude. Figure 3.9 shows global surface temperature change in CMIP6 all-forcing and natural-only simulations globally, averaged over continents, and separately over land and ocean surfaces. All-forcing simulations encompass observed temperature changes for all regions, while natural-only simulations fail to do so in recent decades except in Antarctica, based on the annual means shown. As stated above, warming results from a partial offset of greenhouse gas warming by aerosol cooling. That offset is stronger over land than ocean. Regionally, models show a large range of possible temperature responses to greenhouse gas and aerosol forcing, which complicates single-forcing attribution. A more detailed discussion of regional attribution can be found in Section 10.4. Over global land surfaces, Chan and Wu (2015) used CMIP5 simulations to attribute a warming trend of 0.3 (2.5%–97.5% confidence interval: 0.2–0.36) °C per decade to anthropogenic forcing, with natural forcing only contributing 0.05 (0.02–0.06) °C per decade. Accounting for unsampled sources of uncertainty and the availability of only a single study, their result suggests that it is very likely that human influence is the main driver of warming over land.

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In summary, since the publication of AR5, new literature has emerged that better accounts for methodological and climate model uncertainties in attribution studies (Ribes et al., 2017; Hannart and Naveau, 2018) and that concludes that anthropogenic warming is approximately equal to observed warming over the 1951–2010 period. The IPCC SR1.5 reached the same conclusion for 2017 relative to 1850–1900 based on anthropogenic warming and associated uncertainties calculated using the method of Haustein et al. (2017). Moreover, the improved understanding of the causes of the apparent slowdown in warming over the beginning of the 21st century and the difference in simulated and observed warming trends over this period (Cross-Chapter Box 3.1) further improve our confidence in the assessment of the dominant anthropogenic contribution to observed warming. In deriving our assessments, these considerations are balanced against new literature that raises questions about the ability of some models to simulate variability in surface temperatures over a range of time scales (Laepple and Huybers, 2014; Parsons et al., 2017; Friedman et al., 2020), and the finding that some CMIP6 models exhibit substantially higher multi-decadal internal variability than that seen in CMIP5, which remains to be fully understood (Parsons et al., 2020; Ribes et al., 2021). Further, uncertainties in simulated aerosol-cloud interactions are still large (Section 7.3.3.2.2), resulting in very diverse spatial responses of different climate models to aerosol forcing, and inter-model differences in the historical global mean temperature evolution and in diagnosed cooling attributable to aerosols (Figure 3.8). Moreover, like previous generations of coupled model simulations, historical and single forcing CMIP6 simulations follow a common experimental design (Eyring et al., 2016a; Gillett et al., 2016) and are thus all driven by the same common set of forcings, even though these forcings are uncertain. Hence, forcing uncertainty is not directly accounted for in most of the attribution and model evaluation studies assessed here, although this limitation can to some extent be addressed by comparing with previous generation multi-model ensembles or individual model studies using different sets of forcings.

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The IPCC SR1.5 best estimate and likely range of anthropogenic attributable GMST warming was 1.0 ± 0.2°C in 2017 with respect to the period 1850–1900. Here, the best estimate is expressed in terms of GSAT and is calculated as the average of the three estimates shown in Figure 3.9, yielding a value of 1.07°C. Ranges for attributable GSAT warming are derived by finding the smallest ranges with a precision of 0.1°C which span all of the 5–95% ranges from the attribution studies shown in Figure 3.9. These ranges are then assessed as likely rather than very likely because the studies may underestimate the importance of the structural limitations of climate models, which probably do not represent all possible sources of internal variability; use too simple climate models, which may underestimate the role of internal variability; or underestimate model uncertainty, especially when using model ensembles of limited size and inter-dependent models, for example through common errors in forcings across models, as discussed above. This leads to a likely range for anthropogenic attributable warming in 2010–2019 relative to 1850–1900 of 0.8 to 1.3°C in terms of GSAT. This range encompasses the best estimate and very likely range of observed GSAT warming of 1.06 [0.88 to 1.21] °C over the same period (Cross-Chapter Box 2.3). There is medium confidence that the best estimate and likely ranges of attributable warming expressed in terms of GMST are equal to those for GSAT (Cross-Chapter Box 2.3). Repeating the process for other time periods leads to the best estimates and likely ranges listed in Table 3.1. GSAT change attributable to natural forcings is −0.1 to +0.1°C. The likely range of GSAT warming attributable to greenhouse gases is assessed in the same way to be 1.0 to 2.0°C while the GSAT change attributable to aerosols, ozone and land-use change is −0.8 to 0.0°C. Progress in attribution techniques allows the important advance of attributing observed surface temperature warming since 1850–1900, instead of since 1951 as was done in AR5.

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The IPCC SR1.5 gave a likely range for the human-induced warming rate of 0.1°C to 0.3°C per decade in 2017, with a best estimate of 0.2°C per decade (Allen et al., 2018). Table 3.1 lists the estimates of attributable anthropogenic warming rate over the period 2010–2019 based on the three studies that underpin the assessment of GSAT warming (Haustein et al., 2017; Gillett et al., 2021; Ribes et al., 2021). Estimates from Haustein et al. (2017), based on observed warming, and Ribes et al. (2021), based on CMIP6 simulations constrained by observed warming, are in good agreement. The Gillett et al. (2021) estimate, also based on CMIP6 models, corresponds to a larger anthropogenic attributable warming rate, because of a smaller warming rate attributed to natural forcing than in Ribes et al. (2021). This disagreement does not support a decrease in uncertainty compared to the SR1.5 assessment. So the range for anthropogenic attributable surface temperature warming rate of 0.1°C to 0.3°C per decade is again assessed to be likely, with a best estimate of 0.2°C per decade.

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(Chapter 2 assessed that the troposphere has warmed since at least the 1950s, that it is virtually certain that the stratosphere has cooled, and that there is medium confidence that the upper troposphere in the tropics has warmed faster than the near-surface since at least 2001 (Section 2.3.1.2). The AR5 assessed that anthropogenic forcings, dominated by greenhouse gases, likely contributed to the warming of the troposphere since 1961 and that anthropogenic forcings, dominated by the depletion of the ozone layer due to ozone-depleting substances, very likely contributed to the cooling of the lower stratosphere since 1979. Since AR5, understanding of observational uncertainties in the radiosonde and satellite data has improved with more available data and longer coverage, and differences between models and observations in the tropical atmosphere have been investigated further.

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Several studies since AR5 have continued to demonstrate an inconsistency between simulated and observed temperature trends in the tropical troposphere, with models simulating more warming than observations (Mitchell et al., 2013, 2020; Santer et al., 2017a, b; McKitrick and Christy, 2018; Po-Chedley et al., 2021). Santer et al. (2017b) used updated and improved satellite retrievals to investigate model performance in simulating the tropical mid- to upper-troposphere trends, and removed the influence of stratospheric cooling by regression. These factors were found to reduce the size of the discrepancy in mid- to upper-tropospheric temperature trends between models and observations over the satellite era, but a discrepancy remained. Santer et al. (2017a) found that during the late 20th century, the discrepancies between simulated and satellite-derived mid- to upper-tropospheric temperature trends were consistent with internal variability, while during most of the early 21st century, simulated tropospheric warming was significantly larger than observed, which they related to systematic deficiencies in some of the external forcings used after year 2000 in the CMIP5 models. However, in CMIP6, differences between simulated and observed upper-tropospheric temperature trends persist despite updated forcing estimates (Mitchell et al., 2020). Figure 3.10 shows that CMIP6 models forced by combined anthropogenic and natural forcings overestimate temperature trends compared to radiosonde data (Haimberger et al., 2012) throughout the tropical troposphere (Mitchell et al., 2020). Over the 1979–2014 period, models are more consistent with observations in the lower troposphere, and least consistent in the upper troposphere around 200 hPa, where biases exceed 0.1°C per decade. Several studies using CMIP6 models suggest that differences in climate sensitivity may be an important factor contributing to the discrepancy between the simulated and observed tropospheric temperature trends (McKitrick and Christy, 2020; Po-Chedley et al., 2021), though it is difficult to deconvolve the influence of climate sensitivity, changes in aerosol forcing and internal variability in contributing to tropospheric warming biases (Po-Chedley et al., 2021). Another study found that the absence of a hypothesized negative tropical cloud feedback could explain half of the upper troposphere warming bias in one model (Mauritsen and Stevens, 2015).

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The AR5 assessed as likely that anthropogenic forcings, dominated by greenhouse gases, contributed to the warming of the troposphere since 1961 (Bindoff et al., 2013). Since then, there has been further progress in detecting and attributing tropospheric temperature changes. Mitchell et al. (2020) used CMIP6 models to find that the main driver of tropospheric temperature changes is greenhouse gases. Previous detection of the anthropogenic influence on tropospheric warming may have overestimated uncertainties: Pallotta and Santer (2020) found that CMIP5 climate models overestimate the observed natural variability in global mean tropospheric temperature on time scales of 5–20 years. Nevertheless, Santer et al. (2019) found that stochastic uncertainty is greater for tropospheric warming than stratospheric cooling because of larger noise and slower recovery time from the Mount Pinatubo eruption in the troposphere. The detection time of the anthropogenic signal in the tropospheric warming can be affected by both the model climate sensitivity and the model response to aerosol forcing. Volcanic forcing is also important, as models that do not consider the influence of volcanic eruptions in the early 21st century overestimate the observed tropospheric warming since 1998 (Santer et al., 2014). Changes in the amplitude of the seasonal cycle of tropospheric temperatures have also been attributed to human influence. Santer et al. (2018) found that satellite data and climate models driven by anthropogenic forcing show consistent amplitude increases at mid-latitudes in both hemispheres, amplitude decreases at high latitudes in the Southern Hemisphere, and small changes in the tropics.

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In summary, these studies confirm the dominant role of human activities in tropospheric temperature trends. We therefore assess that it is very likely that anthropogenic forcing, dominated by greenhouse gases, was the main driver of the warming of the troposphere since 1979.

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The AR5 concluded that the CMIP5 models simulated a generally realistic evolution of lower-stratospheric temperatures (Bindoff et al., 2013; Flato et al., 2013), which was better than that of the CMIP3 models, in part because they generally include time-varying ozone concentrations, unlike many of the CMIP3 models. Nonetheless, it was noted that there was a tendency for the simulations to underestimate stratospheric cooling compared to observations. Bindoff et al. (2013) concluded that it was very likely that anthropogenic forcing, dominated by stratospheric ozone depletion by chemical reactions involving trace species known as ozone-depleting substances (ODS), had contributed to the cooling of the lower stratosphere since 1979. Increased greenhouse gases cause near-surface warming but cooling of stratospheric temperatures.

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Upper stratospheric temperature changes were not assessed in the context of attribution or model evaluation in AR5, but this is an area where there has been considerable progress over recent years (Section 2.3.1.2.1). Simulated temperature changes in chemistry-climate models show good consistency with the reprocessed dataset from NOAA STAR but are less consistent with the revised UK Met Office record (Karpechko et al., 2018). The latter still shows stronger cooling than simulated in chemistry-climate models (Maycock et al., 2018). Reanalyses, which assimilate AMSU and SSU datasets, indicate an upper-stratospheric cooling from 1979 to 2009 of about 3°C at 5 hPa and 4°C at 1 hPa that agrees well with the cooling in simulations with prescribed SST and using CMIP5 forcings (Simmons et al., 2014). Mitchell (2016) used regularized optimal fingerprinting techniques to carry out an attribution analysis of annual mid- to upper-stratospheric temperature in response to external forcings. They found that anthropogenic forcing has caused a cooling of approximately 2°C–3°C in the upper stratosphere over the period of 1979–2015, with greenhouse gases contributing two thirds of this change and ozone depletion contributing one third. They found a large upper-stratospheric temperature change in response to volcanic forcing (0.4°C–0.6°C for Mount Pinatubo) but that change is still smaller than the lower-stratospheric signal. Aquila et al. (2016) found that the cooling of the middle and upper stratosphere after 1979 is mainly due to changes in greenhouse gas concentrations. Volcanic eruptions and the solar cycle were found not to affect long-term stratospheric temperature trends but to have short-term influences.

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In summary, based on the latest updates to satellite observations of stratospheric temperature, we assess that simulated and observed trends in global mean temperature through the depth of the stratosphere are more consistent than based on previous datasets, but some differences remain (medium confidence). Studies published since AR5 increase our confidence in the simulated stratospheric temperature response to greenhouse gas and ozone changes, and support an assessment that it is extremely likely that stratospheric ozone depletion due to ozone-depleting substances was the main driver of the cooling of the lower stratosphere between 1979 and the mid-1990s, as expected from physical understanding. Similarly, revised observations and new studies support an assessment that it is extremely likely that anthropogenic forcing, both from increases in greenhouse gas concentrations and depletion of stratospheric ozone due to ozone-depleting substances, was the main driver of upper-stratospheric cooling since 1979.

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Studies since AR5 identify Pacific Decadal Variability (PDV) as the leading mode of variability associated with unforced decadal GSAT fluctuations, with additional influence from Atlantic Multi-decadal Variability (Annex IV.2.6, IV.2.7; Brown et al., 2015; Dai et al., 2015; Steinman et al., 2015; Pasini et al., 2017). PDV transitioned from positive (El Niño-like) to negative (La Niña-like) phases during the slow warming period (Figure 3.39f and Cross-Chapter Box 3.1, Figure 1c). Model ensemble members that capture the observed slower decadal warming under transient forcing, and time segments of model simulations that show decadal GSAT decreases under fixed radiative forcing, also feature negative PDV trends (Cross-Chapter Box 3.1, Figure 1d; Meehl et al., 2011, 2013, 2014; Maher et al., 2014; Middlemas and Clement, 2016), suggesting the influence of PDV. This is confirmed by statistical models with the PDV-GSAT relationship estimated from observations and model simulations (Schmidt et al., 2014; Meehl et al., 2016b; Hu and Fedorov, 2017), selected ensemble members and time segments from model simulations where PDV by chance evolves in phase with observations over the slow warming period (Huber and Knutti, 2014; Risbey et al., 2014), and coupled model experiments in which PDV evolution is constrained to follow the observations (Kosaka and Xie, 2013, 2016; England et al., 2014; Watanabe et al., 2014; Delworth et al., 2015). Part of the PDV trend may have been driven by anthropogenic aerosols (Smith et al., 2016); however, this result is model-dependent, and internally-driven PDV dominates the forced PDV signal in the CMIP6 multi-model ensemble (Section 3.7.6). It is also notable that there is large uncertainty in the magnitude of the PDV influence on GSAT across models (Deser et al., 2017a; C.-Y. Wang et al., 2017) and among the studies cited above. In addition to PDV, contributions to the reduced warming trend from wintertime Northern Hemisphere atmospheric internal variability, particularly associated with a trend towards the negative phase of the Northern Annular Mode/North Atlantic Oscillation (Annex IV.2.1; Guan et al., 2015; Saffioti et al., 2015; Iles and Hegerl, 2017) or the Cold Ocean–Warm Land (COWL) pattern (Molteni et al., 2017; Yang et al., 2020) have been suggested, leading to regional continental cooling over a large part of Eurasia and North America (Cross-Chapter Box 3.1, Figure 1c; C. Li et al., 2015; Deser et al., 2017a; Gan et al., 2019).

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CMIP5 historical simulations driven by observed forcing variations ended in 2005 and were extended with RCP scenario simulations for model-observation comparisons beyond that date. Post AR5 studies based on updated external forcing show that while no net effect of updated anthropogenic aerosols is found on GSAT trends (Murphy, 2013; Gettelman et al., 2015; Oudar et al., 2018), natural forcing by moderate volcanic eruptions in the 21st century (Haywood et al., 2014; Ridley et al., 2014; Santer et al., 2014) and a prolonged solar irradiance minimum around 2009 compared to the normal 11-year cycle (Lean, 2018) yield a negative contribution to radiative forcing, which was missing in CMIP5 (Figure 2.2). This explains part of the difference between observed and CMIP5 trends, as shown based on EMIC simulations (Huber and Knutti, 2014; Ridley et al., 2014), statistical and mathematical models (Schmidt et al., 2014; Lean, 2018), and process-based climate models (Santer et al., 2014). However, in a single climate model study by Thorne et al. (2015), updating most forcings (greenhouse gas concentrations, solar irradiance, and volcanic and anthropogenic aerosols) available when the study was done made no significant difference to the 1998–2012 GMST trend from that obtained with original CMIP5 forcing. Potential underestimation of volcanic (negative) forcing may have played a role (Outten et al., 2015). In the multi-model ensemble mean, the 1998–2012 GMST trends are almost equal in CMIP5 and CMIP6 (Cross-Chapter Box 3.1, Figure 1a), suggesting compensation by a higher transient climate response and equilibrium climate sensitivity in CMIP6 than CMIP5 (Section 7.5.6). To summarize, while there is medium confidence that natural forcing that was missing in CMIP5 contributed to the difference of observed and simulated GMST trends, confidence remains low in the quantitative contribution of net forcing updates.

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With updated observation-based GMST datasets and forcing, improved analysis methods, new modelling evidence and deeper understanding of mechanisms, there is very high confidence that the slower GMST and GSAT increase inferred from observations in the 1998–2012 period was a temporary event induced by internal and naturally-forced variability that partly offset the anthropogenic warming trend over this period. Nonetheless, the heating of the climate system continued during this period, as reflected in the continued warming of the global ocean (very high confidence) and in the continued rise of hot extremes over land (medium confidence). Considering all the sources of uncertainties, it is impossible to robustly identify a single cause of the early 2000s slowdown (Hedemann et al., 2017; Power et al., 2017); rather, it should be interpreted as due to a combination of several factors (Huber and Knutti, 2014; Schmidt et al., 2014; Medhaug et al., 2017).

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A fact hindering detection and attribution studies in precipitation and other hydrological variables is the large internal variability of these fields relative to the anthropogenic signal. This low signal-to-noise ratio hinders the emergence of the anthropogenic signal from natural variability. Moreover, the sign of the change depends on location and time of the year. Paleoclimate records provide valuable context for observed trends in the 20th and 21st century and assist with the attribution of these trends to human influence (see also (Section 2.3.1.3.1). By nature, hydrological proxy data represent regional conditions, but taken together can represent large-scale patterns. As an example of how paleorecords have helped assessing the origin of changes, we consider some, mainly subtropical, regions which have experienced systematic drying in recent decades (see also Section 8.3.1.3). Paleoclimate simulations of monsoons are assessed in Section 3.3.3.2.

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Records of tree ring width have provided evidence that recent prolonged dry spells in the Levant and Chile are unprecedented in the last millennium (high confidence) (Cook et al., 2016a; Garreaud et al., 2017). East Africa has also been drying in recent decades (Rowell et al., 2015; Hoell et al., 2017), a trend that is unusual in the context of the sedimentary paleorecord spanning the last millennium (Tierney et al., 2015). This may be a signature of anthropogenic forcing but cannot yet be distinguished from natural variability (Hoell et al., 2017; Philip et al., 2018). Likewise, tree rings indicate that the 2012–2014 drought in the south-western United States was exceptionally severe in the context of natural variability over the last millennium, and may have been exacerbated by the contribution of anthropogenic temperature rise (medium confidence) (Griffin and Anchukaitis, 2014; Williams et al., 2015). Furthermore, Williams et al. (2020) used a combination of hydrological modelling and tree-ring reconstructions to show that the period from 2000 to 2018 was the driest 19-year span in south-western North America since the late 1500s. Nonetheless, tree rings also indicate the presence of prolonged megadroughts in western North America throughout the last millennium that were more severe than 20th and 21st century events (high confidence) (Cook et al., 2004, 2010, 2015). These were associated with internal variability (Coats et al., 2016; Cook et al., 2016b) and indicate that large-magnitude changes in the water cycle may occur irrespective of anthropogenic influence (see also McKitrick and Christy, 2019).

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The AR5 concluded that an anthropogenic contribution to increases in specific humidity is found with medium confidence at and near the surface. A levelling off of atmospheric water vapour over land in the last two decades that needed better understanding, and remaining observational uncertainties, precluded a more confident assessment (Bindoff et al., 2013). Sections 4.5.1.3 and 8.3.1.4 show that there have been significant advances in the understanding of the processes controlling land surface humidity. In particular, there has been a focus on the role of oceanic moisture transport and land-atmosphere feedbacks in explaining the observed trends in relative humidity.

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The detection and attribution of tropospheric water vapour changes can be traced back to Santer et al. (2007), who used estimates of atmospheric water vapour from the satellite-based Special Sensor Microwave Imager (SSM/I) and from CMIP3 historical climate simulations. They provided evidence of human-induced moistening of the troposphere, and found that the simulated human fingerprint pattern was detectable at the 5% level by 2002 in water vapour satellite data (from 1988 to 2006). The observed changes matched the historical simulations forced by greenhouse gas changes and other anthropogenic forcings, and not those due to natural variability alone. Then, Santer et al. (2009) repeated this study with CMIP5 models, and found that the detection and attribution conclusions were not sensitive to model quality. These results demonstrate that the human fingerprint is governed by robust and basic physical processes, such as the water vapour feedback. Finally, Chung et al. (2014) extended this line of research by focusing on the global-mean water vapour content in the upper troposphere. Using satellite-based observations and sets of CMIP5 climate simulations run under various climate-forcing options, they showed that the observed moistening trend of the upper troposphere over the 1979–2005 period could not be explained by internal variability alone, but is attributable to a combination of anthropogenic and natural forcings. This increase in water vapour is accompanied by a reduction in mid-tropospheric relative humidity and clouds in the subtropics and mid-latitude in both models and observations related to changes in the Hadley cell (Section 3.3.3.1.1; Lau and Kim, 2015).

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Dunn et al. (2017) confirmed earlier findings that global mean surface relative humidity increased between 1973 and 2000, followed by a steep decline (also reported in Willett et al., 2014) until 2013, and specific humidity correspondingly increased and then remained approximately constant (see also (Section 2.3.1.3.2), with none of the CMIP5 models capturing this behaviour. They noted biases in the mean state of the CMIP5 models’ surface relative humidity (and ascribed the failure to the representation of land surface processes and their response to CO2 forcing), concluding that these biases preclude any detection and attribution assessment. On the other hand, Byrne and O’Gorman (2018) showed that the positive trend in specific humidity continued in recent years and can be detected over land and ocean from 1979 to 2016. Moreover, they provided a theory suggesting that the increase in annual surface temperature and specific humidity as well as the decrease in relative humidity observed over land are linked to warming over the neighbouring ocean. They also pointed out that the negative trend in relative humidity over land regions is quite uncertain and requires further investigation. A recent study has also identified an anthropogenically-driven decrease in relative humidity over the Northern Hemisphere mid-latitude continents in summer between 1979 and 2014, which was underestimated by CMIP5 models (Douville and Plazzotta, 2017). Furthermore, in a modelling study Douville et al. (2020) showed that this decrease in boreal summer relative humidity over mid-latitudes is related not only to global ocean warming, but also to the physiological effect of CO2 on plants in the land surface model.

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Since AR5, X. Li et al. (2016b) found that CMIP5 models simulate the large scale patterns of annual mean land precipitation and seasonality well, as well as reproducing qualitatively the observed zonal mean land precipitation trends for the period 1948–2005: models capture the drying trends in the tropics and at 45°S and the wetting trend in the Northern Hemisphere mid- to high latitudes, but the amplitudes of the changes are much smaller than observed. Land precipitation was found to show enhanced seasonality in observations (Chou et al., 2013), qualitatively consistent with the simulated response to anthropogenic forcing (Dwyer et al., 2014). However, models do not appear to reproduce the zonal mean trends in the magnitude of the seasonal cycle over the period 1948–2005, nor the two-dimensional distributions of trends of annual precipitation and seasonality over land, but differences may be explainable by internal variability (X. Li et al., 2016b). However, observed trends in seasonality depend on data set used (X. Li et al., 2016b; Marvel et al., 2017), and Marvel et al. (2017) found that observed changes in the annual cycle phase are consistent with model estimates of forced changes. These phase changes are mainly characterized by earlier onset of the wet season on the equatorward flanks of the extratropical storm tracks, particularly in the Southern Hemisphere. Box 8.2 assesses regional changes in water cycle seasonality.

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Based on long-term island precipitation records, Polson et al. (2016) identified significant increases in precipitation in the tropics and decreases in the subtropics, which are consistent with those simulated by the CMIP5 models. Moreover, results from Polson and Hegerl (2017) give support to an intensification of the water cycle according to the wet-gets-wetter, dry-gets-drier paradigm over tropical land areas as well. Other studies suggest that this paradigm does not necessarily hold over dry regions where moisture is limited (see also Section 8.2.2.1; Greve et al., 2014; Kumar et al., 2015). Polson and Hegerl (2017) explained this discrepancy by taking into account the seasonal and interannual movement of the regions (Allan, 2014). A follow-up study using CMIP6 models also found that the observed strengthening contrast of precipitation over wet and dry regions was detectable, although the increase was significantly larger in observations than in the multi-model mean. The change was attributed to a combination of anthropogenic and natural forcings, with anthropogenic forcings detectable in multi-signal analyses (Figure 3.14; Schurer et al., 2020).

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Global land precipitation has likely increased since the middle of the 20th century (medium confidence), while there is low confidence in trends in land data prior to 1950 and over the ocean during the satellite era due to disagreement between datasets (Section 2.3.1.3.4). Figure 3.15a shows the time evolution of the global mean land precipitation since 1950, as well as the trend during the period. Adler et al. (2017) found no significant trend in the global mean precipitation during the satellite era, consistent with model simulations (Wu et al., 2013) and physical understanding of the energy budget (Section 8.2.1). This has been suggested to be due to the negative effect of anthropogenic sulphate aerosol that opposed the positive influence of rising global mean temperatures due to greenhouse gases (Salzmann, 2016; Richardson et al., 2018). The precipitation change expected from ocean warming is also partly offset by the fast atmospheric adjustment to increasing greenhouse gases (Section 8.2.1). Over the ocean, the negligible trend may be due to the cancelling effects of CO2 and aerosols (Richardson et al., 2018).

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A gridpoint based analysis of annual precipitation trends over land regions since 1901 (Knutson and Zeng, 2018) comparing observed and simulated trends found that detectable anthropogenic increasing trends have occurred prominently over many mid- to high-latitude regions of the Northern Hemisphere and subtropics of the Southern Hemisphere. The observed trends in many cases are significantly stronger than modelled in the CMIP5 historical runs for the 1901–2010 period (though not for 1951–2010), which may be due to disagreement between observed datasets (Section 2.3.1.3.4), and/or suggest possible deficiencies in models.

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The observed precipitation increase in the Northern Hemisphere high latitudes over the period 1966–2005 was attributed to anthropogenic forcing by a study using CMIP5 models (Wan et al., 2015) supporting the AR5 assessment. Initial results from CMIP6 also support the role of anthropogenic forcing in the precipitation increase observed in Northern Hemisphere high latitudes (see Figure 3.15c): the observed positive trend detected for the band 60°N–90°N can only be reproduced when anthropogenic forcing is included, although models tend to simulate overall a larger positive trend. A similar positive trend, but less significant, is also detected between 30°N–60°N, while in the southern mid-latitudes no trend is simulated (see Figure 3.15d, f).

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Overall, studies published since AR5 provide further evidence of an anthropogenic influence on precipitation, and therefore we now assess that it is likely that human influence has contributed to large-scale precipitation changes observed since the mid-20th century. New attribution studies strengthen previous findings of a detectable increase in mid to high latitude land precipitation over the Northern Hemisphere (high confidence). There is medium confidence that human influence has contributed to a strengthening of the zonal mean wet tropics-dry subtropics contrast, and that tropical rainfall changes follow the wet-gets-wetter, dry-gets-drier paradigm. There is also medium confidence that ozone depletion has increased precipitation over the southern high latitudes and decreased it over southern mid-latitudes during austral summer. Owing to observational uncertainties and inconsistent results between studies, we conclude that there is low confidence in the attribution of changes in the seasonality of precipitation.

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Streamflow is to-date the only variable of the terrestrial water cycle with enough in-situ observations to allow for detection and attribution analysis at continental to global scales. Based on evidence from a few formal detection and attribution studies, particularly on the timing of peak streamflow, and the qualitative evaluation of studies reporting on observed and simulated trends, AR5 concluded that there is medium confidence that anthropogenic influence on climate has affected streamflow in some middle and high latitude regions (Bindoff et al., 2013). The AR5 also noted that observational uncertainties are large and that often only a limited number of models were considered.

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(Section 2.3.1.3.6 assesses that there have not been significant trends in global average streamflow over the last century, though regional trends have been observed, driven in part by internal variability. Only a limited number of studies have systematically compared observed streamflow trends at continental to global scales with changes simulated by global circulation models in a detection and attribution setting. H. Yang et al. (2017) did not find a significant correlation between observed runoff changes and changes simulated in CMIP5 models in most grid cells, consistent with the assessment that observed changes are dominated by internal variability. In a pan-European assessment, Gudmundsson et al. (2017) attributed the spatio-temporal pattern of decreasing streamflow in southern Europe and increasing streamflow in northern Europe to anthropogenic climate change, but also concluded that additional effects of human water withdrawals could not be excluded. Focussing on continental runoff between 1958 and 2004, Alkama et al. (2013) found a significant change only when using reconstructed data over all rivers, and a large uncertainty in the estimate of the global streamflow trend due to opposing changes over different continents. Gedney et al. (2014) detected the influence of aerosols on streamflow in North America and Europe, with aerosols having driven an increase in streamflow due to reduced evaporation (see Section 8.3.1.5 for details on processes). There is also evidence for a detectable anthropogenic contribution toward earlier winter-spring streamflows in the north-central US (Kam et al., 2018) and in western Canada (Najafi et al., 2017). From a model evaluation perspective, Sheffield et al. (2013) reported that CMIP5 models reproduce spatial variations in runoff in North America well, though they tend to underestimate it.

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In summary, there is medium confidence that anthropogenic climate change has altered local and regional streamflow in various parts of the world and that the associated global-scale trend pattern is inconsistent with internal variability. Moreover, human interventions and water withdrawals, while affecting streamflow, cannot explain the observed spatio-temporal trends (medium confidence).

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One important aspect of various indicators of temperature extremes is their connection to mean temperature at local, regional and global scales. For example, the highest daily temperature in a summer is often highly correlated with the summer mean temperature. Model projections show that changes in temperature extremes are often closely related to shifts in mean temperature (Seneviratne et al., 2016; Kharin et al., 2018). It is thus no surprise that changes in temperature extremes are consistent with warming mean temperature, with warming leading to more hot extremes and fewer cold extremes. Given the attribution of mean warming to human influence (Section 3.3.1), and the connection between changes in mean and extreme temperatures, it is to be expected that anthropogenic forcing has also influenced temperature extremes.

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(Chapter 11 assesses that there is high confidence that climate models can reproduce the mean state and overall warming of temperature extremes observed globally and in most regions, although the magnitude of the trends may differ, and the ability of models to capture observed trends in temperature-related extremes depends on the metric evaluated, the way indices are calculated, and the time periods and spatial scales considered (Section 11.3.3). There has been widespread evidence of human influence on various aspects of temperature extremes, at global, continental, and regional scales. This includes attribution to human influence of observed changes in intensity, frequency, and duration and other relevant characteristics at global and continental scales (Section 11.3.4). The left-hand panel of Cross-Chapter Box 3.2, Figure 1 clearly shows that long-term changes in the global mean annual maximum daily maximum temperature can be reproduced by both CMIP5 and CMIP6 models forced with the combined effect of natural and anthropogenic forcings, but cannot be reproduced by simulations under natural forcing alone. Consistent with the assessment for global mean temperature (Section 3.3.1), aerosol changes are found to have offset part of the greenhouse gas induced increase in hot extremes globally and over most continents over the 1951–2015 period (Hu et al., 2020; Seong et al., 2021), though greenhouse gas and aerosol influences are less clearly separable in observed changes in cold extremes.

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An important piece of evidence supporting the SREX and AR5 assessment that there is medium confidence that anthropogenic forcing has contributed to a global scale intensification of heavy precipitation during the second half of the 20th century is the evidence for anthropogenic influence on other aspects of the global hydrological cycle. The most significant aspect of that is the increase in atmospheric moisture content associated with warming which should, in general, lead to enhanced extreme precipitation, particularly associated with enhanced convergence in tropical and extratropical cyclones (Sections 8.2.3.2 and 11.4.1). Such a connection is supported by the fact that annual maximum one-day precipitation increases with global mean temperature at a rate similar to the increase in the moisture holding capacity in response to warming, both in observations and in model simulations. Additionally, models project an increase in extreme precipitation across global land regions even in areas in which total annual or seasonal precipitation is projected to decrease.

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The overall performance of CMIP6 models in simulating extreme precipitation intensity and frequency is similar to that of CMIP5 models (high confidence), and there is high confidence in the ability of models to capture the large-scale spatial distribution of precipitation extremes over land (Section 11.4.3). Evidence of human influence on extreme precipitation has become stronger since AR5. Considering changes in precipitation intensity averaged over all wet days, there is high confidence that daily mean precipitation intensities have increased since the mid-20th century in a majority of land regions, including Europe, North America and Asia, and it is likely that such an increase is mainly due to anthropogenic emissions of greenhouse gases (Sections 8.3.1.3 and 11.4.4). Section 11.4.4 also finds a larger fraction of land showing enhanced extreme precipitation and a larger probability of record-breaking one-day precipitation than expected by chance, which can only be explained when anthropogenic greenhouse gas forcing is considered. The right-hand

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panel of Cross-Chapter Box 3.2, Figure 1 demonstrates the consistency between changes in global average annual maximum daily precipitation in the observations and model simulations under combined anthropogenic and natural forcing, and inconsistency with simulations under natural forcing alone. While there is more evidence in the literature to quantify the net anthropogenic influence on extreme precipitation than the influence of individual forcing components, a dominant contribution of greenhouse gas forcing to the long-term intensification of extreme precipitation on global and continental scales has recently been quantified separately from the influence of anthropogenic aerosol and natural forcings (Dong et al., 2020; Paik et al., 2020b).

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Observational SST products indicate that the equatorial zonal SST gradient from the western to the eastern equatorial Pacific has strengthened since 1870 (Section 7.4.4.2.1). While CMIP5 historical simulations on average simulate a weakening, large ensemble simulations span the observed strengthening since the 1950s (Watanabe et al., 2021) suggesting an important contribution from internal variability. Coats and Karnauskas (2017) also find that the anthropogenic influence on the SST gradient is yet to emerge out of internal variability even on centennial time scales.

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Trends since the 1980s in in-situ and satellite observations and reanalyses exhibit strengthening of the Pacific Walker circulation and SST gradient (Section 2.3.1.4.1 and Figure 3.16f; L’Heureux et al., 2013; Boisséson et al., 2014; England et al., 2014; Kociuba and Power, 2015; Ma and Zhou, 2016). AMIP simulations reproduce this strengthening (Figure 3.16d; Boisséson et al., 2014; Ma and Zhou, 2016), indicating a dominant role of SST changes. However, all reanalysis trends lie outside the 5–95% range of simulated CMIP6 historical Walker circulation trends over this period (Figure 3.16f), consistent with CMIP5 results (England et al., 2014; Kociuba and Power, 2015). This may be in part caused by the underestimation of the PDV magnitude especially in CMIP5 models (Section 3.7.6; Kociuba and Power, 2015; Chung et al., 2019), but also suggests a potential error in simulating the forced changes of the Walker circulation. Specifically, anthropogenic and volcanic aerosol changes over this period may have driven a strengthening (DiNezio et al., 2013; Takahashi and Watanabe, 2016; Hua et al., 2018). This aerosol influence may be indirect via Atlantic Multi-decadal Variability (AMV; Annex IV.2.7) through inter-basin teleconnections (McGregor et al., 2014; Chikamoto et al., 2016; Kucharski et al., 2016; X. Li et al., 2016a; Ruprich-Robert et al., 2017), which may be underestimated in models due to SST biases in the equatorial Atlantic (Section 3.5.1.2.2; McGregor et al., 2018). Note also the large uncertainty in aerosol influence on the Walker circulation (Kuntz and Schrag, 2016; Hua et al., 2018; Oudar et al., 2018), which is also seen in CMIP6 (Figure 3.16f).

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In instrumental records, global summer monsoon precipitation intensity (measured by summer precipitation averaged over the monsoon domain) decreased from the 1950s to 1980s, followed by an increase (Section 2.3.1.4.2 and Figure 3.17c), arising mainly from variations in Northern Hemispheric land monsoons. A CMIP5 multi-model study by Y. Zhang et al. (2018) found that observed 1951–2004 trends of the global and Northern Hemisphere summer land monsoon precipitation intensity are well captured by historical simulations, and CMIP6 models show similar results for global land summer monsoon precipitation (Figure 3.17c). However, the 1960s peak in the Northern Hemisphere summer monsoon circulation is outside the 5th–95th percentile range of CMIP5 and CMIP6 historical simulations for two out of three reanalyses (Figure 3.17d). Modelling studies show that greenhouse gas increases act to enhance Northern Hemisphere summer monsoon precipitation intensity (Liu et al., 2012; Polson et al., 2014; Chai et al., 2018; L. Zhang et al., 2018b). Since the mid-20th century, however, modelling studies show that this effect was overwhelmed by the influence of anthropogenic aerosols in CMIP5 (Polson et al., 2014; Guo et al., 2015; Y. Zhang et al., 2018; Giannini and Kaplan, 2019) and in CMIP6 (T. Zhou et al., 2020). Weakening of the monsoon circulation and reduction of moisture availability are important in this aerosol influence (T. Zhou et al., 2020). Besides these human influences, the global monsoon is sensitive to internal variability and natural forcing including ENSO and volcanic aerosols on interannual time scales and PDV and AMV on decadal to multi-decadal time scales (Wang et al., 2013, 2018; F. Liu et al., 2016; Jiang and Zhou, 2019; Zuo et al., 2019); though AMV in the 20th century may have been partly driven by aerosols, see Section 3.7.7. Indeed, AMIP simulations better reproduce the observed multi-decadal variations of the global monsoon precipitation and circulation (Figure 3.17c,d). Y. Zhang et al. (2018) find that the multi-model ensemble mean trend of global land monsoon precipitation in historical simulations, dominated by anthropogenic aerosol forcing contributions, emerges out of the 90% range of internally-driven trends in pre-industrial control simulations. However, it should be noted that CMIP5 models tend to under-represent the PDV magnitude (Section 3.7.6), suggesting potential overconfidence in the detection of the forced signal. An observed enhancement in global summer monsoon precipitation since the 1980s is accompanied by an intensification of the Northern Hemisphere summer monsoon circulation (Figure 3.17c,d). These trends appear to be at the extreme of the range of the CMIP6 historical simulation ensemble but are well captured by AMIP simulations (Figure 3.17c,d). While the precipitation increase is consistent with greenhouse gas forcing, the circulation intensification is opposite to the simulated response to greenhouse gas forcing, and these enhancements have been attributed to PDV and AMV (Wang et al., 2013; Kamae et al., 2017).

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In summary, while greenhouse gas increases acted to enhance the global land monsoon precipitation over the 20th century (medium confidence), consistent with projected future enhancement (Section 4.5.1.5), this tendency was overwhelmed by anthropogenic aerosols from the 1950s to the 1980s, which contributed to weakening of global land summer monsoon precipitation intensity for this period (medium confidence). There is medium confidence that the intensification of global monsoon precipitation and Northern Hemisphere summer monsoon circulation since the 1980s is dominated by internal variability. These assessments are supported respectively by multi-model detection and attribution studies which find an important role for anthropogenic aerosols in the weakening trend, and studies that identify a role for AMV and PDV in inducing the Northern Hemisphere summer monsoon circulation enhancement since the 1980s. Supported by multi-model simulations that are qualitatively consistent with proxy evidence, there is high confidence that orbital forcing contributed to higher Northern Hemisphere monsoon precipitation in the mid-Pliocene and mid-Holocene than pre-industrial. While CMIP5 models can capture the domain and precipitation intensity of the global monsoon, biases remain in their regional representations, and they are unsuccessful in quantitatively reproducing changes in paleo reconstructions (high confidence). CMIP6 models reproduce the domain and precipitation intensity of the global monsoon observed over the instrumental period better than CMIP5 models (medium confidence). However, CMIP5 and CMIP6 models fail to fully capture the variations of the Northern Hemisphere summer monsoon circulation (Figure 3.17d), but there is low confidence in this assessment due to a lack of evidence in the literature.

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In summary, there is low confidence that an observed decrease in the frequency of Northern Hemisphere summertime extratropical cyclones is linked to anthropogenic influence. In the Southern Hemisphere, there is high confidence that human influence, in the form of ozone depletion, has contributed to the observed poleward shift of the jet in austral summer, while confidence is low for human influence on historical blocking activity. The low confidence statements are due to the limited number of studies available. The shift of the Southern Hemisphere jet is correlated with modulations of the SAM (Section 3.7.2). There is medium confidence in model performance regarding the simulation of the extratropical jets, storm track and blocking activity, with increased resolution sometimes corresponding to better performance, but important shortcomings remain, particularly for the Euro-Atlantic sector of the Northern Hemisphere. Nonetheless, synthesizing across Sections 3.3.3.1–3.3.3.3, there is high confidence that CMIP6 models capture the general characteristics of the tropospheric large-scale circulation.

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In summary, an anthropogenic influence on the frequency or other aspects of SSWs has not yet been robustly detected. There is low confidence in the ability of models to simulate any such trends over the historical period because of large natural interannual variability and also due to substantial common biases in the simulated mean state affecting the simulated frequency of SSWs.

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The AR5 concluded that ‘anthropogenic forcings are very likely to have contributed to Arctic sea ice loss since 1979’ (Bindoff et al., 2013), based on studies showing that models can reproduce the observed decline only when including anthropogenic forcings, and formal attribution studies. Since the beginning of the modern satellite era in 1979, Northern Hemisphere sea ice extent has exhibited significant declines in all months with the largest reduction in September (see Section 2.3.2.1.1, and Figures 3.20 and 3.21 for more details on observed changes). The recent Arctic sea ice loss during summer is unprecedented since 1850 (high confidence), but as in AR5 and SROCC there remains only medium confidence that the recent reduction is unique during at least the past 1000 years due to sparse observations (Sections 2.3.2.1.1 and 9.3.1.1). CMIP5 models also simulate Northern Hemisphere sea ice loss over the satellite era but with large differences among models (e.g., Massonnet et al., 2012; Stroeve et al., 2012). The envelope of simulated ice loss across model simulations encompasses the observed change, although observations fall near the low end of the CMIP5 and CMIP6 distributions of trends (Figure 3.20). CMIP6 models on average better capture the observed Arctic sea ice decline, albeit with large inter-model spread. Notz et al. (2020) found that CMIP6 models better reproduce the sensitivity of Arctic sea ice area to CO2 emissions and global warming than earlier CMIP models although the models’ underestimation of this sensitivity remains. Davy and Outten (2020) also found that CMIP6 models can simulate the seasonal cycle of Arctic sea ice extent and volume better than CMIP5 models. For the assessment of physical processes associated with changes in Arctic sea ice, see Section 9.3.1.1.

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Since AR5, there have been several new detection and attribution studies on Arctic sea ice. While the attribution literature has mostly used sea ice extent (SIE), it is closely proportional to sea ice area (SIA; Notz, 2014), which is assessed in Chapters 2 and 9 and shown in Figures 3.20 and 3.21. Kirchmeier-Young et al. (2017) compared the observed time series of the September SIE over the period 1979–2012 with those from different large ensemble simulations which provide a robust sampling of internal climate variability (CanESM2, CESM1, and CMIP5) using an optimal fingerprinting technique. They detected anthropogenic signals which were separable from the response to natural forcing due to solar irradiance variations and volcanic aerosol, supporting previous findings (Figure 3.21; Min et al., 2008b; Kay et al., 2011; Notz and Marotzke, 2012; Notz and Stroeve, 2016). Using selected CMIP5 models and three independently derived sets of observations, Mueller et al. (2018) detected fingerprints from greenhouse gases, natural, and other anthropogenic forcings simultaneously in the September Arctic SIE over the period 1953–2012. They further showed that about a quarter of the greenhouse gas induced decrease in SIE has been offset by an increase due to other anthropogenic forcing (mainly aerosols). Similarly, Gagné et al. (2017b) suggested that the observed increase in Arctic sea ice concentration over the 1950–1975 period was primarily due to the cooling contribution of anthropogenic aerosol forcing based on single model simulations. Gagné et al. (2017a) identified a detectable increase in Arctic SIE in response to volcanic eruptions using CMIP5 models and four observational datasets. Polvani et al. (2020) suggested that ozone depleting substances played a substantial role in the Arctic sea ice loss over the 1955–2005 period.

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An important consideration in comparing Arctic sea ice loss in models and observations is the role of internal variability (medium confidence). Using ensemble simulations from a single model, Kay et al. (2011) suggested that internal variability could account for about half of the observed September ice loss. More recently, large ensemble simulations have been performed with many more ensemble members (Kay et al., 2015). These enable a more robust characterization of internal variability in the presence of forced anthropogenic change. Using such large ensembles, some studies discussed the influence of internal variability on Arctic sea ice trends (Swart et al., 2015). Song et al. (2016) also compared the trends in the forced and unforced simulations using multiple climate models and found that internal variability explains about 40% of the observed September sea ice melting trend, supporting previous studies (Stroeve et al., 2012). Based on the large ensembles of CESM1 and CanESM2, the September Arctic sea ice extent variance first increases and then decreases as SIE declines from its pre-industrial value (Kirchmeier-Young et al., 2017; Mueller et al., 2018) consistent with previous work (Goosse et al., 2009), but neither study found a strong sensitivity of detection and attribution results to the change in variability. Further work has indicated that internally-driven summer atmospheric circulation trends with enhanced atmospheric ridges over Greenland and the Arctic Ocean, which project on the negative phase of the North Atlantic Oscillation (Section 3.7.1), play an important role in the observed Arctic sea ice loss (Hanna et al., 2015; Ding et al., 2017). A fingerprint analysis using the CESM large ensemble suggests that this internal variability accounts for 40–50% of the observed September Arctic sea ice decline (Ding et al., 2019; England et al., 2019). Internally-generated decadal tropical variability and associated atmospheric teleconnections were suggested to have contributed to the changing atmospheric circulation in the Arctic and the associated rapid sea ice decline from 2000 to 2014 (Meehl et al., 2018).

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Based on the new attribution studies since AR5, we conclude that it is very likely that anthropogenic forcing mainly due to greenhouse gas increases was the main driver of Arctic sea ice loss since 1979. Increases in anthropogenic aerosols have offset part of the greenhouse gas induced Arctic sea ice loss since the 1950s (medium confidence). Despite large differences in the mean sea ice state in the Arctic, Arctic sea ice loss is captured by all CMIP5 and CMIP6 models. Nonetheless, large inter-model differences in the Arctic sea ice decline remain, limiting our ability to quantify forced changes and internal variability contributions.

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The AR5 noted the strong linear correlation between Northern Hemisphere snow cover extent (SCE) and annual-mean surface air temperature in CMIP5 models. It was assessed as likely that there had been an anthropogenic contribution to observed reductions in Northern Hemisphere snow cover since 1970 (Bindoff et al., 2013). The AR5 assessed that CMIP5 models reproduced key features of observed snow cover well, including the seasonal cycle of snow cover over northern regions of Eurasia and North America, but had more difficulties in more southern regions with intermittent snow cover. The AR5 also found that CMIP5 models underestimated the observed reduction in spring snow cover over this period (Figure 3.22; see also Brutel-Vuilmet et al., 2013; Thackeray et al., 2016; Santolaria-Otín and Zolina, 2020). This behaviour has been linked to how the snow-albedo feedback is represented in models (Thackeray et al., 2018a). The CMIP5 multi-model ensemble has been shown to represent the snow-albedo feedback more realistically than CMIP3, although models from some individual modelling centres have not improved or have even got worse (Thackeray et al., 2018a). There is still a systematic overestimation of the albedo of boreal forest covered by snow (Thackeray et al., 2015; Y. Li et al., 2016). Consequently, the snow albedo feedback might have been overestimated by CMIP5 models (Section 9.5.3; Xiao et al., 2017).

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Several CMIP5 and CMIP6 based studies have consistently attributed the observed Northern Hemisphere spring SCE changes (Section 2.3.2.2) to anthropogenic influences (Rupp et al., 2013; Najafi et al., 2016; Paik and Min, 2020), with the observed changes being found to be inconsistent with natural variability alone. Similarly, spring snow thickness (Snow Water Equivalent) changes on the scale of the Northern Hemisphere have been attributed to greenhouse gas forcing (Jeong et al., 2017). Using individual forcing simulations from multiple CMIP6 models, Paik and Min (2020) detected greenhouse gas influence in the observed decrease of early spring SCE between 1925 and 2019, which was found to be separable from the responses to other forcings.

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In summary, it is very likely that anthropogenic influence contributed to the observed reductions in Northern Hemisphere springtime snow cover since 1950. CMIP6 models better represent the seasonality and geographical distribution of snow cover than CMIP5 simulations (high confidence). Both CMIP5 and CMIP6 models simulate strong declines in spring SCE during recent years, in general agreement with observations, causing the multi-model mean decreasing trend in spring SCE to now better agree with observations than in earlier evaluations. Evidence has yet to emerge that interactions between vegetation and snow, found problematic in CMIP5, have improved in CMIP6 models (Section 9.5.3). Such deficiencies in the representation of snow in climate models mean there is medium confidence in the simulation of snow cover over the northern continents in CMIP6 model simulations. The models consistently link snow extent to surface air temperature (Figure 9.24). With warming of near-surface air linked to anthropogenic influence, and particularly to greenhouse gas increases (Section 3.3.1.1), this provides additional evidence that reductions in snow cover are also caused by human activity.

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Glaciers are defined as perennial surface land ice masses independent of the Antarctic and Greenland Ice Sheets (Sections 9.5 and 2.3.2.3). The AR5 assessed that anthropogenic influence had likely contributed to the retreat of glaciers observed since the 1960s (Bindoff et al., 2013), based on a high level of scientific understanding and robust estimates of observed mass loss, internal variability, and glacier response to climatic drivers. The SROCC (Hock et al., 2019b) concluded that atmospheric warming was very likely the primary driver of glacier recession.

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A regional study considering 85 Northern Hemisphere glacier systems concluded that there is a discernible human influence on glacier mass balance, with glacier model simulations driven by CMIP5 historical and greenhouse gas-only simulations showing a glacier mass loss, whereas those driven by natural-only forced simulations showed a net glacier growth (Hirabayashi et al., 2016). In addition, a study of the role of climate change in glacier retreat using a simple mass-balance model for 37 glaciers worldwide, concluded that observed length changes would not have occurred without anthropogenic climate change, with observed length variations exceeding those associated with internal variability by several standard deviations in many cases (Roe et al., 2017). Roe et al. (2021) used the same model to estimate that at least 85% of cumulative glacier mass loss since 1850 is attributable to anthropogenic influence. While Marzeion et al. (2014) found that anthropogenic influence contributed only 25 ± 35% of glacier mass loss for the period 1851–2010, their naturally-forced simulations exhibited a substantial negative mass balance, which Roe et al. (2021) argued is unrealistic. Moreover, Marzeion et al. (2014) estimated that anthropogenic influence contributed 69 ± 24% of glacier mass loss for the period 1991 to 2010, consistent with a progressively increasing fraction of mass loss attributable to anthropogenic influence found by Roe et al. (2021).

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In summary, considering together the SROCC assessment that atmospheric warming was very likely the primary driver of glacier recession, the results of Roe et al. (2017, 2021) and our assessment of the dominant role of anthropogenic influence in driving atmospheric warming (Section 3.3.1), we conclude that human influence is very likely the main driver of the near-universal retreat of glaciers globally since the 1990s.

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The AR5 assessed that it is likely that anthropogenic forcing contributed to the surface melting of the Greenland Ice Sheet since 1993 (Bindoff et al., 2013). The SROCC did not directly assess the attribution of Greenland Ice Sheet change to anthropogenic forcing, but it did assess with medium confidence that summer melting of the Greenland Ice Sheet has increased to a level unprecedented over at least the last 350 years, which is two-to-fivefold the pre-industrial level (see also Trusel et al., 2018).

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Detection and attribution studies of change in the Greenland Ice Sheet remain challenging (Kjeldsen et al., 2015; Bamber et al., 2019). This is in part due to the short observational record (Shepherd et al., 2012, 2018, 2020; Bamber et al., 2018; Cazenave et al., 2018; Mouginot et al., 2019; Rignot et al., 2019) and the challenges this poses to the evaluation of modelling efforts (Section 9.4.1.2). The latter require not only dynamic ice-sheet models, but also appropriate atmospheric and oceanic conditions to use as a boundary forcing to drive the models (Nowicki and Seroussi, 2018; Barthel et al., 2020). Nonetheless, new literature since AR5 finds that ice-sheet mass balance calculations using reanalysis-driven regional model simulations of surface mass balance are found to agree well with the observed decrease in ice-sheet mass over the past twenty years (Fettweis et al., 2020; Sasgen et al., 2020; Tedesco and Fettweis, 2020), consistent with earlier studies (Flato et al., 2013). These studies also show that the exceptional melt events observed in 2012 and 2019 were associated with exceptional atmospheric conditions (Sasgen et al., 2020; Tedesco and Fettweis, 2020). These results support the finding that increased surface melting is associated with warming, although atmospheric circulation anomalies, including the summer North Atlantic Oscillation (NAO) and variations in snowfall play an important role in driving interannual variations (Section 9.4.1.1; Sasgen et al., 2020; Tedesco and Fettweis, 2020). Further, a coupled ice-sheet-climate model study found emergence of decreased surface mass balance prior to the present day in coastal locations in Greenland, which dominate the integrated surface mass balance (Fyke et al., 2014), suggesting that observed variations in surface mass balance in these regions might be expected to be distinguishable from internal variability. A CMIP6 simulation of the historical period showed stable Greenland surface mass balance up to the 1990s, after which it declined due to increased melt and runoff, consistent with a downscaled reanalysis (van Kampenhout et al., 2020). Further, all experts surveyed in a structured expert judgement exercise examining the causes of the increase in mass loss from the Greenland Ice Sheet over the last two decades (Bamber et al., 2019) concluded that external forcing was responsible for at least 50% of the mass loss. A comparison of Greenland Ice Sheet mass loss trends from observations and AR5 model projections for the period 2007–2017 found that the magnitude of the observed surface mass balance trends was at the top of the AR5 assessed range, while mass loss due to changing ice dynamics was near the centre of the AR5 range (Slater et al., 2020), providing further evidence of consistent anthropogenically-forced mass loss trends in models and observations.

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Drawing together the evidence from the continued and strengthened observed mass loss, the agreement between anthropogenically forced climate simulations and observations, and historical and paleo evidence for the unusualness of the observed rate of surface melting and mass loss, we assess that it is very likely that human influence has contributed to the observed surface melting of the Greenland Ice Sheet over the past two decades, and that there is medium confidence in an anthropogenic contribution to recent overall mass loss from Greenland.

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AR5 assessed that there was low confidence in attributing the causes of the observed mass loss from the Antarctic Ice Sheet since 1993 (Bindoff et al., 2013). The SROCC assessed that there is medium agreement but limited evidence of anthropogenic forcing of Antarctic mass balance through both surface mass balance and glacier dynamics. It further assessed that Antarctic ice loss is dominated by acceleration, retreat and rapid thinning of the major West Antarctic Ice Sheet outlet glaciers (very high confidence), driven by melting of ice shelves by warm ocean waters (high confidence). Based on updated observations, Chapter 2 assesses that there is very high confidence that the Antarctic Ice Sheet lost mass between 1992 and 2017, and that there is medium confidence that this mass loss has accelerated. Models of Antarctic Ice Sheet evolution are evaluated in detail in Section 9.4.2.2, which assesses that there is medium confidence in many ice-sheet processes in Antarctic Ice Sheet models, but low confidence in the ocean forcing affecting basal melt rates. CMIP5 and CMIP6 models perform similarly in their simulation of Antarctic surface mass balance (Section 9.4.2.2, Gorte et al., 2020). Model evaluation of surface mass balance over the Antarctic Ice Sheet, including regional aspects, is also assessed in Atlas.11.1.3.

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Ice discharge around the West Antarctic Ice Sheet is strongly influenced by variability in basal melt (Jenkins et al., 2018; Hoffman et al., 2019), in particular at decadal and longer time scales (Snow et al., 2017). Basal melt rate variability can be induced by wind-driven ocean current changes, which may partly be of anthropogenic origin via greenhouse gas forcing (Holland et al., 2019). Moreover, ice discharge losses from the Antarctic Ice Sheet over the 2007–2017 period are close to the centre of the model-based range projected in AR5 (Slater et al., 2020). However, expert opinion differs as to whether recent Antarctic ice loss from the West Antarctic Ice Sheet has been driven primarily by external forcing or by internal variability, and there is no consensus (Bamber et al., 2019). Anthropogenic influence on the Antarctic surface mass balance, which is expected to partially compensate for ice discharge losses through increases in snowfall, is currently masked by strong natural variability (Previdi and Polvani, 2016; Bodart and Bingham, 2019), and observations suggest that it has been close to zero over recent years (see further discussion in Section 9.4.2.1; Slater et al., 2020).

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Overall, there is medium agreement but limited evidence of anthropogenic influence on Antarctic mass balance through changes in ice discharge.

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The global ocean plays an important role in the climate system, as it is responsible for transporting and storing large amounts of heat (Sections 3.5.1 and 9.2.2.1), freshwater (Sections 3.5.2 and 9.2.2.2) and carbon (Sections 3.6.2 and 5.2.1.3) that are exchanged with the atmosphere. Therefore, accurate ocean simulation in climate models is essential for the realistic representation of the climatic response to anthropogenic warming, including rates of warming, sea level rise and carbon uptake, and the representation of coupled modes of climate variability.

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The ocean plays an important role as the Earth’s primary energy store. The AR5 and SROCC assessed that the ocean accounted for more than 90% of the Earth’s energy change since the 1970s (Rhein et al., 2013; Bindoff et al., 2019). These assessments are consistent with recent studies assessed in Section 7.2 and Cross-Chapter Box 9.1, which find that 91% of the observed change in Earth’s total energy from 1971 to 2018 was stored in the ocean (von Schuckmann et al., 2020). The AR5 concluded that anthropogenic forcing has very likely made a substantial contribution to ocean warming above 700 m, whereas below 700 m, limited measurements restricted the assessment of ocean heat content changes in AR5 and prevented a robust comparison between observations and models (Bindoff et al., 2013).

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The multi-model means of both CMIP5 and CMIP6 historical simulations forced with time varying natural and anthropogenic forcing shows robust increases in ocean heat content in the upper (0–700 m) and intermediate (700–2000 m) ocean (high confidence) (Figure 3.26; Cheng et al., 2016, 2019; Gleckler et al., 2016; Bilbao et al., 2019; Tokarska et al., 2019). Temporary (<10 years) surface and subsurface cooling during and after large volcanic eruptions is also captured in the upper-ocean, and global mean ocean heat content (Balmaseda et al., 2013). The ocean heat content increase is also reflected in the corresponding ocean thermal expansion which is a leading contributor to global mean sea level rise (Sections 3.5.3.2 and 9.2.4, and Box 9.1).

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Since AR5, the attribution of ocean heat content increases to anthropogenic forcing has been further supported by more detection and attribution studies. These studies have shown that contributions from natural forcing alone cannot explain the observed changes in ocean heat content in either the upper or intermediate ocean layers, and a response to anthropogenic forcing is clearly detectable in ocean heat content (Gleckler et al., 2016; Bilbao et al., 2019; Tokarska et al., 2019). Moreover, a response to greenhouse gas forcing is detectable independently of the response to other anthropogenic forcings (Bilbao et al., 2019; Tokarska et al., 2019), which has offset part of the greenhouse gas induced warming. Further evidence is provided by the agreement between observed and simulated changes in global thermal expansion associated with the ocean heat content increase when both natural and anthropogenic forcings are included in the simulations (Section 3.5.3.2), though internal variability plays a larger role in driving basin-scale thermosteric sea level trends (Bilbao et al., 2015). Over the Southern Ocean, warming is detectable over the late 20th century and is largely attributable to greenhouse gases (Swart et al., 2018; Hobbs et al., 2021), while other anthropogenic forcings such as ozone depletion have been shown to mitigate the warming in some of the CMIP5 simulations (Swart et al., 2018; Hobbs et al., 2021). The use of the mean temperature above a fixed isotherm rather than fixed depth further strengthens a robust detection of the anthropogenic response in the upper ocean (Weller et al., 2016), and better accounting for internal variability in the upper ocean (Rathore et al., 2020), helps explain reported hemispheric asymmetry in ocean heat content change (Durack et al., 2014b).

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While ocean assessments have primarily focused on temperature changes, improved observational salinity products since the early 2000s have supported more assessment of long-term ocean salinity change and variability from AR4 (Bindoff et al., 2007) to AR5 across both models and observations (Flato et al., 2013; Rhein et al., 2013). The AR5 assessed that it was very likely that anthropogenic forcings have made a discernible contribution to surface and subsurface ocean salinity changes since the 1960s. The SROCC augmented these insights, noting that observed high latitude freshening and warming have very likely made the surface ocean less dense with a stratification increase of between 2.18% and 2.42% from 1970 to 2017 (Bindoff et al., 2019). A recent observational analysis has expanded on these assessments, suggesting a very marked summertime density contrast enhancement across the mixed layer base of 6.2–11.6% per decade, driven by changes in temperature and salinity, which is more than six times larger than previous estimates (Sallée et al., 2021). An idealized ocean modelling study suggests that the enhanced stratification can account for a third of the salinity enhancement signal since 1990 (Zika et al., 2018). Thus, there has been an expansion of observed global- and basin-scale salinity change assessment literature since AR5, with many new studies reproducing the key patterns of long-term salinity change reported in AR5 (Rhein et al., 2013), and linking these through modelling studies to coincident changes in evaporation–precipitation patterns at the ocean surface (Sections 2.3.1.3, 3.3.2, 8.2.2.1 and 9.2.2).

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AR5 concluded that it was very likely that anthropogenic forcings had made a discernible contribution to surface and subsurface ocean salinity changes since the 1960s (Bindoff et al., 2013; Rhein et al., 2013). It highlighted that the spatial patterns of salinity trends, and the mean fields of salinity and evaporation minus precipitation are all similar, with an enhancement to Atlantic Ocean salinity and freshening in the Pacific and Southern Oceans. Since AR5 all subsequent work on assessing observed and modelled salinity changes has confirmed these results.

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Climate change detection and attribution studies have considered salinity, with the first of these assessed in AR5 (Bindoff et al., 2013). Since that time, the positive detection conclusions (Stott et al., 2008; Pierce et al., 2012; Terray et al., 2012) have been supported by a number of more recent and independent assessments which have reproduced the multi-decadal basin-scale patterns of change in observations and models (Figures 3.27 and 3.28; Durack et al., 2014a; Durack, 2015; Levang and Schmitt, 2015; Skliris et al., 2016). Observed depth-integrated basin responses, contrasting the Pacific and Atlantic basins (freshening Pacific and enhanced salinity Atlantic) were also shown to be replicated in most historical (natural and anthropogenically forced) simulations, with this basin contrast absent in CMIP5 and CMIP6 natural-only simulations that exclude anthropogenic forcing (Durack et al., 2014a; Figure 3.28).

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The SROCC concluded with high confidence that the dominant cause of GMSL rise since 1970 is anthropogenic forcing. Prior to that, AR5 had concluded that it is very likely that there has been a substantial contribution from anthropogenic forcings to GMSL rise since the 1970s. Since AR5, several studies have identified a human contribution to observed sea level change resulting from a warming climate as manifest in thermosteric sea level change and the contribution from melting glaciers and ice sheets.

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For the global mean thermosteric sea level change, Slangen et al. (2014) showed the importance of anthropogenic forcings (combined greenhouse gas and aerosol forcings) for explaining the magnitude of the observed changes between 1957 and 2005, considering the full depth of the ocean and natural forcings in order to capture the variability (see also Figure 3.29). Over the 1950–2005 period, Marcos and Amores (2014) found that human influence explains 87% of the 0–700 m global thermosteric sea level rise. Both thermosteric and regional dynamic patterns of sea level change in individual forcing experiments from CMIP5 were considered by Slangen et al. (2015) who showed that responses to anthropogenic forcings are significantly different from both internal variability and inter-model differences and that although greenhouse gas and anthropogenic aerosol forcings produce opposite GMSL responses, there are differences in the response on regional scales. Based on these studies, we conclude that it is very likely that anthropogenic forcing was the main driver of the observed global mean thermosteric sea level change since 1970.

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In an attribution study of the sea-level contributions of glaciers, Marzeion et al. (2014) found that between 1991 and 2010, the anthropogenic fraction of global glacier mass loss was 69 ± 24% (see also Section 3.4.3.1). Slangen et al. (2016) considered all quantifiable components of the GMSL budget and showed that anthropogenically forced changes account for 69 ± 31% of the observed sea level rise over the period 1970 to 2005, whereas natural forcings combined with internal variability have a much smaller effect – only contributing 9 ± 18% of the change over the same period. These studies indicate that about 70% of the combined change in glaciers, ice-sheet surface mass balance and thermal expansion since 1970 can be attributed to anthropogenic forcing, and that this percentage has increased over the course of the 20th century. Detection studies on GMSL change in the 20th century (Becker et al., 2014; Dangendorf et al., (2015) found that observed total GMSL change in the 20th century was inconsistent with internal variability. Dangendorf et al. (2015) determined that for 1900 to 2011 at least 45% of GMSL change is human-induced. A study that developed a semi-empirical model to link sea-level change to observed GMST change concluded that at least 41% of the 20th century sea-level rise would not have happened in the absence of the century’s increasing GMST and that there was a 95% probability that by 1970 GMSL was higher than that which would have occurred in the absence of increasing GMST (Kopp et al., 2016). Richter et al. (2020) compared modelled sea level change with the satellite altimeter observations from 1993 to 2015; a period short enough that internal variability can dominate the spatial pattern of change. They found that when GMSL is not removed, model simulated zonally averaged sea level trends are consistent with altimeter observations globally as well as in each ocean basin and much larger than might be expected from internal variability. Using spatial correlation, Fasullo and Nerem (2018) showed that the satellite altimeter trend pattern is already detectable.

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We note that current detection and attribution studies do not yet include all processes that are important for sea-level change (see Section 9.6). However, based on the body of literature available, we conclude that the main driver of the observed GMSL rise since at least 1971 is very likely anthropogenic forcing. The assessed period starts in 1971 for consistency with observations assessed in Cross-Chapter Box 9.1.

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Circulation of the ocean, whether it be wind or density driven, plays a prominent role in the heat and freshwater transport of the Earth system (Buckley and Marshall, 2016). Thus, its accurate representation is crucial for the realistic representation of water mass properties, and replication of observed changes driven by atmosphere-land-ocean coupling. Here, we assess the ability of CMIP models to reproduce the observed large-scale ocean circulation, along with assessment of the detection and attribution of any anthropogenically-driven changes. We also note that the process-based understanding of these circulation changes and circulation changes occurring at smaller scales is assessed in Section 9.2.3.

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The AR5 concluded that while climate models suggested that an AMOC slowdown would occur in response to anthropogenic forcing, the short direct observational AMOC record precluded it from being used to support this model finding. Chapter 2 reports with high confidence, a weakening of the AMOC was observed in the mid-2000s to the mid-2010s, while again also noting that the observational record was too short to determine whether this is a significant trend or a manifestation of decadal and multi-decadal variability (Section 2.3.3.4.1). Indirect evidence of AMOC weakening since at least the 1950s is also presented, but confidence in this longer-term decrease was low Section 2.3.3.4.1).

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As reported in Section 2.3.3.4.1, estimates of AMOC since at least 1950, which are generated from observed surface temperatures or sea surface height, suggest the AMOC weakened through the 20th century (low confidence) (Ezer et al., 2013; Caesar et al., 2018). Over the same period, the CMIP5 multi-model mean showed no significant net forced response in AMOC (Cheng et al., 2013). However, a significant forced change is simulated in the CMIP6 multi-model mean, where a clear increase of the AMOC is seen over the 1940–1985 period (Figure 3.30e; Menary et al., 2020). Although there is general agreement that the influence of greenhouse gases acts to a weaken the modelled AMOC (Delworth and Dixon, 2006; Caesar et al., 2018), changes in solar, volcanic and anthropogenic aerosol emissions can lead to temporary changes in AMOC on decadal- to multi-decadal time scales (Delworth and Dixon, 2006; Menary et al., 2013; Menary and Scaife, 2014; Swingedouw et al., 2017; Undorf et al., 2018b). As such, the simulated net forced response in AMOC is a balance between the different forcing factors (Section 9.2.3.1; Delworth and Dixon, 2006; Menary et al., 2020). The differing AMOC response of CMIP5 and CMIP6 models during the historical period has been associated with stronger aerosol effective radiative forcing in the CMIP6 models (Menary et al., 2020), such that the aerosol-induced AMOC increase during the 1940–1985 period overcomes the greenhouse gas induced decline (Figure 3.30e). However, models simulate a range of anthropogenic aerosol effective radiative forcing and a range of historical AMOC trends in CMIP6 (Menary et al., 2020) and there remains considerable uncertainty over the realism of the CMIP6 AMOC response during the 20th century (Figure 3.30d–f) due to disagreement among the differing lines of evidence. For example, ocean reanalysis (Jackson et al., 2019) and forced ocean model simulations (Robson et al., 2012; Danabasoglu et al., 2016), which show AMOC changes that are broadly consistent with the CMIP6 response, appear to disagree with observational estimates of AMOC over the historical period (Ezer et al., 2013; Caesar et al., 2018). It is noted, however, that the relatively short length of the forced ocean simulations and ocean reanalysis precludes a comparable assessment of 20th century trends. Furthermore, despite the similar AMOC evolution seen in forced ocean model simulations and the CMIP6 models, it is unclear whether the same underlying mechanisms are responsible for the changes.

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In summary, models do not support robust assessment of the role of anthropogenic forcing in the observed AMOC weakening between the mid-2000s and the mid-2010s, which is assessed to have occurred with high confidence in Section 2.3.3.4.1, as the changes are outside of the range of modelled AMOC trends (regardless of whether they are forced or internally generated) in most models. Thus, we have low confidence that anthropogenic forcing has influenced the observed changes in AMOC strength in the post-2004 period. In addition, there remains considerable uncertainty over the realism of the CMIP6 AMOC response during the 20th century due to disagreement among the differing lines of observational and modelled evidence (i.e., historical AMOC estimates, ocean reanalysis, forced ocean simulations and historical CMIP6 simulations). Thus, we have low confidence that anthropogenic forcing has had a significant influence on changes in AMOC strength during the 1860–2014 period.

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Proposed causes of the trend in the amplitude of the seasonal cycle of CO2, and its amplification at higher latitudes, include increases in the summer productivity and/or increases in the magnitude of winter respiration of northern ecosystems (Barichivich et al., 2013; Graven et al., 2013; Forkel et al., 2016; Wenzel et al., 2016), increases in productivity throughout the Northern Hemisphere by CO2 fertilization, and increases in the productivity of agricultural crops in northern mid-latitudes (Gray et al., 2014; Zeng et al., 2014). Recent studies have attempted to quantify the different contributions by comparing atmospheric CO2 observations with ensembles of land surface model simulations. Piao et al. (2017) found that CO2 fertilization of photosynthesis is the main driver of the increase in the amplitude of the seasonal cycle of atmospheric CO2 but noted that climate change drives the latitudinal differences in that increase. North of 40°N, Bastos et al. (2019) also found CO2 fertilization to be the most likely driver, with warming at northern high latitudes contributing a decrease in amplitude, in contrast to earlier conclusions (Graven et al., 2013; Forkel et al., 2016), and agricultural and land use changes making only a small contribution. For temperate regions of the Northern Hemisphere, K. Wang et al. (2020) found that the importance of CO2 fertilization is decreased by drought stress, but also found only a small contribution from agricultural and land use changes. However, many global models do not include nitrogen fertilization, changes to crop cultivars or irrigation effects, with the latter associated with deficiencies in simulated terrestrial water cycling (H. Yang et al., 2018). All these factors influence the capability of models to simulate accurately the seasonal cycle in atmosphere-land CO2 exchanges. Model comparisons to the atmospheric CO2 concentration record for Barrow, Alaska, suggest that models underestimate current levels of carbon fixation (Winkler et al., 2019) and have deficiencies in their phenological representation of greenness levels, particularly for autumn (Z. Li et al., 2018). Based on these studies and noting the uncertainty in the processes ultimately driving changes in atmospheric CO2 seasonal cycles (Section 5.2.1.4), we assess with medium confidence that fertilization by anthropogenic increases in atmospheric CO2 concentrations is the main driver of the increase in the amplitude of the seasonal cycle of atmospheric CO2.

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Section 5.2.1.3 assesses that both observational reconstructions based on the partial pressure of CO2 and ocean biogeochemical models show a quasi-linear increase in the ocean sink of anthropogenic CO2 from 1.0 ± 0.3 PgC yr–1 to 2.5 ± 0.6 PgC yr–1 between 1960–1969 and 2010–2019 in response to global CO2 emissions (high confidence). During the 1990s, the global net flux of CO2 into the ocean is estimated to have weakened to 0.8 ± 0.5 PgC yr–1 while in 2000 and thereafter, it is estimated to have strengthened considerably to rates of 2.0 ± 0.5 PgC yr–1, associated with changes in SST, the surface concentration of dissolved inorganic carbon and alkalinity, and decadal variations in atmospheric forcing (Landschützer et al., 2016, see also Section 5.2).

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Ocean acidification is one of the most detectible metrics of environmental change and was well covered in AR5, in which it was assessed that the uptake of anthropogenic CO2 had very likely resulted in acidification of surface waters (Bindoff et al., 2013). Since then, observations and simulations of multi-decadal trends in surface carbon chemistry have increased in robustness. The evidence on ocean pH decline had further strengthened in SROCC with good agreement found between CMIP5 models and observations and an assessment that the ocean was continuing to acidify in response to ongoing carbon uptake (Bindoff et al., 2019). An observed decrease in global surface open ocean pH is assessed in Section 2.3.3.5 to be virtually certain to have occurred with a rate of 0.003–0.026 per decade for the past 40 years. The ocean acidification has occurred not only in the surface layer but also in the interior of the ocean (Sections 2.3.3.5 and 5.3.3). Rates have been observed to be between −0.015 and −0.020 per decade in mode and intermediate waters of the North Atlantic through the combined effect of increased anthropogenic and remineralized carbon (Ríos et al., 2015) and acidification has been observed down to 3000 m in the deep water formation regions (Perez et al., 2018). There has also been considerable improvement in detection and attribution of anthropogenic CO2 versus eutrophication-based acidification in coastal waters (Wallace et al., 2014).

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The increased evidence in recent studies supports an assessment that it is virtually certain that the uptake of anthropogenic CO2 was the main driver of the observed acidification of the global surface open ocean. The observed increase in acidification over the North Atlantic subtropical and equatorial regions since 2000 is likely associated in part with an increase in ocean temperature, a response which corresponds to the expected weakening of the ocean carbon sink with warming. Due to strong internal variability, systematic changes in carbon uptake in response to climate warming have not been observed in most other ocean basins at present. We further assess, consistent with AR5 and SROCC, that deoxygenation in the upper ocean is due in part to anthropogenic forcing, with medium confidence. There is high confidence that Earth system models simulate a realistic time evolution of the global mean ocean carbon sink.

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Conclusions on external forcing influences on the NAM are supported by CMIP6 results based on single forcing ensembles (Figure 3.34a). Positive trends are found in historical simulations over 1958–2019 in boreal winter and are mainly driven by greenhouse gas increases. No significant trends are simulated in response to anthropogenic aerosols, stratospheric ozone or natural forcing. Albeit weak and not statistically significant, the sign of the multi-model mean forced response due to natural forcing is consistent with the observed reduction of solar activity since the 1980s (Section 2.2.1) whose influence would have favoured the negative phase of wintertime NAM/NAO based on the fingerprint of the nearly periodical 11-year solar cycle extracted from models (Scaife et al., 2013; Andrews et al., 2015; Thiéblemont et al., 2015) or observations (Gray et al., 2016; Lüdecke et al., 2020). But such an NAO response to solar forcing remains highly uncertain and controversial, being contradicted by longer proxy records over the last millennium (Sjolte et al., 2018) and modelling evidence (Gillett and Fyfe, 2013; Chiodo et al., 2019). For all seasons and for all individual forcings, uncertainties remain in the estimation of the forced response in the NAM trend as evidenced by considerable model spread (Figure 3.34a) and because the simulated forced component has small amplitude compared to internal variability.

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In summary, CMIP5 and CMIP6 models are skilful in simulating the spatial features and the variance of the NAM/NAO and associated teleconnections (high confidence). There is limited evidence for a significant role for anthropogenic forcings in driving the observed multi-decadal variations of the NAM/NAO from the mid 20th century. Confidence in attribution is low: (i) because there is a large spread in the modelled forced responses which is overwhelmed anyway by internal variability; (ii) because of the apparent signal-to-noise problem; and (iii) because of the chronic inability of models to produce a range of trends which encompasses the observed estimates over the last 60 years.

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In general agreement with AR5, new research continues to indicate that both stratospheric ozone depletion and increasing greenhouse gases have contributed to the trend of the SAM during austral summer toward its positive phase in recent decades (Solomon and Polvani, 2016), with the ozone depletion influence dominating (Gerber and Son, 2014; Son et al., 2018). In CMIP6 historical simulations there are significant positive SAM trends over the 1979–2019 period in austral summer, although the contribution from ozone forcing evaluated with the four available models is not significant (Figure 3.34b). Three of these models share the same standard prescribed ozone forcing and produce significantly positive SAM trends over an extended period (1957–2019). The fourth model, MRI-ESM2-0, has the option of interactive ozone chemistry. Its ozone-only experiment is forced by prescribed ozone derived from its own historical simulations and produces a negative SAM trend associated with weak ozone depletion (Morgenstern et al., 2020). Morgenstern et al. (2014) and Morgenstern (2021) find an indirect influence of greenhouse gases on the SAM via induced ozone changes in coupled chemistry-climate simulations, which differ from the prescribed ozone simulations shown in Figure 3.34b. Since about 1997, the effective abundance of ozone-depleting halogen has been decreasing in the stratosphere (WMO, 2018), leading to a stabilization or even a reversal of stratospheric ozone depletion (Sections 2.2.5.2 and 6.3.2.2). The ozone stabilization and slight recovery since about 2000 may have caused a pause in the summertime SAM trend (Figure 3.34c; Saggioro and Shepherd, 2019; Banerjee et al., 2020), although some influence from internal variability cannot be ruled out. While some studies find an anthropogenic aerosol influence on the summertime SAM (Gillett et al., 2013; Rotstayn, 2013), recent studies with larger multi-model ensembles find that this effect is not robust (Steptoe et al., 2016; Choi et al., 2019), consistent with CMIP6 single forcing ensembles (Figure 3.34). In the CMIP5 simulations, volcanic stratospheric aerosol has a significant weakening effect on the SAM in autumn and winter (Cross-Chapter Box 4.1; Gillett and Fyfe, 2013), but there is no evidence that this effect leads to a significant multi-decadal trend since the late 20th century. Beyond external forcing, Fogt et al. (2017) show a significant association of tropical SST variability with the summertime SAM trend since the mid-20th century in agreement with Lim et al. (2016), who, however, demonstrate that such a teleconnection between the summertime SAM and El Niño–Southern Oscillation (Annex IV.2.3), found in observations, is missing in many CMIP5 models.

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In summary, it is very likely that anthropogenic forcings have contributed to the observed trend of the summer SAM toward its positive phase since the 1970s. This assessment is supported by further model studies that confirm the human influence on the summertime SAM with improved models since AR5. While ozone depletion contributed to the trend from the 1970s to the 1990s (medium confidence), its influence has been small since 2000, leading to a weaker summertime SAM trend over 2000–2019 (medium confidence). Climate models reproduce the spatial structure of the summertime SAM observed since the late 1970s well (high confidence). CMIP6 models reproduce the spatiotemporal features and recent multi-decadal trend of the summertime SAM better than CMIP5 models (medium confidence). However, there is a large spread in the intensity of the SAM response to ozone and greenhouse gas changes in both CMIP5 and CMIP6 models (high confidence), which limits the confidence in the assessment of the ozone contribution to the observed trends. CMIP5 and CMIP6 models do not capture multicentennial variability of the SAM found in proxy reconstructions (low confidence). This confidence level reflects that it is unclear whether this is due to a model or an observational shortcoming.

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To conclude, ENSO representation in CMIP5 models displayed a significant improvement from the representation of ENSO variability in CMIP3 models, which displayed much more intermodel spread in standard deviation, and stronger biennial periodicity (Guilyardi et al., 2012; Flato et al., 2013). In general, there has been no large step change in the representation of ENSO between CMIP5 and CMIP6, however, CMIP6 models appear to better represent some key ENSO characteristics (e.g., Brown et al., 2020; Planton et al., 2021). The instrumental record and paleo-proxy evidence through the Holocene all suggest that ENSO can display considerable modulations in amplitude, pattern and period (see also (Section 2.4.2). For the period since 1850, there is no clear evidence for a sustained shift in ENSO index beyond the range of internal variability. However, paleo-proxy evidence indicates with medium confidence that ENSO variability since 1950 is greater than at any time between 1400 and 1850 (Section 2.4.2). Coupled models display large changes of ENSO behaviour in the absence of external forcing changes, and little-to-no variance sensitivity to historical anthropogenic forcing. Thus, there is low confidence that anthropogenic forcing has led to the changes of ENSO variability inferred from paleo-proxy evidence.

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Chapter 2 reports low confidence that the apparent change from East Pacific- to Central Pacific-type El Niño events that occurred in the last 20–30 years was representative of a long term change. While some climate models do suggest external forcing may affect the El Niño event type, most climate models suggest that what has been observed is well within the range of natural variability. Thus, there is low confidence that anthropogenic forcing has had an influence on the observed changes in El Niño event type.

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The observed Indian Ocean basin-average SST increase on multi-decadal and centennial time scales is well represented by CMIP5 historical simulations, and has been attributed to the effects of greenhouse gases offset in part by the effects of anthropogenic aerosols mainly through aerosol-cloud interactions (Dong and Zhou, 2014; Dong et al., 2014b). The observed SST trend is larger in the western than eastern tropical Indian Ocean, which leads to an apparent upward trend of the IOD index, but this trend is statistically insignificant (Section 2.4.3). CMIP5 models capture this warming pattern, which may be associated with Walker circulation weakening over the Indian Ocean due to greenhouse gas forcing (Dong and Zhou, 2014). However, strong internal decadal IOD-like variability and observational uncertainty preclude attribution (Cai et al., 2013; Han et al., 2014b; Gopika et al., 2020). Such a positive IOD-like change in equatorial zonal SST gradient suggests an increase in the frequency of extreme positive events (Cai et al., 2014) and skewness (Cowan et al., 2015) of the IOD mode. While there is some evidence of an increase in frequency of positive IOD events during the second half of the 20th century, the current level of IOD variability is not unprecedented in a proxy reconstruction for the last millennium (Section 2.4.3; Abram et al., 2020). Besides, the IOD magnitude in the late 20th century is not significantly different between CMIP5 simulations forced by historical and natural-only forcings, though this conclusion is based on only five selected ensemble members that realistically reproduce statistical features of the IOD (Blau and Ha, 2020). While selected CMIP5 models show weakening (Thielke and Mölg, 2019) and seasonality changes (Blau and Ha, 2020) in IOD-induced rainfall anomalies in tropical eastern Africa, no comparison with observational records has been made. Likewise, while a strengthening tendency of the ENSO-IOB mode correlation and resultant intensification of the IOB mode are found in historical or future simulations in selected CMIP5 models (Hu et al., 2014; Tao et al., 2015), such a change has not been detected in observational records.

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After linear detrending, Pacific Decadal Variability (PDV; Annex IV.2.6; Section 3.7.6) has been suggested as a driver of decadal to multi-decadal variations in the IOB mode (Dong et al., 2016). However, correlation between the PDV and a decadal IOB index, defined from linearly detrended SST, changed from positive to negative during the 1980s (Han et al., 2014a). The increase in anthropogenic forcing and recovery from the eruptions of El Chichón in 1982 and Pinatubo in 1991 may have overwhelmed the PDV influence, and explain this change (Dong and McPhaden, 2017; L. Zhang et al., 2018a). However, the low statistical degrees of freedom hamper clear detection of human influence in this correlation change.

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To summarize, there is medium confidence that changes in the interannual IOD variability in the late 20th century inferred from observations and proxy records are within the range of internal variability. There is no evidence of anthropogenic influence on the interannual IOB. On decadal- to multi-decadal time scales, there is low confidence that human influence has caused a reversal of the correlation between PDV and decadal variations in the IOB mode. The low confidence in this assessment is due to the short observational record, limited number of models used for the attribution, lack of model evaluation of the decadal IOB mode, and uncertainty in the contribution from volcanic aerosols. Nevertheless, CMIP5 models have medium overall performance in reproducing both the interannual IOB and IOD modes, with an apparently good performance in reproducing the IOB magnitude arising from compensation of biases in the formation process, and overly high IOD magnitude due to the mean state bias (high confidence). There is no clear improvement in the simulation of the IOD from CMIP5 to CMIP6 models, though there is only medium confidence in this assessment, since only a subset of CMIP6 models have been examined. There is no evidence for performance changes in simulating the IOB from CMIP5 to CMIP6 models.

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(Section 2.4.4 assess that there is low confidence in any sustained changes to the AZM and AMM variability in instrumental observations. Moreover, any attribution of possible human influence on the Atlantic modes and associated teleconnections is limited by the poor fidelity of CMIP5 and CMIP6 models in reproducing the mean tropical Atlantic climate, its seasonality and variability, despite hints of some improvement in CMIP6, as well as other sources of uncertainties related to limited process understanding in the observations (Foltz et al., 2019), the response of the tropical Atlantic climate to anthropogenic aerosol forcing (Booth et al., 2012; Zhang et al., 2013a) and the presence of strong multi-decadal fluctuations related to AMV (Section 3.7.7) and cross-tropical basin interactions (Martín-Rey et al., 2018; Cai et al., 2019). The fact that most models poorly represent the climatology and variability of the tropical Atlantic combined with the short observational record makes it difficult to place the recent observed changes in the context of internal multi-annual variability versus anthropogenic forcing.

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In summary, based on CMIP5 and CMIP6 results, there is no robust evidence that the observed changes in either the Atlantic Niño or AMM modes and associated teleconnections over the second half of the 20th century are beyond the range of internal variability or have been influenced by natural or anthropogenic forcing. Considering the physical processes responsible for model biases in these modes, increasing resolution in both ocean and atmosphere components may be an opportunity for progress in the simulation of the tropical Atlantic changes as evidenced by some individual model studies (Roberts et al., 2018), but this needs confirmation from a multi-model perspective.

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While PDV is primarily understood as an internal mode of variability (Si and Hu, 2017), there are some indications that anthropogenically induced SST changes project onto PDV and have contributed to its past evolution (Bonfils and Santer, 2011; Dong et al., 2014a; Boo et al., 2015; Xu and Hu, 2018). However, the level of evidence is limited because of the difficulty in correctly separating internal versus externally forced components of the observed SST variations, and because it is unclear whether the dynamics of the PDV are operative in this forced SST change pattern. Over the last two to three decades which encompass the period of slower GMST increase (Cross-Chapter Box 3.1), Smith et al. (2016) found that anthropogenic aerosols have driven part of the PDV change toward its negative phase. A similar result is shown in Takahashi and Watanabe (2016) who found intensification of the Pacific Walker circulation in response to aerosol forcing (Section 3.3.3.1.2). Indeed, CMIP6 models simulate a negative PDV trend since the 1980s (Figure 3.39f), which is much weaker than internal variability. However, a response to anthropogenic aerosols is not robustly identified in a large ensemble of a model (Oudar et al., 2018), across CMIP5 models (Hua et al., 2018), or in idealized model simulations (Kuntz and Schrag, 2016). Alternatively, inter-basin teleconnections associated with the warming of the North Atlantic Ocean related to the mid-1990s phase shift of the AMV (McGregor et al., 2014; Chikamoto et al., 2016; Kucharski et al., 2016; X. Li et al., 2016a; Ruprich-Robert et al., 2017), and also warming in the Indian Ocean (Luo et al., 2012; Mochizuki et al., 2016), could have favoured a PDV transition to its negative phase in the 2000s. Considering the possible influence of external forcing on Indian Ocean decadal variability (Section 3.7.4) and AMV (Section 3.7.7), any such human influence on PDV would be indirect through changes in these ocean basins, and then imported to the Pacific via inter-basin coupling. However, this human influence on AMV, and how consistently such inter-basin processes affect PDV phase shifts, are uncertain. Other modelling studies find that anthropogenic aerosols can influence the PDV (Verma et al., 2019; Amiri-Farahani et al., 2020; Dow et al., 2020). It is however unclear whether and how much those forcings contributed to the observed variations of PDV. In CMIP6 models, the temporal correlation of the multi-model ensemble mean PDV index with its observational counterpart is insignificant and negligible (Figure 3.39f), suggesting that any externally-driven component in historical PDV variations was weak. Lastly, the multi-model ensemble mean computed from CMIP6 historical simulations shows slightly stronger variation than the CMIP5 counterpart, suggesting a greater simulated influence from external forcings in CMIP6. Still, the fraction of the forced signal to the total PDV is very low (Figure 3.39f), in contrast to AMV (Section 3.7.7). Consistently, Liguori et al. (2020) estimate that the variance fraction of the externally-driven to total PDV is up to only 15% in a multi-model large ensemble of historical simulations. These findings support an assessment that PDV is mostly driven by internal variability since the pre-industrial era. The sensitivity of ensemble-mean PDV trends to the ensemble size (Oudar et al., 2018), and the dominance of the ensemble spread over the ensemble mean in the 60-year trend of the equatorial Pacific zonal SST gradient in large ensemble simulations (Watanabe et al., 2021), also support this statement.

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AR5 assessed, based on climate models, that the AMV was primarily internally-driven alongside some contribution from external forcings (mainly anthropogenic aerosols) over the late 20th century (Bindoff et al., 2013; Flato et al., 2013). But AR5 also concluded that models show medium performance in reproducing the observed AMV, with difficulties in simulating the time scale, the spatial structure and the coherency between all the physical processes involved (Flato et al., 2013).

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There is additional evidence since AR5 that external forcing has been playing an important role in shaping the timing and intensity of the observed AMV since pre-industrial times (Bellomo et al., 2018; Andrews et al., 2020). The time synchronisation between observed and multi-model mean AMV SST indices is significant in both CMIP5 and CMIP6 historical simulations, while the explained variance of the forced response in CMIP6 appears stronger (Figure 3.40d–f). The competition between greenhouse gas warming and anthropogenic sulphate aerosol cooling has been proposed to be particularly important over the latter half of the 20th century (Booth et al., 2012; Steinman et al., 2015; Murphy et al., 2017; Undorf et al., 2018a; Haustein et al., 2019). The latest observed AMV shift from the cold to the warm phase in the mid-1990s at the surface ocean is well captured in the CMIP6 forced component and may be associated with the lagged response to increased AMOC due to strong anthropogenic aerosol forcing over 1955–1985 (Menary et al., 2020) in combination with the rapid response through surface flux processes to declining aerosol forcing and increasing greenhouse gas influence since then. However, natural forcings may have also played a significant role. For instance, volcanic forcing has been shown to contribute in part to the cold phases of the AMV-related SST anomalies observed in the 20th century (Terray, 2012; Bellucci et al., 2017; Swingedouw et al., 2017; Birkel et al., 2018). Over the last millennium, natural forcings including major volcanic eruptions and fluctuations in solar activity are thought to have driven a larger fraction of the multi-decadal variations in the AMV than in the industrial era, with some interplay with internal processes (Otterå et al., 2010; Knudsen et al., 2014; Moffa-Sánchez et al., 2014; J. Wang et al., 2017; Malik et al., 2018; Mann et al., 2021), but other studies question the role of natural forcings over this period (Zanchettin et al., 2014; Lapointe et al., 2020).

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Model evaluation of the AMV phenomenon remains difficult because of short observational records (especially of detailed process-based observations), the lack of stationarity in the variance, spatial patterns and frequency of the AMV assessed from modelled SST (Qasmi et al., 2017), difficulties in estimating the forced signals in both historical simulations and observations (Tandon and Kushner, 2015), and because of probable interplay between internally and externally-driven processes (Watanabe and Tatebe, 2019). Furthermore, models simulate a large range of historical anthropogenic aerosol forcing (Smith et al., 2020) and questions often referred to as signal-to-noise paradox have been raised concerning the models’ ability to correctly simulate the magnitude of the response of AMV-related atmospheric circulation phenomena, such as the NAO (Section 3.7.1), to both internally and externally generated changes (Scaife and Smith, 2018). Related methodological and epistemological uncertainties also call into question the relevance of the traditional basin-average SST index to assessing the AMV phenomenon (Zanchettin et al., 2014; Frajka-Williams et al., 2017; Haustein et al., 2019; Wills et al., 2019).

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To summarize, results from CMIP5 and CMIP6 models together with new statistical techniques to evaluate the forced component of modelled and observed AMV, provide robust evidence that external forcings have modulated AMV over the historical period. In particular, anthropogenic and volcanic aerosols are thought to have played a role in the timing and intensity of the negative (cold) phase of AMV recorded from the mid-1960s to mid-1990s and subsequent warming (medium confidence). However, there is low confidence in the estimated magnitude of the human influence. The limited level of confidence is primarily explained by difficulties in accurately evaluating model performance in simulating AMV. The evaluation is severely hampered by short instrumental records but also, equally importantly, by the lack of detailed and coherent long-term process-based observations (for example of the AMOC, aerosol optical depth, surface fluxes and cloud changes), which limit our process understanding. In addition, studies often rely solely on simplistic SST indices that may be hard to interpret (Zhang et al., 2016) and may mask critical physical inconsistencies in simulations of the AMV compared to observations (Zhang, 2017).

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The AR5 concluded that human influence on the climate system is clear (IPCC, 2013), based on observed increasing greenhouse gas concentrations in the atmosphere, positive radiative forcing, observed warming, and physical understanding of the climate system. The AR5 also assessed that it was virtually certain that internal variability alone could not account for observed warming since 1951 (Bindoff et al., 2013). Evidence has grown since AR5 that observed changes since the 1950s in many parts of the climate system are attributable to anthropogenic influence. So far, this chapter has focused on examining individual aspects of the climate system in separate sections. The results presented in Sections 3.3 to 3.7 substantially strengthen our assessment of the role of human influence on climate since pre-industrial times. In this section we look across the whole climate system to assess to what extent a physically consistent picture of human induced change emerges across the climate system (Figure 3.41).

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The observed global surface air temperature warming of 0.9°C to 1.2°C in 2010–2019 is much greater than can be explained by internal variability (likely –0.2°C to +0.2°C) or natural forcings (likely –0.1°C to +0.1°C) alone, but consistent with the assessed anthropogenic warming (likely 0.8°C to 1.3°C; Section 3.3.1.1). It is very likely that human influence is the main driver of warming over land Section 3.3.1.1). Moreover, the atmosphere as a whole has warmed (Table 7.1), and it is very likely that human-induced greenhouse gas increases were the main driver of tropospheric warming since 1979 (Section 3.3.1.2). It is virtually certain that greenhouse gas forcing was the main driver of the observed changes in hot and cold extremes over land at the global scale (Cross-Chapter Box 3.2).

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Marzeion, B., J.G. Cogley, K. Richter, and D. Parkes, 2014: Attribution of global glacier mass loss to anthropogenic and natural causes. Science, 345(6199), 919–921, doi: 10.1126/science.1254702.

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Mueller, B.L., N.P. Gillett, A.H. Monahan, and F.W. Zwiers, 2018: Attribution of Arctic Sea Ice Decline from 1953 to 2012 to Influences from Natural, Greenhouse Gas, and Anthropogenic Aerosol Forcing. Journal of Climate, 31(19), 7771–7787, doi: 10.1175/jcli-d-17-0552.1.

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Notz, D. and J. Stroeve, 2016: Observed Arctic sea-ice loss directly follows anthropogenic CO2 emission. Science, 354(6313), 747–750, doi: 10.1126/science.aag2345.

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Oschlies, A. et al., 2017: Patterns of deoxygenation: sensitivity to natural and anthropogenic drivers. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 375(2102), 20160325, doi: 10.1098/rsta.2016.0325.

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Oudar, T., P.J. Kushner, J.C. Fyfe, and M. Sigmond, 2018: No Impact of Anthropogenic Aerosols on Early 21st Century Global Temperature Trends in a Large Initial-Condition Ensemble. Geophysical Research Letters, 45(17), 9245–9252, doi: 10.1029/2018gl078841.

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Paik, S. and S.K. Min, 2020: Quantifying the anthropogenic greenhouse gas contribution to the observed spring snow-cover decline using the CMIP6 multimodel ensemble. Journal of Climate, 33(21), 9261–9269, doi: 10.1175/jcli-d-20-0002.1.

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Paik, S. et al., 2020b: Determining the Anthropogenic Greenhouse Gas Contribution to the Observed Intensification of Extreme Precipitation. Geophysical Research Letters, 47(12), e2019GL086875, doi: 10.1029/ 2019gl086875.

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Park, B.J., Y.H. Kim, S.K. Min, and E.P. Lim, 2018: Anthropogenic and natural contributions to the lengthening of the summer season in the Northern Hemisphere. Journal of Climate, 31(17), 6803–6819, doi: 10.1175/jcli-d-17-0643.1.

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Polson, D., M. Bollasina, G.C. Hegerl, and L.J. Wilcox, 2014: Decreased monsoon precipitation in the Northern Hemisphere due to anthropogenic aerosols. Geophysical Research Letters, 41, 6023–6029, doi: 10.1002/2014gl060811.

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Previdi, M. and L.M. Polvani, 2016: Anthropogenic impact on Antarctic surface mass balance, currently masked by natural variability, to emerge by mid-century. Environmental Research Letters, 11(9), 094001, doi: 10.1088/1748-9326/11/9/094001.

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Roe, G.H., J.E. Christian, and B. Marzeion, 2021: On the attribution of industrial-era glacier mass loss to anthropogenic climate change. The Cryosphere, 15(4), 1889–1905, doi: 10.5194/tc-15-1889-2021.

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Rotstayn, L.D., 2013: Projected effects of declining anthropogenic aerosols on the southern annular mode. Environmental Research Letters, 8(4), 044028, doi: 10.1088/1748-9326/8/4/044028.

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Seong, M.G., S.K. Min, Y.H. Kim, X. Zhang, and Y. Sun, 2021: Anthropogenic greenhouse gas and aerosol contributions to extreme temperature changes during 1951–2015. Journal of Climate, 34(3), 857–870, doi: 10.1175/jcli-d-19-1023.1.

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Slangen, A.B.A. et al., 2016: Anthropogenic forcing dominates global mean sea-level rise since 1970. Nature Climate Change, 6(7), 701–705, doi: 10.1038/nclimate2991.

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Smith, D.M. et al., 2016: Role of volcanic and anthropogenic aerosols in the recent global surface warming slowdown. Nature Climate Change, 6(10), 936–940, doi: 10.1038/nclimate3058.

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Solomon, A. and L.M. Polvani, 2016: Highly Significant Responses to Anthropogenic Forcings of the Midlatitude Jet in the Southern Hemisphere. Journal of Climate, 29(9), 3463–3470, doi: 10.1175/jcli-d-16-0034.1.

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Steptoe, H., L.J. Wilcox, and E.J. Highwood, 2016: Is there a robust effect of anthropogenic aerosols on the Southern Annular Mode?Journal of Geophysical Research: Atmospheres, 121(17), 10029–10042, doi: 10.1002/2015jd024218.

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Stern, D.I. and R.K. Kaufmann, 2014: Anthropogenic and natural causes of climate change. Climatic Change, 122(1–2), 257–269, doi: 10.1007/s10584-013-1007-x.

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Stone, D.A. and G. Hansen, 2016: Rapid systematic assessment of the detection and attribution of regional anthropogenic climate change. Climate Dynamics, 47(5–6), 1399–1415, doi: 10.1007/s00382-015-2909-2.

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Tao, L., Y. Hu, and J. Liu, 2016: Anthropogenic forcing on the Hadley circulation in CMIP5 simulations. Climate Dynamics, 46(9–10), 3337–3350, doi: 10.1007/s00382-015-2772-1.

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Undorf, S. et al., 2018b: Detectable Impact of Local and Remote Anthropogenic Aerosols on the 20th Century Changes of West African and South Asian Monsoon Precipitation. Journal of Geophysical Research: Atmospheres, 123(10), 4871–4889, doi: 10.1029/2017jd027711.

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Vecchi, G.A. et al., 2006: Weakening of tropical Pacific atmospheric circulation due to anthropogenic forcing. Nature, 441(1), 73–76, doi: 10.1038/nature04744.

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Vera, C.S. and L. Díaz, 2015: Anthropogenic influence on summer precipitation trends over South America in CMIP5 models. International Journal of Climatology, 35(10), 3172–3177, doi: 10.1002/joc.4153.

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Weller, E., B.-J. Park, and S.-K. Min, 2020: Anthropogenic and Natural Contributions to the Lengthening of the Southern Hemisphere Summer Season. Journal of Climate, 33(24), 10539–10553, doi: 10.1175/jcli-d-20-0084.1.

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Williams, A.P. et al., 2015: Contribution of anthropogenic warming to California drought during 2012–2014. Geophysical Research Letters, 42(16), 6819–6828, doi: 10.1002/2015gl064924.

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Williams, A.P. et al., 2020: Large contribution from anthropogenic warming to an emerging North American megadrought. Science, 368(6488), 314–318, doi: 10.1126/science.aaz9600.

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Wu, P., N. Christidis, and P. Stott, 2013: Anthropogenic impact on Earth’s hydrological cycle. Nature Climate Change, 3(9), 807–810, doi: 10.1038/nclimate1932.

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Wu, T., A. Hu, F. Gao, J. Zhang, and G.A. Meehl, 2019a: New insights into natural variability and anthropogenic forcing of global/regional climate evolution. npj Climate and Atmospheric Science, 2(1), 18, doi: 10.1038/s41612-019-0075-7.

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Yan, X., T. DelSole, and M.K. Tippett, 2016: What Surface Observations Are Important for Separating the Influences of Anthropogenic Aerosols from Other Forcings?Journal of Climate, 29(11), 4165–4184, doi: 10.1175/jcli-d-15-0667.1.

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Zhou, T. et al., 2020: The dynamic and thermodynamic processes dominating the reduction of global land monsoon precipitation driven by anthropogenic aerosols emission. Science China Earth Sciences, 63(7), 919–933, doi: 10.1007/s11430-019-9613-9.

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Essential input to the simulations assessed here is provided by future scenarios of concentrations or anthropogenic emissions of radiatively active substances; the scenarios represent possible sets of decisions by humanity, without any assessment that one set of decisions is more probable to occur than any other set (Section 1.6). As in previous assessment reports, these scenarios are used for projections of future climate using global atmosphere–ocean general circulation models (AOGCMs) and Earth system models (ESMs; Section 1.5.3); the latter include representation of various biogeochemical cycles such as the carbon cycle, the sulphur cycle, or ozone (e.g., Flato, 2011; Flato et al., 2013). This chapter thus provides a comprehensive assessment of the future global climate response to different future anthropogenic perturbations to the climate system.

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Studies focusing on the relationship of sea ice extent and changes in external drivers have consistently found a much-reduced likelihood of a practically ice-free Arctic Ocean during summer for global warming of 1.5°C than for 2.0°C (Screen and Williamson, 2017; Jahn, 2018; Niederdrenk and Notz, 2018; Notz and Stroeve, 2018; Sigmond et al., 2018; Olson et al., 2019). This is shown here in a large initial-condition ensemble of observationally constrained model simulations where GSAT are stabilized at 1.5°C, 2.0°C and 3.0°C warming relative to 1850–1900 in the RCP8.5 scenario (Figure 4.5). Temperature stabilization is achieved by switching off all the anthropogenic emissions around the time that GSAT first reaches the stabilization thresholds. Simulations have been observationally constrained to correct for a model bias in simulated historical September sea ice extent. In these simulations, Arctic sea ice coverage in September is simulated, on average, to drop below 1 million km2 around 2040, consistent with the AR5 set of assessed models (Sigmond et al., 2018). The individual model simulations, for which there are twenty for each stabilized temperature level, show that the probability of the Arctic becoming practically ice free at the end of the 21st century is significantly higher for 2°C warming than for 1.5°C warming above 1850–1900 levels (high confidence).

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The AR5 concluded with very high confidence that ocean carbon uptake of anthropogenic CO2 will continue under all RCPs through the 21st century, with higher uptake corresponding to higher concentration pathways. The future evolution of the land carbon uptake was assessed to be much more uncertain than for ocean carbon uptake, with a majority of CMIP5 models projecting a continued cumulative carbon uptake.

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We first summarize the assessment of past NAM changes and their attribution from Chapters 2 and 3 to put into context the future projections described here. Strong positive trends for the NAM/NAO indices were observed since 1960, which have weakened since the 1990s (high confidence) (Section 2.4.1.1). The NAO variability in the instrumental record was likely not unusual in the millennial and multi-centennial context (Section 2.4.1.1). Climate models simulate the gross features of the NAM with reasonable fidelity, including its interannual variability, but models tend to systematically underestimate the amount of multi-decadal variability of the NAM and jet stream compared to observations (Section 3.7.1; J. Wang et al., 2017b; Bracegirdle et al., 2018; Simpson et al., 2018), with the caveat of the observational record being relatively short to characterize decadal variability (Chiodo et al., 2019). A realistic simulation of the stratosphere and SST variability in the tropics and northern extratropics are important for a model to realistically capture the observed NAM variability. Despite some evidence from climate model studies that anthropogenic forcings influence the NAM, there is limited evidence for a significant role for anthropogenic forcings in driving the observed multi-decadal variations of the NAM over the instrumental period (Section 3.7.1).

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Significant progress has been made since AR5 in understanding the physical mechanisms responsible for changes in the NAM, although uncertainties remain. It is now clear from the literature that the NAM response, and the closely-related response of the mid-latitude storm tracks, to anthropogenic forcing in CMIP5-era climate models is determined by a ‘tug-of-war’ between two opposing processes (Harvey et al., 2014; Shaw et al., 2016; Screen et al., 2018a): (i) Arctic amplification (Sections 4.5.1.1 and 7.4.4.1), which decreases the low-level meridional temperature gradient, reduces baroclinicity on the poleward flank of the eddy-driven jet, and shifts the storm tracks equatorward and leading to a negative NAM (see Box 10.1; Harvey et al., 2015; Hoskins and Woollings, 2015; Peings et al., 2017; Screen et al., 2018a); and (ii) enhanced warming in the tropical upper-troposphere, due to GHG increases and associated water vapour and lapse rate feedbacks, which increases the upper-level meridional temperature gradient and causes a poleward shift of the storm tracks and a positive NAM (Harvey et al., 2014; Vallis et al., 2015; Shaw, 2019). The large diversity in projected NAM changes in CMIP5 multi-model ensemble (Gillett and Fyfe, 2013) appears to be at least partly explained by the relative importance of these two mechanisms in particular models (Harvey et al., 2014, 2015; Vallis et al., 2015; McCusker et al., 2017; Oudar et al., 2017). Models that produce larger Arctic amplification also tend to produce larger equatorward shifts of the mid-latitude jets and associated negative NAM responses (Barnes and Polvani, 2015; Harvey et al., 2015; Zappa and Shepherd, 2017; McKenna et al., 2018; Screen et al., 2018a; Zappa et al., 2018).

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Another area of progress is new understanding the role of cloud radiative effects in shaping the mid-latitude circulation response to anthropogenic forcing. Through their non-uniform distribution of radiative heating, cloud changes can modify meridional temperature gradients and alter mid-latitude circulation and the annular modes in both hemispheres (Ceppi et al., 2014; Voigt and Shaw, 2015, 2016; Ceppi and Hartmann, 2016; Ceppi and Shepherd, 2017; Lipat et al., 2018; Albern et al., 2019; Voigt et al., 2019). In addition to the effects of changing upper and lower tropospheric temperature gradients on the NAM, progress has been made since AR5 in understanding the effect of simulated changes in the strength of the stratospheric polar vortex on winter NAM projections (Manzini et al., 2014; Zappa and Shepherd, 2017; Simpson et al., 2018).

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The ‘wet get wetter, dry get drier’ paradigm, which has been used to explain the global precipitation pattern responding to global warming (Held and Soden, 2006), might not hold, especially over subtropical land regions (Greve et al., 2014; Feng and Zhang, 2015; Greve and Seneviratne, 2015). Over the tropical oceans, precipitation changes are largely driven by the pattern of SST changes (He et al., 2018), and in the subtropics, precipitation response is driven primarily by the fast adjustment to CO2 forcing (He and Soden, 2017). In addition to the response to GHG forcing, forcing from natural and anthropogenic aerosols exert impacts on regional patterns of precipitation (Section 10.3.1; Ramanathan et al., 2005; Bollasina et al., 2011; Polson et al., 2014; Krishnan et al., 2016; L. Liu et al., 2018; Shawki et al., 2018). The large uncertainties in near-term regional precipitation projections arise due to the interplay between internal variability and anthropogenic external forcing (Endo et al., 2018; Wang et al.,2021). Uncertainties in future aerosol emissions scenarios contribute to uncertainties in regional precipitation projections (Wilcox et al., 2020). Aerosol changes induce a drying in the SH tropical band compensated by wetter conditions in the NH counterpart (Acosta Navarro et al., 2017). The spatially uneven distribution of the aerosol forcing may also induce changes in tropical precipitation caused by shifts in the mean location of the intertropical convergence zone (ITCZ) (Hwang et al., 2013; Ridley et al., 2015; Voigt et al., 2017). Because of the large uncertainty in the aerosol radiative forcing and the dynamical response to the aerosol forcing there is medium confidence in the impacts of aerosols on near-term projected changes in precipitation. Precipitation changes in the near term show seasonal amplification, precipitation increase in the rainy season and decrease in the dry season (Fujita et al., 2019).

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Consistent with AR5, we conclude that projected changes of seasonal mean precipitation in the near term will increase at high latitudes. Near-term projected changes in precipitation are uncertain mainly because of natural internal variability, model uncertainty, and uncertainty in natural and anthropogenic aerosol forcing (medium confidence).

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The AR5 assessed that for RCP8.5, Arctic sea ice coverage in September will drop below 1 million km2, or become practically ice-free, at some point between 2040 and 2060 (Collins et al., 2013). Since AR5, there has been substantial progress in understanding the response of Arctic sea ice to near-term changes in external forcing. In particular, it is very likely that different trajectories of the near-term evolution of anthropogenic forcing cause distinctly different likelihood ranges for very low sea ice coverage to occur over the next two decades (Notz and Stroeve, 2018). For example, there is an unlikely drop of September Arctic sea ice coverage to below 1 million km2 before 2040 for RCP 2.6, and a likely drop of September Arctic sea ice coverage to below 1 million km2 before 2040 for RCP 8.5 (medium confidence given the single study). The much higher likelihood of a practically sea ice free Arctic Ocean during summer before 2040 in RCP8.5 compared to RCP2.6 is consistent with related studies assessed in SROCC that find a substantially increased likelihood of an ice-free Arctic Ocean for 2.0°C compared to 1.5°C mean global warming relative to pre-industrial levels (Screen and Williamson, 2017; Jahn, 2018; Niederdrenk and Notz, 2018; Notz and Stroeve, 2018; Sigmond et al., 2018; Olson et al., 2019).

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On seasonal to interannual time scales, there is new evidence since AR5 that initialized predictions show lower potential predictability for the boreal winter NAO than the correlation skill with respect to observations (Eade et al., 2014; Baker et al., 2018; Scaife and Smith, 2018; Athanasiadis et al., 2020). This has been referred to in the literature as a ‘signal-to-noise paradox’ and means that very large ensembles of predictions are needed to isolate the predictable component of the NAO. While the processes that contribute to the predictability of the winter NAO on seasonal time scales may be distinct from the processes that drive multi-decadal trends, there is emerging evidence that initialized predictions also underrepresent the predictability of the winter NAO on decadal time scales (D.M. Smith et al., 2019). Post-processing and aggregation of initialized predictions may therefore reveal significant skill for predicting the winter NAO on decadal time scales (Smith et al., 2020). Considering these new results since AR5, in the near-term it is likely that any anthropogenic forced signal in the NAM will be of comparable magnitude or smaller than natural internal variability in the NAM (medium confidence).

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An influence of forcing agents other than stratospheric ozone and GHGs, such as anthropogenic aerosols, on SAM changes over the historical period has been reported in some climate models (Rotstayn, 2013), but the response across a larger set of CMIP5 models is not robust (Steptoe et al., 2016) and depends on how tropospheric temperature responds to aerosols (Choi et al., 2019). This gives low confidence in the potential influence of anthropogenic aerosols on the SAM in the future.

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The AR5 assessed that it is very likely that the ENSO will remain the dominant mode of interannual variability in the future but did not specify its change in near term. A subset of CMIP5 models that simulate the ENSO Bjerknes index most realistically show an increase of ENSO SST amplitude in the near-term future and decline thereafter (Kim et al., 2014). However, detection of robust near-term changes of ENSO SST variability in response to anthropogenic forcing is difficult to achieve due to pronounced unforced low-frequency modulations of ENSO (Wittenberg, 2009; Maher et al., 2018; Wengel et al., 2018). Figure 4.10 in Section 4.3.3.2, using CMIP6 models, also shows no robust change in ENSO SST variability in the near term.

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The Atlantic Multi-decadal Variability (AMV) is a large-scale climate mode accounting for the main fluctuations in North Atlantic SST on multi-decadal time scales (Section AIV.2.7). The AMV influences air temperatures and precipitation over adjacent and remote continents, and its undulations can partially explain the observed variations in the NH mean temperatures (Steinman et al., 2015). The origin of this variability is still uncertain. Ocean dynamics (e.g., changes in the AMOC), external forcing, and local atmospheric forcing all seem to play a role (Menary et al., 2015; Ruprich-Robert and Cassou, 2015; Brown et al., 2016; Cassou et al., 2018; Wills et al., 2019). Recent studies have discussed that the ocean dynamics play an active role in generating AMV (Oelsmann et al., 2020) and its interplay with the NAO (Vecchi et al., 2017; R. Zhang et al., 2019; Kim et al., 2020), although natural and anthropogenic external forcing might be crucial in modulating its amplitude and timing (Bellucci et al., 2017; Bellomo et al., 2018; Andrews et al., 2020; Borchertet al., 2021; Mann et al., 2021; see Sections 3.7.7 and AIV.2.7).

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Mitigation of SLCFs affects future climate projections and could alter the time of emergence of anthropogenic climate change signals. The AR5 assessed that emission reductions aimed at decreasing local air pollution could have a near-term warming impact on climate (high confidence) (Kirtman et al., 2013). Because of their shorter lifetimes, reductions in emissions of SLCF species mainly influence near-term GSAT trends (Chalmers et al., 2012; Shindell et al., 2017; Shindell and Smith, 2019), but on decadal time scales the near-term response to even very large reductions in SLCFs may be difficult to detect in the presence of large internal climate variability (Samset et al., 2020). The changes in SLCF emissions during the COVID-19 pandemic has resulted in a small net radiative forcing without a discernible impact on GSAT (Cross-Chapter Box 6.1). SLCF mitigation also leads to a higher GSAT in the mid- to long-term (Smith and Mizrahi, 2013; Stohl et al., 2015; Hienola et al., 2018) and can influence peak warming during the 21st century (Rogelj et al., 2014; Hienola et al., 2018). This section focuses on the total effect of SLCF changes on GSAT projections in the SSP scenarios. A more detailed breakdown of the separate climate effects of SLCF species and precursor species can be found in Sections 6.7.2 and 6.7.3.

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The main uncertainties in climate effects of SLCFs in the future come from: (i) the uncertainty in anthropogenic aerosol ERF (Section 7.3.3); (ii) uncertainty in the relative emissions of different SLCFs that have warming and cooling effects in the current climate (Section 6.2); and (iii) physical uncertainty including the efficacy of the climate response to SLCFs compared to long-lived GHGs (Marvel et al., 2016; Richardson et al., 2019). One example of physical uncertainty is that the shortwave radiative forcing from methane was neglected in previous calculations (Etminan et al., 2016; Collins et al., 2018), which affects understanding of present day and future methane ERF (Modak et al., 2018). Another example of physical uncertainty is projected changes in lightning-NOx production, which contribute to future ozone radiative forcing (Banerjee et al., 2014, 2018; Finney et al., 2018).

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In AR5, uncertainty due to future volcanic activity was not considered in the assessment of the CMIP5 21st century climate projections (Taylor et al., 2012; O’Neill et al., 2016). Since AR5, there has been considerable progress in quantifying the impacts of volcanic eruptions on decadal climate prediction and longer-term climate projections (Meehl et al., 2015; Swingedouw et al., 2015, 2017; Timmreck et al., 2016; Bethke et al., 2017; Illing et al., 2018). By exploring 60 possible volcanic futures under RCP4.5, it has been demonstrated that the inclusion of time-varying volcanic forcing may enhance climate variability on annual-to-decadal time scales (Bethke et al., 2017). Consistent with a tropospheric cooling response, the change in ensemble spread in the volcanic cases is skewed towards lower GSAT relative to the non-volcanic cases (Cross-Chapter Box 4.1, Figure 1). In these simulations with multiple volcanic forcing futures there is: (i) an increase in the frequency of extremely cold individual years; (ii) an increased likelihood of decades with negative GSAT trend (decades with negative GSAT trends become 50% more commonplace); (iii) later anthropogenic signal emergence (the mean time at which the signal of global warming emerges from the noise of natural climate variability is delayed almost everywhere) (high confidence); and (iv) a 10% overall reduction in global land monsoon precipitation and a 20% overall increase in the ensemble spread (Man et al., 2021).

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Most of the projected changes in precipitation exhibit a sharp contrast between land and ocean (Sections 8.2.1 and 8.4.1). Temperature-driven intensification of land-mean precipitation during the 20th century has been masked by fast precipitation responses to anthropogenic sulphate and volcanic forcing (Allen and Ingram, 2002; Richardson et al., 2018a). Based on the Precipitation Driver and Response Model Intercomparison Project (PDRMIP), land-mean precipitation is expected to increase more rapidly with the projected decrease in sulphate forcing and continued warming, contributing to increased global mean precipitation (Table 4.3) and will be clearly observable by the mid-21st century based on RCP4.5 and RCP8.5 scenarios (Richardson et al., 2018a).

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The anthropogenic forced signal in extratropical atmospheric circulation may well be small compared to internal variability (Deser et al., 2012b, 2014) and, as assessed in AR5, there is generallylow agreement across models in many aspects of regional atmospheric circulation change particularly in the NH (Shepherd, 2014). The latter means that, in some regions, a multi-model average perspective of atmospheric circulation change represents a small residual after averaging over large intermodel spread. This is in strong contrast to thermodynamic aspects of climate change, such as surface temperature change, for which model results are generally highly consistent (see, e.g., Figure 4.19). Furthermore, models share systematic biases in some aspects of extratropical atmospheric circulation such as mid-latitude jets, which can have complex implications for understanding forced changes (Simpson and Polvani, 2016). Given these issues, an emerging field of research since AR5 has focused on the development of ‘storylines’ for regional atmospheric circulation change (Shepherd, 2019). The storyline approach is grounded in the identification of a set of physical predictors of atmospheric circulation change, such as those described above (Harvey et al., 2014; Manzini et al., 2014; Shepherd et al., 2018), which act together to determine a specific outcome in the projected atmospheric circulation change. The consequences of multi-model spread in the physical predictors of atmospheric circulation change can be investigated, conditioned on a specified level of global warming (see also Section 1.4.4.2 and Box 10.2; Zappa and Shepherd, 2017; Zappa, 2019; Mindlin et al., 2020).

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The model-simulated, long-term trend of ocean acidification is assessed in Section 4.3.2.5 and Chapter 5 (Section 5.3.4.1). It is virtually certain that surface ocean acidification will continue in response to the rise in atmospheric CO2, and continued penetration of anthropogenic CO2 from the surface to the deep ocean will acidify the ocean interior (Figure 4.29). By the end of this century, under SSP3-7.0, a pH reduction of about 0.3 is found at a few hundred metres depth of the global ocean, with stronger acidification in the interior North Atlantic and the mid- to high-latitude Southern Ocean. At a depth of about 1 km, a pH reduction of about 0.1 is found.

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Projections with CMIP6 ESMs (Kwiatkowski et al., 2020) show a surface pH decline of –0.16 ± 0.002 (±1 standard deviation) under SSP1-2.6 and –0.44 ± 0.005 under SSP5-8.5 from 1870–1899 to 2080–2099. The high-latitude oceans, in particular the Arctic, show greater decline in pH and accelerated acidification (Terhaar et al., 2020). For the same period, model-projected bottom-water pH decline is –0.018 ± 0.001 under SSP1-2.6 and –0.030 ± 0.002 under SSP5-8.5. The projected large-scale surface ocean acidification will be primarily determined by the pathway of atmospheric CO2, with weak dependence on change in climate (high confidence) (Section 5.3.4.1; Hurd et al., 2018). However, for a given atmospheric CO2 scenario, uncertainty in projected ocean acidification increases with ocean depth because of model-simulated differences in ocean circulation that transports anthropogenic CO2 from the surface to bottom ocean (high confidence) (Kwiatkowski et al., 2020). For example, projected surface pH fully separates between SSPs scenarios before 2050, but some overlap across SSPs is still found for projected bottom-water pH in 2080 (Kwiatkowski et al., 2020).

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There is no consensus on changes in amplitude of ENSO SST variability across CMIP iterations. The main factors driving the diversity of ENSO SST amplitude change in climate models are internal variability, SST-mean warming pattern, and model systematic biases. First, pronounced low-frequency modulations of ENSO exist even in unforced control simulations due to internal variability, which leads a large uncertainty in quantifying future ENSO changes (Wittenberg, 2009; Vega-Westhoff and Sriver, 2017; Zheng et al., 2018). Second, ENSO characteristics depend on the climate mean state of the tropical Pacific; however, ENSO can also influence the mean state through non-linear processes (Cai et al., 2015; Timmermann et al., 2018). The response of the tropical Pacific mean state to anthropogenic forcing is characterized by a faster warming on the equator compared to the off-equatorial region, a faster warming of the eastern equatorial Pacific compared to the central tropical Pacific (e.g., El Niño-like mean SST warming, see Section 7.4.4.2), and a weakening of the Walker circulation in most models. Those models with a El Niño-like warming tend to project a strengthening of ENSO SST variability whereas models with a La Niña-like warming tend to project a weakening of variability (Zheng et al., 2016; Kohyama and Hartmann, 2017; J. Wang et al., 2017b; Cai et al., 2018a; Fredriksen et al., 2020). Third, how to take model biases into account leads to different ENSO changes. Kim et al. (2014) suggested that a subset of CMIP5 models that simulate linear ENSO stability realistically exhibit a decrease in ENSO amplitude by the second half of the 21st century. However, an increase of ENSO SST variability has been projected when considering biases in ENSO pattern simulation by different models (Zheng et al., 2016; Cai et al., 2018a). This highlights the importance of constraining tropical Pacific mean state changes in order to enhance confidence in the projected response of ENSO.

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Global warming of 1.5°C implies higher mean temperatures compared to 1850–1900, with generally higher warming over land compared to ocean areas (virtuallycertain) and larger warming in high latitudes compared to low latitudes (Figure 4.31). In addition, global warming of 2°C versus 1.5°C results in robust increases in the mean temperatures in almost all locations, both on land and in the ocean (virtually certain), with subsequent further warming at almost all locations at higher levels of global warming (virtually certain) (Hoegh-Guldberg et al., 2018). For each particular level of global warming, relatively larger mean warming is projected for land regions (virtually certain) (see Figure 4.31; Christensen et al., 2013; Collins et al., 2013; Seneviratne et al., 2016). The projected changes at 1.5°C and 2°C global warming are consistent with observed historical global trends in temperature and their attribution to anthropogenic forcing (Chapter 3), as well as with observed changes under the recent global warming of 0.5°C (Schleussner et al., 2017; Hoegh-Guldberg et al., 2018). That is, spatial patterns of temperature changes associated with the 0.5°C difference in GMST warming between 1991–2010 and 1960–1979 (Schleussner et al., 2017; Hoegh-Guldberg et al., 2018) are consistent with projected changes under 1.5°C and 2°C of global warming.

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Most strong-mitigation scenarios assume – in addition to emissions reductions – some form of carbon dioxide removal (CDR). Anthropogenic activities that remove CO2 from the atmosphere and durably store it in geological, terrestrial, or ocean reservoirs, or in products (see Glossary). The SR1.5 (Rogelj et al., 2018b) assessed that all pathways that limit warming to 1.5°C by 2100 with no or limited overshoot use CDR. In the SSP class of scenarios, SSP1-1.9 is characterized by a rapid decline of net CO2 emissions to zero by 2050 and net negative CO2 emissions in the second half of this century (O’Neill et al., 2016; Rogelj et al., 2018a), implying the use of CDR. The term ‘net CO2 emissions’ refers to the difference between anthropogenic CO2 emissions and removal by CDR options, and ‘net negative CO2 emissions’ imply a scenario where CO2 removal exceeds emissions (van Vuuren et al., 2011, 2016). The terms ‘negative emissions’ and ‘net negative emissions’ refer to and include all GHGs (see Glossary).

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Deployment of CDR will lead to a reduction in atmospheric CO2 levels only if uptake by sinks exceeds net CO2 emissions. Hence, there could be a substantial delay between the initiation of CDR and net CO2 emissions turning negative (van Vuuren et al., 2016), and the time to reach net negative CO2 emissions and the evolution of atmospheric CO2 and climate thereafter would depend on the combined pathways of anthropogenic CO2 emissions, CDR, and natural sinks. The cooling (or avoided warming) due to CDR would be proportional to the cumulative amount of CO2 removed from the atmosphere by CDR (Tokarska and Zickfeld, 2015; Zickfeld et al., 2016), as implied by the near-linear relationship between cumulative carbon emissions and GSAT change (Section 5.5).

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Emissions pathways that limit globally averaged warming to 1.5°C or 2°C by the year 2100 assume the use of CDR approaches in combination with emissions reductions to follow net negative CO2 emissions trajectory in the second half of this century. For instance, in SR1.5, all analysed pathways limiting warming to 1.5°C by 2100 with no or limited overshoot include the use of CDR to some extent to offset anthropogenic CO2 emissions and the median of CO2 removal across all scenarios was 730 GtCO2 in the 21st century (Rickels et al., 2018; Rogelj et al., 2018b). Affordable and environmentally and socially acceptable CDR options at scale well before 2050 are an important element of 1.5°C-consistent pathways especially in overshoot scenarios (de Coninck et al., 2018). The required scale of removal by CDR can vary from 1–2 GtCO2 yr–1year from 2050 onwards to as much as 20 GtCO2 yr–1 (Waisman et al., 2019). In the SSP class of scenarios, net CO2 emissions turn negative from around 2050 in SSP1-1.9 and around 2070 in SSP1-2.6 and in the overshoot scenario SSP5-3.4-OS (O’Neill et al., 2016). Thus, CDR would play a pivotal role in limiting climate warming to 1.5°C or 2°C (Minx et al., 2018). In stark contrast, however, two extensive reviews (Lawrence et al., 2018; Nemet et al., 2018) conclude that it is implausible that any CDR technique can be implemented at the scale needed by 2050.

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For the same amount of global mean cooling achieved, the pattern of climate response would depend on SRM characteristics (Niemeier et al., 2013; Duan et al., 2018; Muri et al., 2018). This is illustrated in Figure 4.38 for temperature and precipitation change relative to a high-CO2 world for scenarios of CO2 reduction, solar irradiance reduction, SAI, and MCB. The pattern differences for different methods are much larger for precipitation than for temperature. The pattern of climate change resulting from SRM is also different from that resulting from CO2 reduction (Figure 4.38). It is virtually certain that SRM approaches would not be able to precisely offset the GHG-induced anthropogenic climate change at global and regional scales.

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There is large uncertainty in the stratospheric response to SAI, and the change in stratospheric dynamics and chemistry would depend on the amount, size, type, location, and timing of injection. There is high confidence that aerosol-induced stratospheric heating will play an important role in surface climate change (Simpson et al., 2019b) by altering the effective radiative forcing (Krishnamohan et al., 2019), lower stratosphere stability (Ferraro and Griffiths, 2016), quasi-biennial oscillation (QBO) (Aquila et al., 2014; Niemeier and Schmidt, 2017; Kleinschmitt et al., 2018), polar vortexes (Visioni et al., 2020a), and North Atlantic Oscillation (Jones et al., 2021). Model simulations indicate stronger polar jets and weaker storm tracks and a poleward shift of the tropospheric mid-latitude jets in response to stratospheric sulphate injections in the tropics (Ferraro et al., 2015; Richter Jadwiga et al., 2018), as the meridional temperature gradient is increased in the lower stratosphere by the aerosol-induced heating. The aerosol-induced warming would also offset some of the GHG-induced stratospheric cooling. Compared to equatorial injection, off-equatorial injection is likely to result in reduced change in stratospheric heating, circulation, and QBO (Richter Jadwiga et al., 2018; Kravitz et al., 2019). Stratospheric ozone response to sulphate injection is uncertain depending on the amount, altitude, and location of injection (WMO, 2018). It is likely that sulphate injection would cause a reduction in polar column ozone concentration and delay the recovery of Antarctic ozone hole (Pitari et al., 2014; Richter Jadwiga et al., 2018; Tilmes et al., 2018b), which would have implications for UV radiation and surface ozone (Pitari et al., 2014; Xia et al., 2017; Richter Jadwiga et al., 2018; Tilmes et al., 2018b). Injection of non-sulphate aerosols is likely to result in less stratospheric heating and ozone loss (Pope et al., 2012; Weisenstein et al., 2015; Keith et al., 2016). One side effect of SAI is increased sulphate deposition at surface. A recent modelling study indicates that to maintain global temperature at 2020 levels under RCP 8.5, increased sulphate deposition from stratospheric sulphate injection could be globally balanced by the projected decrease in tropospheric anthropogenic SO2 emissions, but the spatial distribution of sulphate deposition would move from low to high latitudes (Visioni et al., 2020c).

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Marine cloud brightening (MCB) involves injecting small aerosols such as sea salt into the base of marine stratocumulus clouds where the aerosols act as cloud condensation nuclei (CCN). In the absence of other changes, an increase in CCN would produce higher cloud droplet number concentration with reduced droplet sizes, increasing cloud albedo. Increased droplet concentration may also increase cloud water content and optical thickness, but recent studies suggest that liquid water path response to anthropogenic aerosols is weak due to the competing effects of suppressed precipitation and enhanced cloud water evaporation (Toll et al., 2019). An analogue for MCB are reflective, persistent ‘ship tracks’ observed after the passage of a sea-going vessel emitting combustion aerosols into susceptible clouds (Christensen and Stephens, 2011; Chen et al., 2012; Gryspeerdt et al., 2019). A recent study (Diamond et al., 2020) found a substantial increase in cloud reflectivity from shipping in south-east Atlantic basin, suggesting that a regional-scale test of MCB in stratocumulus‐dominated regions could be successful.

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The zero emissions commitment (ZEC) is the climate change commitment that would result, in terms of projected GSAT, from setting carbon dioxide (CO2) emissions to zero. It is determined by both inertia in physical climate system components (ocean, cryosphere, land surface) and carbon cycle inertia (see Annex VII). In its widest sense it refers to emissions of all compounds including greenhouses gases, aerosols and their pre-cursors. A specific sub-category of zero emissions commitment is the zero CO2 emissions commitment, which refers to the climate system response to a cessation of anthropogenic CO2 emissions excluding the impact of non-CO2 forcers. Assessment of remaining carbon budgets requires an assessment of zero CO2 emissions commitment as well as of the transient climate response to cumulative carbon emissions (TCRE; Section 5.5.2).

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Bollasina, M.A., Y. Ming, and V. Ramaswamy, 2011: Anthropogenic Aerosols and the Weakening of the South Asian Summer Monsoon. Science, 334(6055), 502–505, doi: 10.1126/science.1204994.

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Collins, W.D., D.R. Feldman, C. Kuo, and N.H. Nguyen, 2018: Large regional shortwave forcing by anthropogenic methane informed by Jovian observations. Science Advances, 4(9), 1–10, doi: 10.1126/sciadv.aas9593.

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Fischer, H. et al., 2018: Palaeoclimate constraints on the impact of 2°C anthropogenic warming and beyond. Nature Geoscience, 11, 474–485, doi: 10.1038/s41561-018-0146-0.

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Frölicher, T.L. et al., 2015: Dominance of the Southern Ocean in Anthropogenic Carbon and Heat Uptake in CMIP5 Models. Journal of Climate, 28(2), 862–886, doi: 10.1175/jcli-d-14-00117.1.

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Harrington, L.J. et al., 2016: Poorest countries experience earlier anthropogenic emergence of daily temperature extremes. Environmental Research Letters, 11(5), 055007, doi: 10.1088/1748-9326/11/5/055007.

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Hwang, Y.-T., D.M.W. Frierson, and S.M. Kang, 2013: Anthropogenic sulfate aerosol and the southward shift of tropical precipitation in the late 20th century. Geophysical Research Letters, 40(11), 2845–2850, doi: 10.1002/grl.50502.

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Lehner, F., C. Deser, and L. Terray, 2017: Toward a new estimate of “Time of Emergence” of anthropogenic warming: Insights from dynamical adjustment and a large initial-condition model ensemble. Journal of Climate, 30(19), 7739–7756, doi: 10.1175/jcli-d-16-0792.1.

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Polson, D., M. Bollasina, G.C. Hegerl, and L.J. Wilcox, 2014: Decreased monsoon precipitation in the Northern Hemisphere due to anthropogenic aerosols. Geophysical Research Letters, 41(16), 6023–6029, doi: 10.1002/2014gl060811.

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Ramanathan, V. and Y. Feng, 2008: On avoiding dangerous anthropogenic interference with the climate system: Formidable challenges ahead. Proceedings of the National Academy of Sciences, 105(38), 14245–14250, doi: 10.1073/pnas.0803838105.

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Robock, A., D.G. MacMartin, R. Duren, and M.W. Christensen, 2013: Studying geoengineering with natural and anthropogenic analogs. Climatic Change, 121(3), 445–458, doi: 10.1007/s10584-013-0777-5.

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Regional climate change refers to a change in climate in a given region (Section 10.1.2.1) identified by changes in the mean or higher moments of the probability distribution of a climate variable and persisting for a few decades or longer. It can also refer to a change in temporal properties such as persistence and frequency of occurrence of weather and climate extreme events. Regional climate change may be caused by natural internal processes such as atmospheric internal variability and local climate response to low-frequency modes of climate variability (Technical Annex IV), as well as by changes in external forcings such as modulations of the solar cycle, orbital forcing, volcanic eruptions, and persistent anthropogenic changes in the composition of the atmosphere or in land use and land cover (Cross-Chapter Box 3.2; IPCC, 2018a), in addition to the interactions and feedbacks between them. Process interaction in space is pervasive, which means that small spatial scales often have an influence on the larger scales (Palmer, 2013; Sandu et al., 2016). Depending on the context, a region may refer to a large area such as a monsoon region, but may also be confined to smaller areas such as coastlines, mountain ranges or human settlements like cities. Users (understood as anyone incorporating climate information into their activity) often request climate information for these range of scales since their operating and adaptation decision scales range from the local to the sub-continental level.

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Given the many types of regional climates, the broad range of spatial and temporal scales (Section 10.1.2), and the diversity of user needs, a variety of methodologies and approaches have been developed to construct regional climate change information. The sources include global and regional climate model simulations, statistical downscaling and bias adjustment methods. A commonly used source is long-term (end-of-century) model projections of regional climate change, as well as near-term (next 10 years) climate predictions (Kushnir et al., 2019; Rössler et al., 2019a). Regional observations, with their associated challenges, are a key source for the regional climate information construction process (Q. Li et al., 2020). High-quality observations that enable monitoring of the regional aspects of climate are used to adjust inherent model biases and are the basis for assessing model performance. Process understanding and attribution of observed changes to large- and regional-scale anthropogenic and natural drivers and forcings are also important sources.

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The chapter (Figure 10.2) starts with an introduction of the concepts used in the distillation of regional climate information (Section 10.1). Section 10.2 addresses the aspects associated with the access to and use of observations, while different modelling approaches are introduced and assessed in Section 10.3. Section 10.3 also addresses the performance of models in simulating relevant climate characteristics as needed to estimate the credibility of future projections. Section 10.4 assesses the interplay between anthropogenic causes and internal variability at regional scales, and its relevance for the attribution of regional climate changes and the emergence of regional climate change signals. Section 10.5 tackles the issue of how regional climate information is distilled from different sources taking into account the context and the values of both the producer and the user. Section 10.6 illustrates the distillation approach using three comprehensive examples. Finally, Section 10.7 lists some limitations to the assessment of regional climate information.

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Variability in regional climate arises from natural and anthropogenic forcings, internal variability including the local expression of large-scale remote drivers (also known as teleconnections), and the feedbacks between them. Due to the many possible drivers of variability and change (Figure 10.3), quantifying the interplay between internal modes of decadal variability and any externally forced component is crucial in attempts to attribute causes of regional climate changes (e.g., Hoell et al., 2017; Nath et al., 2018). A regional climate signal could arise purely due to some anthropogenic influence or conversely, entirely due to internal variability, but it is most likely the result of a combination of both (Section 10.4). This section briefly introduces these sources of regional variability and should be read along with corresponding sections in Chapters 3, 6 and 7. Section 10.3 assesses their representation in climate models, Section 10.4 discusses their relevance for the attribution of multi-decadal trends and Section 10.6 refers to them as sources in specific examples where regional climate information is built. Section 8.2 offers a companion discussion focussing on changes in the water cycle. An example of how changes in one region could act as a source for changes in a neighbouring one is assessed in the Cross-Chapter Box 10.1 for the linkages between polar and mid-latitude regions, an interaction that has led to substantial recent research. This section also introduces the sources of uncertainty in model-derived regional climate information and how the quantification of the uncertainties influences the confidence of the regional climate information.

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Both natural and anthropogenic aerosols are often emitted at a regional scale, have a short atmospheric lifetime (from a few hours to several days; Section 6.1), are dispersed regionally and affect climate at a regional scale through radiative cooling/heating and cloud microphysical effects (Chapter 8; Rotstayn et al., 2015; Sherwood et al., 2015). The majority of aerosols scatter solar radiation, but with strong regional variations (Shindell and Faluvegi, 2009) that lead to regional radiative effects of up to two orders of magnitude larger than the global average (B. Li et al., 2016; K. Li et al., 2016; Mallet et al., 2016). Black carbon, instead, is known to absorb solar radiation, leading to regional atmospheric warming patterns due to its inhomogeneous spatial distribution (Gustafsson and Ramanathan, 2016). Patterns of forcing generally follow those of aerosol burden. However, temperature and precipitation responses are both local and remote (Z. Li et al., 2016; Kasoar et al., 2018; L. Liu et al., 2018; Samset et al., 2018; Thornhill et al., 2018; Westervelt et al., 2018). For instance, changes in aerosol concentrations in the NH have been reported to modulate monsoon precipitation in West Africa and the Sahel (Undorf et al., 2018; Section 10.4.2.1) and in Asia (H. Zhang et al., 2018; Section 10.6.3).

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Anthropogenic aerosols play a key role in climate change (Chapter 6). Although the global mean optical depth caused by anthropogenic aerosols did not change from 1975 to 2005 (Chapter 6), the regional pattern changed dramatically between Europe and eastern Asia (Fiedler et al., 2017, 2019; Stevens et al., 2017). Large regional differences in present-day aerosol forcing exist with consequences for regional temperature, hydrological cycle and modes of variability (Chapter 8, Section 10.6). Examples of regions with a notable role for anthropogenic aerosol forcing are the Indian monsoon region (Section 10.6.3) and the Mediterranean basin Section 10.6.4). Anthropogenic aerosols are also very relevant in many urban areas (Box 10.3; Gao et al., 2016; Kajino et al., 2017).

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Atmospheric modes of variability may have seasonally-dependent regional effects like the North Atlantic Oscillation (NAO) in European winter (Tsanis and Tapoglou, 2019) and summer (Bladé et al., 2012; Dong et al., 2013). Even though these modes are internal to the climate system, their variability can be affected by anthropogenic forcings. For instance, the SAM (Hendon et al., 2014) is both internally driven (Smith and Polvani, 2017), but also affected by recent stratospheric ozone changes (Bandoro et al., 2014). The teleconnections between these modes of variability and surface weather often exhibit considerable non-stationarity (Hertig et al., 2015).

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Due to the large ocean heat capacity and their long temporal scales, multi-annual to multi-decadal modes of ocean variability such as the Pacific Decadal Variability (PDV; Newman et al., 2016) and the Atlantic Multi-decadal Variability (AMV; Buckley and Marshall, 2016) are key drivers of regional climate change. In the case of the AMV both natural (volcanic) and anthropogenic (aerosol) external forcings are thought to be involved in its timing and intensity (Section 3.7.7). These modes not only affect nearby regions but also remote parts of the globe through atmospheric teleconnections (Meehl et al., 2013; Dong and Dai, 2015) and can act to modulate the influence of natural and anthropogenic forcings (Davini et al., 2015; Ghosh et al., 2017; Ménégoz et al., 2018b). The dynamics of the ocean modes is simultaneously affected by other modes of variability spanning the full range of spatial and temporal scales due to non-linear interactions (Figure 10.3; Kucharski et al., 2010; Dong et al., 2018). This mutual interdependence can result in changing characteristics of the connection over time (Gallant et al., 2013; Brands, 2017; Dong and McPhaden, 2017), and of their regional climate impact (Martín-Gómez and Barreiro, 2016, 2017). As with atmospheric modes of variability, the regional influence of ocean modes of variability on regional climates can be seasonally dependent (Haarsma et al., 2015).

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The SROCC (IPCC, 2019b) stated that observations and models for assessing changes in the ocean and the cryosphere have been developed considerably during the past century but observations in some key regions remain under-sampled and were very short relative to the time scales of natural variability and anthropogenic changes. Retreat of mountain glaciers and thawing of mountain permafrost continues and will continue due to significant warming in those regions, where it is likely to exceed global temperature increase.

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The SROCC assessed that it is virtually certain that Antarctica and Greenland have lost mass over the past decade and observed glacier mass loss over the last decades is attributable to anthropogenic climate change (high confidence). It is virtually certain that projected warming will result in continued loss in Arctic sea ice in summer, but there is low confidence in climate model projections of Antarctic sea ice change because of model biases and disagreement with observed trends. Knowledge and observations of the polar regions were sparse compared to many other regions, due to remoteness and challenges of operating in them.

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The Arctic has very likely warmed more than twice the global rate over the past 50 years with the greatest increase during the cold season (Atlas.11.2). Several mechanisms are responsible for the enhanced lower troposphere warming of the Arctic, including ice albedo, lapse rate, Planck and cloud feedbacks (Section 7.4.4.1). The rapid Arctic warming strongly affects the ocean, atmosphere, and cryosphere in that region (Section 2.3.2.1 and Atlas.11.2). Averaged over the decade 2010–2019, monthly average sea ice area in August, September and October has been about 25% smaller than during 1979–1988 (high confidence) (Section 9.3.1.1). It is very likely that anthropogenic forcings mainly due to greenhouse gas increases have contributed substantially to Arctic sea ice loss since 1979, explaining at least half of the observed long-term decrease in summer sea ice extent (Section 3.4.1.1).

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Some recent regional climate changes can only be simulated by climate models if anthropogenic aerosols are correctly included (Sections 10.4.2.1, 10.6.3 and 10.6.4; Chapters 6 and 8). Examples of the importance of correctly representing anthropogenic aerosols are the recent enhanced warming over Europe (Nabat et al., 2014; Dong et al., 2017), the cooling over the East Asian monsoon region, leading to a weakening of the monsoon (Section 8.3.2.4; Song et al., 2014; Q. Wang et al., 2017), as well as changes in the monsoons of West Africa (Sections 8.3.2.4 and 10.4.2.1) and South Asia (Sections 8.3.2.4 and 10.6.3; Undorf et al., 2018). The relevance of appropriately representing anthropogenic aerosols has been widely studied in regional models (Boé et al., 2020a; Gutiérrez et al., 2020), with an advantage for models with interactive aerosol schemes (Drugé et al., 2019; Nabat et al., 2020). Without a fully coupled chemistry module, radiative forcing can be simulated by including simple models of sulphate chemistry or specifying the optical properties from observations and prescribing the effect of aerosols on the cloud-droplet number (Fiedler et al., 2017, 2019; Stevens et al., 2017). In all cases, the specification of the aerosol load limits the trustworthiness of the simulations at the regional scale when enough detail is not provided (Samset et al., 2019; Shonk et al., 2020; Z. Wang et al., 2021).

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RCM experiments are often set up such that changes in forcing agents are included only via the boundary conditions, but not explicitly included inside the domain. Jerez et al. (2018) demonstrated that not including time-varying GHG concentrations within the RCM domain may misrepresent temperature trends by 1–2°C per century. Including the past trend in anthropogenic sulphate aerosols in reanalysis-driven RCM simulations substantially improved the representation of recent brightening and warming trends in Europe (Nabat et al., 2014; see Sections 10.3.3.6 and 10.6.4, and Atlas.8.4). Similarly, Bukovsky (2012) argued that RCMs may not capture observed summer temperature trends in the USA because changes in land cover are not taken into account. Barlage et al. (2015) have revealed that including the behaviour of groundwater in land schemes increases the performance of an RCM model to represent climate variability in the central USA. Hamdi et al. (2014) found that an RCM that did not incorporate the historical urbanization in the land-use, land-cover scheme is not able to reproduce the warming trend observed in urban stations, with a larger bias for the minimum temperature trend.

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Analysis of multi-model archives such as CMIP or CORDEX simulation results cannot easily disentangle model uncertainty and uncertainty related to internal variability. Since AR5, the development of single-model (global model and/or RCM) initial-condition large ensembles (SMILEs) has emerged as a promising way to robustly assess the regional-scale forced response to external forcings and the respective contribution of internal variability and model uncertainty to future regional climate changes (Section 4.2.5; Deser et al., 2014, 2020; Kay et al., 2015; Sigmond and Fyfe, 2016; Aalbers et al., 2018; Bengtsson and Hodges, 2019; Dai and Bloecker, 2019; Leduc et al., 2019; Maher et al., 2019; von Trentini et al., 2019; Lehner et al., 2020). The recent development of a multi-model archive of SMILE simulations facilitates the quantification and comparison of the influence of internal variability on global model-based regional climate projections between different models (Deser et al., 2020; Lehner et al., 2020). Another related development is the more frequent use of observation-based statistical models to assess the influence of internal variability on regional-scale global and regional model projections (Thompson et al., 2015; Salazar et al., 2016). However, these methods often implicitly assume that regional-scale internal variability does not change under anthropogenic forcing, which is a strong assumption that does not seem to hold at regional and local scales (LaJoie and DelSole, 2016; Pendergrass et al., 2017; W. Cai et al., 2018; Dai and Bloecker, 2019; Mankin et al., 2020; Milinski et al., 2020).

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The use of SMILEs assumes that they have a realistic representation of internal variability and its evolution under anthropogenic climate change (Eade et al., 2014; McKinnon et al., 2017; McKinnon and Deser, 2018; Chen and Brissette, 2019). Assessing the realism of simulated internal variability for past and current climates remains an active research field with a number of issues such as the shortness and uncertainties of the observed record, in particular in data-scarce regions (Section 10.2.2.3), the signal-to-noise paradox (Section 4.4.3.1; Scaife and Smith, 2018), uncertainty in past observed external forcing estimates (Chapters 2, 6 and 7) and the limitations of assumptions underlying the statistical methods used to derive observational large ensembles (McKinnon et al., 2017; McKinnon and Deser, 2018; Castruccio et al., 2019). Calibration methods inspired by weather and seasonal forecasts can be used to improve the reliability of regional-scale climate projections from large ensembles (Brunner et al., 2019; O’Reilly et al., 2020). Interestingly, reliability is improved when the calibration is performed separately for the dynamical and residual components of the ensemble resulting from dynamical adjustment (Section 10.4.1; O’Reilly et al., 2020).

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This section focuses on the assessment of the methodologies used to identify the physical causes of past and future regional climate change in the context of the ongoing anthropogenic influence on the global climate. The main foci are the attribution of past regional-scale changes (Sections 10.4.1–2) and the robustness and future emergence of the regional-scale response to anthropogenic forcing (Section 10.4.3).

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In this chapter, regional-scale attribution is defined as the process of evaluating the relative contributions of multiple causal factors (or drivers) to regional climate change (Cross-Working Group Box: Attribution in Chapter 1; Rosenzweig and Neofotis, 2013; Shepherd, 2019). Attribution at regional scale builds upon the usual definition of attribution used in the AR5 (Cross-Working Group Box: Attribution in Chapter 1; Hegerl et al., 2010). However, in contrast with global-scale attribution methods where internal variability might be considered as a noise problem (Section 3.2), the preliminary detection step is not always required to perform regional-scale attribution since causal factors of regional climate change may also include internal modes of variability in addition to external natural and anthropogenic forcing. Importantly, regional-scale (or process-based) attribution also seeks to determine the physical processes and uncertainties involved in the causal factor’s influence (Cross-Working Group Box: Attribution in Chapter 1).

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(Section 10.4.1 describes regional-scale attribution methodologies and assesses their application to regional changes of temperature and precipitation. Section 10.4.2 presents three illustrative attribution examples that illustrate a number of specific regional-scale challenges and methodological aspects. Section 10.4.3 focuses on methodologies used to assess the robustness and emergence of the regional climate response to anthropogenic forcing. A basic description of future regional climate change for all regions considered in the report (as defined in Section 1.4.5) appears in the Atlas.

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Optimal fingerprint regression-based methods have been applied to detection and attribution of mean temperature anthropogenic signal in several regions of the world such as Canada, India, central Asia, northern and western China, Australia, and North Africa (Xu et al., 2015; C. Li et al., 2017; Dileepkumar et al., 2018; Y. Wang et al., 2018; Peng et al., 2019; Wan et al., 2019). The influence of anthropogenic forcing, and in particular that of greenhouse gases (GHGs), is robustly detected in annual and seasonal mean temperatures for all considered regions. Most of the observed regional temperature changes since the mid-twentieth century can only be explained by external forcings, with anthropogenic influence being the dominant factor. GHG increase is found to be the primary factor of the anthropogenic-induced warming while the aerosol forcing leads to a cooling offsetting a fraction of the GHG change (C. Li et al., 2016, 2017). While the influence of external natural forcing can often be detected as well, its contribution to observed changes is usually much smaller (C. Li et al., 2017; Wan et al., 2019). Temperature detection results are found to be robust to the use of different observational datasets and detection methodologies (Dileepkumar et al., 2018).

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Detection of mean precipitation changes caused by human influence is much more difficult, due to a larger role of internal variability at regional to local scales, as well as substantial modelling and observational uncertainty (Wan et al., 2015; Sarojini et al., 2016; C. Li et al., 2017). However, multi-decadal precipitation changes due to anthropogenic forcing have been detected for several regions. Ma et al. (2017b) show that anthropogenic forcing has strongly contributed to the observed shift of China daily precipitation towards heavy precipitation. The observed weakening of the East Asia summer monsoon, also known as the southern flooding and northern drought pattern has been partially linked to anthropogenic forcing (Section 8.3.2.4.2; Song et al., 2014; Zhou et al., 2017; Tian et al., 2018). Changes in GHGs lead to increasing precipitation over southern China, while changes in anthropogenic aerosols over East Asia are the dominant factors determining drought conditions over northern China (Song et al., 2014; Tian et al., 2018). Based on all-forcing and single-forcing simulation ensembles with a high-resolution model, Delworth and Zeng (2014) found that the observed long-term regional austral autumn and winter rainfall decline over southern and particularly south-west Australia is partially reproduced in response to anthropogenic changes in GHGs and ozone in the atmosphere, whereas anthropogenic aerosols do not contribute to the simulated precipitation decline. In contrast, the observed increase of north-west Australian summer rainfall since 1950 has been partially attributed to anthropogenic aerosol based on CMIP5 detection and attribution single-forcing simulations (Section 8.3.2.4.6; Dey et al., 2019a, b).

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Time scale separation methods such as the low-frequency component analysis and ensemble empirical mode decomposition methods take advantage of the longer time scale associated with anthropogenic external forcing compared to that of most internal modes of variability. The low-frequency component analysis method tries to find low-frequency variability patterns by searching for linear combinations of a moderate number of empirical orthogonal functions that maximize the ratio of low-frequency to total variance. It has first been used to separate internal modes of interannual and decadal variability from slowly varying and externally-forced variability in the Pacific and Atlantic oceans (Wills et al., 2018, 2019). The methodology has also been applied to patterns of observed surface air temperature to isolate the slow components of observed changes that are consistent with the expected response to anthropogenic greenhouse gas and aerosol forcing (Wills et al., 2020).

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The ensemble empirical mode decomposition method (Wu and Huang, 2009; Wilcox et al., 2013; Ji et al., 2014; Qian and Zhou, 2014) decomposes data, such as time series of historical temperature and precipitation, into independent oscillatory modes of decreasing frequency. The last step of the method leaves behind a smooth and low-frequency residual time series. Typically, the non-linear anthropogenic trend (e.g., of 20th-century temperature) can be reconstructed by summing the long-term mean, the residual, and eventually the lowest-frequency mode to account for a multi-decadal forced signal, for instance associated with anthropogenic aerosol forcing. The ensemble empirical mode decomposition method is an example of a data-driven, non-parametric approach that can be used to directly provide an estimate of the forced response without the need for model data (Qian, 2016).

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The univariate detection method does not use spatial pattern information, but compares observed trends in gridded datasets with distributions of trends from ensembles of simulations during the historical period (Knutson et al., 2013; Knutson and Zeng, 2018). The trends arising from simulations constrained by natural forcing-only and all-forcing are compared with distributions of trends purely due to internal variability and derived from long simulations with constant pre-industrial external forcing. Consistency between observed and simulated historical trends is also assessed with statistical tests that can be applied independently over a large number of grid points. The fraction of area over a given region where the change is classified as detectable, attributable, or consistent/inconsistent, is then finally estimated. The method can be viewed as a simple consistency test for both amplitude and pattern of observed versus simulated trends. Its application to CMIP3 and CMIP5 models suggests that 80% of the Earth’s surface has a detectable anthropogenic warming signal (Knutson et al., 2013). Regarding regional land precipitation changes over the 1901–2010 and 1951–2010 periods, application of the univariate detection method based on CMIP5 models suggests attributable anthropogenic changes at several locations such as increases over regions of the north-central USA, southern Canada, Europe, and southern South America and decreases over parts of the Mediterranean region, northern tropical Africa and south-western Australia (Delworth and Zeng, 2014; Knutson and Zeng, 2018).

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The Sahel, fed by the West African monsoon, has experienced severe decadal rainfall variations (Figure 10.11a). Abundant rainfall in the 1950s–1960s was followed by a large negative trend (Figure 10.11b) until at least the 1980s, over which annual rainfall fell by 20–30% (Hulme, 2001). The subsequent partial recovery (B. Wang et al., 2021) is more uncertain: rain-gauge studies suggest a return to long-term positive anomalies in the western Sahel in the early 2000s (Panthou et al., 2018), while CHIRPS merged satellite/gauge data show a wetter western Sahel since 1981 (Bichet and Diedhiou, 2018a, b). The recovery has been more significant over the central rather than the western Sahel (Lebel and Ali, 2009; Maidment et al., 2015; Sanogo et al., 2015) and a multiple-gauge record supports a greater recovery to the eastern side (Nicholson et al., 2018). In this attribution example, drivers of the long-term drought and subsequent partial recovery are discussed, including anthropogenic GHG and aerosol emissions, and sea surface temperature (SST) variations that, in part, relate to internal variability. The reader is also referred to assessment in Section 8.3.2.4. We define the Sahel within 10°N–20°N across to 30°E, consistent with the eastern boundary used in Chapter 8, and the rainy season as spanning June to September.

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In terms of anthropogenic emissions, regional aerosol emissions from Europe, and to a lesser extent from Asia, have been shown in a global model to weaken Sahel precipitation either through a weakened Saharan heat low or via the Walker circulation (Dong et al., 2014). Greenhouse gases (GHGs) and anthropogenic aerosol can be considered together to control ITCZ position based on temperature asymmetry at the hemispheric scale. GHGs increase Sahel precipitation, while aerosol reduces it (in coupled slab-ocean model experiments by Ackerley et al. (2011) following Biasutti and Giannini (2006)). This effect is stronger when models account for aerosol–cloud interactions (Allen et al., 2015). Perturbed physics GCM ensembles suggests that aerosol emissions were the main driver of observed drying over 1950–1980 (Ackerley et al., 2011), supported by CMIP5 single-forcing experiments (Polson et al., 2014). A coherent drying signal in CMIP5 over the extended 1901–2010 period has also been found, although smaller than the observed trend (Knutson and Zeng, 2018). By applying aerosol scaling factors to the historical period in order to sample the uncertainty in CMIP5 aerosol radiative forcing, Shonk et al. (2020) found differences of 0.5 mm day–1 for Gulf of Guinea rainfall between strong and weak aerosol experiments as illustrated in Figure 10.11c, although the drying appears further south than observed due to model bias.

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For the partial recovery in West African monsoon and Sahel rainfall since the late 1980s, a detection study using three reanalyses (Cook and Vizy, 2015) shows a connection to increasing Saharan temperatures at a rate two to four times greater than the tropical mean, also confirmed by multiple observational and satellite-based data (Zhou and Wang, 2016; Vizy and Cook, 2017) and the review of Cook and Vizy (2019). Reanalyses are also noted to significantly underestimate the Saharan warming (Zhou and Wang, 2016). Saharan warming causes a stronger thermal low and more intense monsoon flow, providing more moisture to the central and eastern Sahel, supported by CMIP5 models (Lavaysse et al., 2016), although not all models capture the observed rainfall–heat–low relationship. Sahel rainfall is also incorrectly located in prototype versions of a few CMIP6 models, related to tropospheric temperature biases (Martin et al., 2017). Amplified Saharan warming has increased the wind shear, leading to a tripling of extreme storms since 1982, which may partially explain the recovery (Taylor et al., 2017). Instead, observations, multiple models and SST-sensitivity experiments with AGCMs have suggested that stronger Mediterranean Sea evaporation enhances low-level moisture convergence to the Sahel, increasing rainfall (Park et al., 2016). Meanwhile, an AGCM study suggested that GHGs alone (in the absence of SST warming) could cause Sahel rainfall recovery, with an additional role for anthropogenic aerosol (Dong and Sutton, 2015); recent changes in North Atlantic SSTs, although substantial, did not exert a significant impact on the recovery. Large spread in the recovery in a five-member AGCM ensemble suggests that atmospheric internal variability cannot be discounted (Roehrig et al., 2013).

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Consistent timing of the southward ITCZ shift during the decline period in CMIP3 and CMIP5 historical simulations supports the role of external forcing, chiefly anthropogenic aerosol (Hwang et al., 2013). The evolution of the observed decline and recovery is largely followed by the CMIP5 multi-model mean, further supporting the role of external drivers (Giannini and Kaplan, 2019). Updated results from CMIP6 for historical simulations with all and single forcings are represented in Figure 10.11d,e showing smaller trends than those observed. Giannini and Kaplan (2019) attempted to unify the driving mechanisms for decline and recovery based on singular-value decomposition of observed and modelled SSTs. Since the 1950s, tropical warming arising from GHGs and North Atlantic cooling from aerosol led to regional stabilization, suppressing Sahel rainfall. The subsequent reduction in aerosol emissions then led to North Atlantic warming and recovery of Sahel rainfall. Such mechanisms continue into the near-term future in idealized and modified RCP experiments, with scenarios featuring more aggressive reductions in aerosol emissions, or including aerosol–cloud interactions, favouring a greater northward shift of rainfall (Allen, 2015; Westervelt et al., 2017, 2018; Scannell et al., 2019). There is paleoclimate evidence of changes to Sahel rainfall in the past, in particular with enhancement of the West African monsoon during the mid-Holocene. However, the mechanisms governing such a change have been shown to be largely dynamical in nature (D’Agostino et al., 2019), suggesting that the mid-Holocene cannot be used to inform the credibility of changes due to greenhouse warming.

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There is very high confidence (robust evidence and high agreement) that patterns of 20th-century ocean and land surface temperature variability have caused the Sahel drought and subsequent recovery by adjusting meridional gradients. There is high confidence (robust evidence and medium agreement) that the changing temperature gradients that perturb the West African monsoon and Sahel rainfall are themselves driven by anthropogenic emissions: warming by GHG emissions was initially restricted to the tropics but suppressed in the North Atlantic due to nearby emissions of sulphate aerosols, leading to a reduction in rainfall. The North Atlantic subsequently warmed following the reduction of aerosol emissions, leading to rainfall recovery.

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The positive trend of precipitation has also been attributed to anthropogenic GHGemissions and stratospheric ozone depletion. CMIP5 models only show a positive trend when including anthropogenic forcings (Vera and Díaz, 2015). These results were supported by Knutson and Zeng (2018) based on univariate detection/attribution analysis of annual mean trends for the 1901–2010 and 1951–2010 periods. However, the main features of summer mean precipitation and variability of South America are still not well-represented in all CMIP5 and CMIP6 models (Gulizia and Camilloni, 2015; Díaz and Vera, 2017; Díaz et al., 2021). This motivates the construction of ensembles that exclude the worst performing models (Section 10.3.3.4). The construction of ensembles of CMIP5 historical simulations with realistic representation of precipitation anomalies with opposite sign over south-eastern South America and eastern Brazil showed that the trend since the 1950s could be related to changes in precipitation characteristics only when simulations included anthropogenic forcings (Díaz and Vera, 2017). GHG emissions have been related to increased precipitation in south-eastern South America through three different mechanisms (Figure 10.12a). First, GHG warming induces a non-zonally uniform pattern of SST warming that includes a warming pattern over the Indian and Pacific oceans that excites wave responses over South America (Junquas et al., 2013). Zonally uniform SST patterns of warming alone lead to precipitation signals opposite to those observed in an AGCM (Junquas et al., 2013). Second, GHG radiative forcing drives an expansion of the Hadley cell so that its descending branch moves poleward from the region, generating anomalous ascending motion and precipitation (H. Zhang et al., 2016; Saurral et al., 2019). The third mechanism by which increased GHG can contribute to increased precipitation in the region is through a delay of the stratospheric polar vortex breakdown. As depicted in Figure 10.12a, both stratospheric ozone depletion and increased GHGs have contributed to the later breakdown of the polar vortex in recent decades (McLandress et al., 2010; Wu and Polvani, 2017; Ceppi and Shepherd, 2019). Mindlin et al. (2020) developed future atmospheric circulation storylines (Section 10.3.4.2, Box 10.2) for Southern Hemisphere mid-latitudes with the CMIP5 models and found that for south-eastern South America summer precipitation, increases are related to the late-spring breakdown of the stratospheric polar vortex. The connecting mechanism is through a lagged southward shift of the jet stream (Saggioro and Shepherd, 2019), which enhances cyclonic activity over the region (Wu and Polvani, 2017).

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There is high confidence that South-Eastern South America summer precipitation has increased since the beginning of the 20th century. Since AR5, science has advanced in the identification of the drivers of the precipitation increase in South-Eastern South America since 1950, including GHG through various mechanisms, stratospheric ozone depletion and Pacific and Atlantic variability. There is high confidence that anthropogenic forcing has contributed to the South-Eastern South America summer precipitation increase since 1950, butvery low confidence on the relative contribution of each driver to the precipitation increase.

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It has also been suggested that the ocean-controlled influence is limited and internal atmospheric variability has to be invoked to fully explain the observed history of drought on decadal time scales (Seager and Hoerling, 2014; Seager and Ting, 2017). From roughly 1980 to the present, the regional climate signals show an interesting mix between forced and internal variability. Lehner et al. (2018) used a dynamical adjustment method and large ensembles of coupled and SST-forced atmospheric experiments to suggest that the observed south-western North America rainfall decline mainly results from the effects of atmospheric internal variability, which is in part driven by a PDV-related phase shift in Pacific SST around 2000 (Figure 10.13b,c). Based upon four SMILEs (three using a GCM and another one an AGCM constrained by observed SSTs) and a CMIP6 multi-model suite constrained by observed external forcings, Figure 10.13 shows, in agreement with Lehner et al. (2018), that observed SSTs with their associated atmospheric response are the main drivers of the south-western North America precipitation decrease during the 1983–2014 period. Once aspects of the internal variability are removed by dynamical adjustment, the observed precipitation change signal and simulated anthropogenically-forced components look more similar (Lehner et al., 2018).

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Importantly, as the AR6 assessment views the PDV as being mostly driven by internal variability (Section 3.7.6), the lines of evidence cited above suggest that the contribution of natural and anthropogenic forcings to the precipitation decline has a small amplitude. Unlike the precipitation deficit, the accompanying south-western North America warming is driven primarily by anthropogenic forcing from GHGs rather than atmospheric circulation variability and may help to enhance the drought through increased evapotranspiration (Knutson et al., 2013; Diffenbaugh et al., 2015; Williams et al., 2015, Williams et al., 2020; Lehner et al., 2018, 2020).

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To conclude, there is high confidence (robust evidence and medium agreement) that most (>50%) of the anomalous atmospheric circulation that caused the south-western North America negative precipitation trend can be attributed to teleconnections arising from tropical Pacific SST variations related to PDV. There is high confidence (robust evidence and medium agreement) that anthropogenic forcing has made a substantial contribution (about 50%) to the south-western North America warming since 1980.

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The robustness of regional-scale attribution differs strongly between temperature and precipitation changes. While the influence of anthropogenic forcing on regional temperature long-term change has been detected and attributed in almost all land regions, a robust detection and attribution of human influence on regional precipitation change has not yet fully occurred for many land regions (Section 10.4.3). Although the contribution of anthropogenic forcing to long-term regional precipitation change has been detected in some regions, a robust quantification of the contributions of different drivers remains elusive. The delayed emergence of the anthropogenic precipitation fingerprint with respect to temperature is likely due to the opposing sign of the fast and slow land precipitation forced responses and time-dependent SST change patterns (Sections 8.2.1 and Section 10.4.3), stronger internal variability (Section 10.3.4.3) as well as larger observational uncertainty (Section 10.2) and impact of model biases. The contribution of internal variability to the observed changes can also be very sensitive to the period length and level of spatial aggregation for the region under scrutiny (Section 4.4.1 and Cross-Chapter Box 3.1; Kumar et al., 2016). Finally, even in the case of temperature changes at multi-decadal time scale, internal variability can still be a substantial driver of regional changes due to cancellation between different external forcings (Nath et al., 2018).

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To conclude, it is virtually certain (robust evidence and high agreement) that anthropogenic forcing has been a major driver of temperature change since 1950 in many sub-continental regions of the world. There is high confidence (robust evidence and medium agreement) that anthropogenic forcing has contributed to multi-decadal mean precipitation changes in several regions, for example western Africa, south-east South America, south-western Australia, northern central Eurasia, and South and East Asia. However, at regional scale, the role of internal variability is stronger while uncertainties in observations, models and external forcing are all larger than at the global scale, precluding a robust assessment of the magnitude of the relative contributions of greenhouse gases, including stratospheric ozone, and different aerosol species.

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Regional climate projections are one key element of the multiple lines of evidence that are used for climate risk assessments as well as for adaptation and policy decisions at regional scales (Sections 10.3.3.9 and 10.5). Regional climate projections can be separated into two components: the regional-scale forced response or regional-scale climate sensitivity when normalized by the global mean temperature change (Seneviratne and Hauser, 2020) and the climate internal variability characterizing the future period or global warming level under scrutiny. This section assesses a few methodological aspects related to robustness and emergence properties of the regional-scale forced response as well as the possible influence of internal variability on the emergence of the anthropogenic signal.

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Figure 10.14 illustrates a similar comparison based on the CMIP6 multi-model ensemble forced with the scenario SSP5-8.5 and applied to two large-scale continental areas. The forced response to anthropogenic forcing is simply taken as the CMIP6 multi-model mean of future regional climate change relative to the 1850–1900 reference period. Robustness of the forced response is based on both significance of the change and model agreement about the sign (direction) of change (Cross-Chapter Box Atlas.1; Figure 10.14). Caution has to be exercised against a too literal interpretation of lack of robust change given that significance and sign agreement can be sensitive to spatial and temporal aggregation (Cross-Chapter Box Atlas.1, Figure 2) and lack of a robust change does not necessarily translate to lack of regional-scale climate change impacts (McSweeney and Jones, 2013; Hibino and Takayabu, 2016).

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There is high confidence that the time-evolving contribution of different mechanisms operating at different time scales can modify the amplitude of the regional-scale response of temperature, and both the amplitude and sign of the regional-scale response of precipitation, to anthropogenic forcing. These mechanisms include non-linear temperature, precipitation and soil-moisture feedbacks, and slow and fast response of SST patterns and atmospheric circulation changes to increasing GHGs.

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This section provides an assessment of the different approaches used in emergence studies as well as sensitivities to methodological choices. The section then focuses on the possible influence of internal variability on future emergence of the simulated mean precipitation anthropogenic signal at regional scales with some illustrative examples.

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In climate science, emergence or distinguishability of a signal refers to the appearance of a persistent change in the probability distribution and/or temporal properties of a climate variable compared with that of a reference period (Section 1.4.2; Giorgi and Bi, 2009; Mahlstein et al., 2011, 2012; Hawkins and Sutton, 2012). Similar to anthropogenic climate change detection (Cross-Working Group Box: Attribution in Chapter 1), signal emergence can be detected, at least initially, without identifying the physical causes of the emergence (Section 1.4.2). In the context of human influence on climate, the objective of emergence studies is the search for the appearance of a signal characterizing an anthropogenically-forced change relatively to the climate variability of a reference period, defined as the noise.

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Precise definitions of signal and noise as well as a metric to measure the relative importance of the signal are key ingredients of the emergence framework and depend on the framing question. In particular, emergence study results can depend on the specific definitions of signal and noise such as the level of spatial and temporal aggregation (McSweeney and Jones, 2013). For instance, grid-point scale emergence will likely be delayed compared with region-average emergence (Section 11.2.4 and Cross-Chapter Box Atlas.1, Figure 2; Fischer et al., 2013; Maraun, 2013b; Lehner et al., 2017a). The signal is often estimated by a running mean multi-decadal average or probability distribution function of the physical variable under scrutiny in order to avoid false emergence due to manifestation of multi-decadal internal variability (King et al., 2015). In the case of extremes such as climate records, a notion of multi-year persistence or recurrence can also be used to fully characterize the anthropogenic signal and its emergence (Christiansen, 2013; Bador et al., 2016).

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Since AR5, the development and production of SMILEs (Sections 4.2.5 and 10.3.4.3) has allowed the assessment of the influence of internal variability on anthropogenic signal emergence. The influence of internal variability, and specifically of the unforced atmospheric circulation, on temperature signal emergence can delay or advance the time of emergence by a decade or two in mid- to high-latitude regions (Lehner et al., 2017a; Koenigk et al., 2020). Internal variability can also result in small or decreasing decadal to multi-decadal heatwave frequency trends under the historical anthropogenic forcing over most regions, thereby delaying emergence of unprecedented heatwave frequency trends relative to the pre-industrial trend distribution (Sections 11.2–11.3; Perkins-Kirkpatrick et al., 2017).

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There is high confidence that consistency in definitions of signal and noise, choice of the reference period and signal-to-noise threshold, is important to robustly assess the future emergence of anthropogenic signals across different types or generations of models, as well as comparing past emergence results between observations and models. There is high confidence that internal variability can delay the emergence of the regional-scale mean precipitation anthropogenic signal in many regions, mainly located in the tropics, subtropics and mid-latitudes. An accurate estimation of the delay in regional-scale emergence caused by internal variability remains challenging due to global model biases in their representation of internal variability as well as methodological difficulties to precisely estimate these biases (high confidence).

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Multiple, sometimes contrasting, lines of evidence are derived from the various data sources, methodologies and approaches that can be used to construct climate information (Section 10.5 and Figure 10.1). Such data sources and methodologies include theoretical understanding of relevant processes, drivers and feedbacks of climate at regional scale, observed data from multiple datasets (e.g., ground station networks, satellite products, reanalysis, etc.), simulations from different model types (including general circulation models (GCMs), regional climate models (RCMs), statistical downscaling methods, etc.) and experiments (e.g., Coupled Model Intercomparison Project Phases 5 and 6 (CMIP5 and 6), Coordinated Regional Climate Downscaling Experiment (CORDEX), and single-model initial-condition large ensembles), methodologies to attribute observed changes or events to large- and regional-scale anthropogenic and natural drivers and forcings as well as other relevant local knowledge (e.g., indigenous knowledge).

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Global warming has contributed to drying in dry summer climates including the Mediterranean (high confidence). Records of soil moisture indicate that higher temperatures and increased atmospheric demand have played a strong role in driving Mediterranean aridity. Multiple lines of evidence suggest that anthropogenic forcings are causing increased aridity and drought severity in the Mediterranean region (high confidence) (Section 8.3.1.6).

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Cape Town’s crisis resulted from a combination of a strong, rare multi-year meteorological drought (Figure 10.18), estimated at 1 in 300 years (Wolski, 2018), and factors related to the nature of the water supply system, operational water management and water resource policies. Cape Town was very successful in implementing water-saving actions after the previous drought of 2000–2003, reducing water losses from over 22% to 15% (Frame and Killick, 2007; DWA, 2013), breaking the previous coupling of growth in water demand with growth in population. As a consequence, Cape Town won a Water Smart City award from the C40 Cities program only three years prior to the crisis. However, the water-saving actions, together with changing priorities in water resource provision from infrastructure-oriented towards resource and demand management, may well have led to delays in implementation of the expansion of water supply infrastructure (Muller, 2018). The expansion plan, formulated a decade prior to the crisis, included an expectation of long-term climate-change drying in the region (DWAF, 2007). The crisis also exposed structural deficiencies of water management and inadequacy of a policy process in which decisions about local water resources are taken at a national level, particularly in a situation of political tension (Visser, 2018). The crisis was widely seen as a harbinger of future problems to be faced by the city, and a highlight of vulnerability of many cities in the world resulting from the interplay of three factors: (i) the fast urban-population growth, (ii) the economic, policy, infrastructural and water resource paradigms and constraints, and (iii) anthropogenic climate change.

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Cape Town is located at the south-western tip of Africa, within an approximately 100 km × 300 km region that receives 80% of its rainfall during the austral winter (March to October), with the largest portion in June to August. In the vicinity of Cape Town, rainfall is strongly heterogeneous, ranging from about 300 mm/year on coastal plains to >2,000 mm/year in mountain ranges. The Cape Town water supply relies on surface water reservoirs located in a few small mountain catchments (about 800 km2 in total). The Cape Town region receives 85% of its rainfall from a series of cold fronts forming within mid-latitude cyclones. The remainder is brought in by infrequent cut-off lows that occur throughout the year (Favre et al., 2013). This creates a very strong water resource dependency on a single rainfall delivery mechanism that may be strongly affected by anthropogenic climate change (Chapter 4 and Section 10.6.2.6).

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Global models show strong consistency in a drying signal for the Cape Town region, with the reduction in total annual rainfall of up to 20% by the end of the 21st century in CMIP5 RCP8.5 and CMIP6 SSP5-8.5 simulations (Figure 10.18; Almazroui et al., 2020c). The consistency across the models is a robust signal compared to the rest of southern Africa, where the climate change signal varies spatially: stronger drying in the west and moderate drying or weak wetting in the east (DEA, 2013, 2018; see Atlas.4.4 for further discussion of southern Africa precipitation projections). Rainfall changes projected for the Cape Town region are consistent with projected changes in hemispheric-scale processes and regional-scale dynamics that point toward reduced frequency of frontal systems affecting that region. These changes include robust signals in CMIP5 models for the Southern Hemisphere for a poleward expansion of the tropics (Hu et al., 2013b), poleward displacement of mid-latitude storm tracks (Chang et al., 2012), increased strength and poleward shift of the westerly winds (Bracegirdle et al., 2018) and subtropical jet-streams (Chenoli et al., 2017), and a shift toward a more positive phase of the SAM (E.-P. Lim et al., 2016). However, despite the consistency in circulation changes, the emergence of anthropogenic rainfall change above unforced variability in West Southern Africa remains uncertain for annual rainfall throughout most of the 21st century, even under SSP5-8.5 (Figure 10.15).

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Thus, although a clear association appears in observations from 1979 onward between increasing GHG concentrations, drying in the Cape Town region and behaviour of a key circulation process, the SAM, further analysis suggests caution. Not all global models show the historical post-1979 association among these factors, and when the observational record is extended back further to times when the anthropogenic greenhouse forcing was weaker, there is no strong association between the SAM and Cape Town drought. Thus, there is only medium confidence in the expectation of a future drier climate for Cape Town.

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The Indian summer monsoon provides 80% of the country’s annual rainfall from June to September, supplying the majority of water for agriculture, industry, drinking and sanitation to over a billion people. Any variations in the monsoon on time scales from days to decades can have large impacts (Challinor et al., 2006; Gadgil and Gadgil, 2006). Evidence from paleoclimate records (Sections 8.3.2.4.1) shows high confidence in a weakened Indian monsoon during cold epochs of the past such as the Younger Dryas (12,800–11,600 years ago) as measured by speleothem oxygen isotopes (Kathayat et al., 2016). There is a pressing need to understand if the monsoon will change in the future under anthropogenic forcing and to quantify such changes. Multiple datasets have shown robust negative trends since the 1950s until the turn of the century (Bollasina et al., 2011) followed by a recovery (Jin and Wang, 2017), yet repeated assessments project the monsoon to increase in strength under enhanced GHG forcing (Christensen et al., 2007, 2013; Sections 8.3.2.4.1 and 8.4.2.4.1). The apparent contradiction between future projections and observed historical trends makes the region an ideal choice for an in-depth assessment. The reader is also referred to the South Asia (SAS) regional assessment of precipitation extremes (Section 11.9), which is not discussed here for brevity.

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The robust decline of Indian summer monsoon rainfall averaged over India in the second half of the 20th century (Section 10.6.3.3) is not in line with expectations arising from thermodynamic constraints on the water cycle in a warming world (Section 8.2.2) and has been regarded as a puzzle (Goswami et al., 2006). Assessing the attribution of 20th-century changes to Indian rainfall is the subject of coordinated modelling under the Global Monsoon MIP (GMMIP; Zhou et al., 2016), but is complicated by long-standing dry biases in coupled CMIP3, CMIP5 (Sperber et al., 2013) and CMIP6 (Figure 10.19b) global models. These dry biases are connected to a lower tropospheric circulation that is too weak (Sperber et al., 2013) and wet biases in the equatorial Indian Ocean (Bollasina and Ming, 2013). Section 8.3.2.4.1 finds high confidence that anthropogenic aerosol emissions have dominated the observed declining trends of countrywide Indian summer monsoon rainfall, consistent with findings at the global-monsoon scale (Section 3.3.3.2).

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Stronger Northern Hemisphere aerosol emissions cool it relative to the Southern Hemisphere, increasing northward energy transport at the expense of moisture transport towards India (Bollasina et al., 2011). The attribution to anthropogenic aerosols is supported in CMIP5 single-forcing experiments, including some testing the sensitivity to local and remote emissions (Guo et al., 2015, 2016; Shawki et al., 2018), comparing CMIP5 GCMs forced by both aerosol and GHG to GHG only (Salzmann et al., 2014) and reducing emissions to pre-industrial levels (Takahashi et al., 2018). The large spread between individual model realisations of comparable magnitude to the aerosol-induced signal suggested to Salzmann et al. (2014) that internal variability may also play a role over regions such as northern-central India. Further uncertainty surrounds the level of radiative forcing. Dittus et al. (2020) forced a GCM with historical aerosol emissions scaled between 0.2 and 1.5 times their observed values, representing the spread in CMIP5 effective radiative forcing. The strongest forcing led to around 0.5 mm day–1less late-20th century Indian monsoon rainfall than the weakest (Shonk et al., 2020). Meanwhile, the uncertainty surrounding aerosol–cloud interactions could change the sign of long-term precipitation trends (Takahashi et al., 2018).

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A small anthropogenic contribution may be expected from local land-use/land-cover changes and land management. India is the world’s most irrigated region with around 0.5 mm/day in places, although peaking higher in summer (Cook et al., 2015b; McDermid et al., 2017). Including irrigation in GCMs and RCMs slows the monsoon circulation and diminishes rainfall (Lucas-Picher et al., 2011; Guimberteau et al., 2012; Shukla et al., 2014; Tuinenburg et al., 2014; Cook et al., 2015b) due to reduced surface temperature (Thiery et al., 2017), reducing the monsoon wind and moisture fluxes towards India (Mathur and AchutaRao, 2020). However, implementation methodologies for irrigation in climate models are simplified and often do not account for spatial heterogeneity while overestimating demand and supply (Section 10.3.3.6; Nazemi and Wheater, 2015; Pokhrel et al., 2016). Changing forest cover to agricultural land in an RCM (Paul et al., 2016) finds weakened summer monsoon rainfall especially in central and eastern India, due to decreased local evapotranspiration. Decreased evapotranspiration from a warmer surface since the 1950s in the CMIP5 ensemble may also feedback on the supply of moisture (Ramarao et al., 2015). Based on an AGCM study and literature review, Krishnan et al. (2016) support the role of land-use/land-cover change in adding to the effects of aerosol in weakening the monsoon, in addition to dynamic effects on the circulation caused by rapid warming of the Indian Ocean.

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In addition to anthropogenic forcing, there is evidence that internal variability in the Pacific is a significant driver. Huang et al. (2020b) compared a large perturbed-physics ensemble (HadCM3C) with a SMILE for the historical period. Ensemble members replicating the negative Indian rainfall trend were accompanied by a strong phase change in the PDV from negative to positive, consistent with SST observations. Jin and Wang (2017) have demonstrated increasing Indian monsoon rainfall since 2002 in a variety of observed datasets, suggesting the increase is due either to a change in dominance of a particular forcing (for example from aerosol to GHG) or to a change in phase of internal variability such as the PDV. Huang et al. (2020b) partially attribute the rainfall recovery to a phase change in the PDV, supported by a SMILE study combined with reanalyses (Ha et al., 2020).

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The drying trend of Indian summer monsoon rainfall since the mid-20th century can be attributed with high confidence to aerosol as the dominant anthropogenic forcing with a further contribution from internal variability, supported by the review of B. Wang et al. (2021) including CMIP6 results. Understanding the interplay between anthropogenic and internal drivers will be important for understanding future change.

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Given the assessment for a future wetter monsoon dominated by GHG emissions and attribution of the late-20th century decline to aerosol (Sections 8.3.2.4.1 and 10.6.3.5), the change between dominant forcings will lead, at some point, to a positive trend. For example, RCP4.5 experiments in an AGCM forced by coupled model-derived future SSTs showed continuation of 20th-century drying, before a rainfall recovery (Krishnan et al., 2016). By holding aerosol emissions at 2005 levels, lower monsoon rainfall is found throughout the 21st century than in a standard RCP8.5 scenario (Zhao et al., 2019), suggesting that the timing of the recovery will be partially controlled by the rate at which aerosol emissions decline. The spread in spatial distribution of aerosol emissions in SSPs may also play a role in near-term projections (Samset et al., 2019). Under divergent air-quality policies, SSP3 features a dipole of declining sulphate emissions for China but increases over India, leading to suppression of GHG-related precipitation increases there (Wilcox et al., 2020). For the near-term future around the mid-21st century, the interplay between internal variability and external forcing must be considered (Singh and AchutaRao, 2019). Huang et al. (2020a) used two SMILEs to show that internal variability related to PDV could potentially overcome the GHG-forced upward trend in Indian monsoon rainfall, consistent with assessments of the global monsoon for the near term (Section 4.4.1.4). Emergence of the anthropogenic signal for South Asian precipitation is shown from the 2050s onwards in CMIP6 (Figure 10.15b).

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Projections can also be expressed in terms of global-mean warming levels (GWLs) rather than time horizons (Cross-Chapter Box 11.1). Advancing on SR1.5, amplification of mean and extreme monsoon rainfall at 2.0°C compared to 1.5°C has been found both by an AGCM forced by future SST patterns (Chevuturi et al., 2018) and by using time slices in CMIP5 GCMs (Yaduvanshi et al., 2019; J. Zhang et al., 2020). These findings are consistent with the general scaling of Indian monsoon precipitation per degree of warming in CMIP5 (Zhang et al., 2019) and CMIP6 (B. Wang et al., 2021). Increasing GWLs also lead to emergence of the anthropogenic signal over larger proportions of the South Asian region (Figure 10.15a).

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Finally, the recent national climate-change assessment for India (Krishnan et al., 2020) has distilled multiple lines of evidence to show declining summer monsoon rainfall over the second half of the 20th century, attributable to emissions of anthropogenic aerosols, while future projections informed by CMIP5 modelling and dominated by GHG forcing show increased mean rainfall by the end of the 21st century.

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There is very high confidence (robust evidence, high agreement) of a negative trend of summer monsoon rainfall over the second half of the 20th century averaged over all of India. There is medium agreement over trends at the regional level owing to uncertainty among observational products, which hinders model evaluation, downscaling and assessment of changes to extremes. There is high confidence (robust evidence, medium agreement) that anthropogenic aerosol emissions over the Northern Hemisphere and internal variability have contributed to the negative trend, while there is high confidence (robust evidence, medium agreement) that Indian summer monsoon rainfall will increase at the end of the 21st century in response to increased GHG forcing, due to the dominance of thermodynamic mechanisms. No contradictory evidence is found from downscaling methods. The contrast between declining rainfall in the observational record and long-term future increases can be explained using multiple lines of evidence. They are not contradictory since they are attributable to different mechanisms (primarily aerosols and greenhouse gases, respectively). The long-term future changes are generally consistent across global (including at high resolution) and regional climate models, and supported by theoretical arguments. Furthermore, while there are subtle differences found in past periods with a climate similar to the future climate (the mid-Holocene), different physical mechanisms at play suggest that paleoclimate evidence does not reduce confidence in the future projections. In the near term, there is high confidence that internal variability will dominate.

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The Mediterranean summer climate is affected by large-scale modes of natural variability, the most dominant being the NAO (Annex IV) in winter and the summer NAO in summer (Folland et al., 2009; Bladé et al., 2012), although regional differences exist. The influence of those modes of variability over the eastern Mediterranean is recognized by some studies (Chronis et al., 2011; Kahya, 2011; Black, 2012; Bladé et al., 2012), but disputed by others (Ben-Gai et al., 2001; Ziv et al., 2006; Donat et al., 2014; Turki et al., 2016; Zamrane et al., 2016; Han et al., 2019). During positive summer NAO phase, associated with an upper-level trough over the Balkans, the Mediterranean is anomalously wet (Bladé et al., 2012). Drivers of Mediterranean climate variability include modes of variability such as the AMV (Sutton and Dong, 2012) and the Asian monsoon (Rodwell and Hoskins, 1996; Logothetis et al., 2020). In addition, the increase of GHGs (e.g., Zittis et al., 2019), the decrease of anthropogenic aerosols over Europe and the Mediterranean since the 1980s resulting from air pollution policies (Turnock et al., 2016), and anthropogenic land-use change (Millán, 2014; MedECC 2020) have been shown to be linked to the regional warming. The role of the zonal averaged circulation as a driver for the Mediterranean climate has been stressed by (Garfinkel et al., 2020).

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The observed trends over 1901–2010 are outside the range of internal variability shown in CMIP5 pre-industrial control experiments and consistent with, or greater than those simulated by experiments including both anthropogenic and natural forcings (Knutson et al., 2013) and therefore partly attributable to anthropogenic forcing. The decrease of anthropogenic aerosols over Europe including the Mediterranean resulting from European de-industrialisation and air pollution policies (Turnock et al., 2016) has been highlighted as an important contributor to the observed warming (Ruckstuhl et al., 2008; Philipona et al., 2009; de Laat and Crok, 2013; Nabat et al., 2014; Besselaar et al., 2015; Dong et al., 2017; Boé et al., 2020a). Pfeifroth et al. (2018) argue that this brightening is mainly due to cloud changes caused by the indirect aerosol effect with a minor role for the direct aerosol effect, in contrast to Nabat et al. (2014) and Boers et al. (2017) who attribute it to the direct aerosol effect. Using model sensitivity experiments, Nabat et al. (2014) also associated the increase in Mediterranean SST since 1980–2012 with the decrease in aerosol concentrations (Atlas.8.2, Atlas.8.3 and Atlas.8.5).

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As noted in Section 10.6.4.4, the Mediterranean summer climate is affected by large-scale circulation patterns, of which the summer NAO is the most important (Folland et al., 2009; Bladé et al., 2012). Barcikowska et al. (2020) highlight the importance of correctly simulating the summer NAO impact on the Mediterranean climate, as it partly offsets the anthropogenic warming signal in the western and central Mediterranean.

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Despite the large efforts of these regional downscaling projects, the global model–RCM matrix is still sparse and lacking a systematic design to explore the uncertainty sources (e.g., global model, RCM, scenario, resolution) (Section 10.3). Focusing on the Iberian peninsula, Fernández et al. (2019) argued that the driving global model is the main contributor to uncertainty in the ensemble. Physically consistent but implausible temperature changes in RCMs can occur. An example is a strong temperature increase over the Pyrenees due to excessive snow cover in the present climate (Fernández et al., 2019). Based on an older set of RCM simulations (ENSEMBLES), Déqué et al. (2012) also argued that the largest source of uncertainty in the temperature response over southern Europe is the choice of the driving global model (whereas for summer precipitation the choice of the RCM dominates the uncertainty). Finally, Boé et al. (2020a) found that over a large area of Europe, including parts of the Mediterranean, RCMs project a summer warming 1.5°C–2°C colder than in their driving global models for the end of the 21st century. This is caused by differences in solar radiation related to the absence of time-varying anthropogenic aerosols in RCMs (Boé et al., 2020a; Gutiérrez et al., 2020), which also affects the noted differences in cloud cover between global models and RCMs (Bartók et al., 2017).

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During nighttime, urban centres are often several degrees warmer than the surrounding rural area, a phenomenon known as the nighttime canopy urban heat island effect (Bader et al., 2018; Kuang, 2019; Li et al., 2019; Y. Li et al., 2020a). While green and blue infrastructures can mitigate the urban heat island effect, three main factors contribute to its development (Hamdi et al., 2020; Masson et al., 2020): (i) three-dimensional urban geometry including building density and plan area, street aspect ratio and building height; (ii) thermal characteristics of impervious surfaces; and (iii) anthropogenic heat release, either from building energy consumption, especially waste heat from air conditioning systems, or as direct emissions from industry, traffic, or human metabolism (Ichinose et al., 1999; Sailor, 2011; de Munck et al., 2013; Bohnenstengel et al., 2014; Chow et al., 2014; Salamanca et al., 2014; Dou and Miao, 2017; Ma et al., 2017a; Chrysoulakis et al., 2018; Takane et al., 2019). Urban heat island magnitude is also affected by aerosols due to air pollution in urban areas (Cheng et al., 2020; Han et al., 2020) and by local background climate (Zhao et al., 2014; Ward et al., 2016).

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Exchanges of heat, water and momentum between the urban surface and its overlying atmosphere are calculated using specific surface-atmosphere exchange schemes. Three different schemes, here in order of increasing complexity, can be distinguished (Masson, 2006; Grimmond et al., 2010, 2011; Chen et al., 2011; Best and Grimmond, 2015): (i) in the slab or bulk approach, the three-dimensional city structure is not resolved but cities are represented by modifying soil and vegetation parameters within land surface models, increasing roughness length and displacement height (e.g., Seaman et al., 1989; Dandou et al., 2005; Best et al., 2006; Liu et al., 2006). The energy balance is often modifiedto account for the radiation trapped by the urban canopy, heat storage, evaporation and anthropogenic heat fluxes. (ii) Single-layer urban canopy modules use a simplified geometry (urban canyon, with three surface types: roof, road and wall) that approximately capture the three-dimensional dynamical and thermal physical processes influencing radiative and energy fluxes (Masson, 2000; Kusaka et al., 2001). (iii) Multi-layer urban canopy modules compute urban effects vertically, allowing a direct interaction with the planetary boundary layer (Brown, 2000; Martilli et al., 2002; Hagishima et al., 2005; Dupont and Mestayer, 2006; Hamdi and Masson, 2008; Schubert et al., 2012). Building-energy models that estimate anthropogenic heat from a building for given atmospheric conditions can be incorporated. Recent model development has focused on improving the representation of urban vegetation (Lee et al., 2016; Redon et al., 2017; Mussetti et al., 2020).

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The SREX, AR5, and SR1.5 assessed that there is evidence from observations that some extremes have changed since the mid-20th century, that some of the changes are a result of anthropogenic influences, and that some observed changes are projected to continue into the future. Additionally, other changes are projected to emerge from natural climate variability under enhanced global warming (SREX Chapter 3; AR5 Chapter 10).

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In summary, both thermodynamic and dynamic processes are involved in the changes of extremes in response to warming. Anthropogenic forcing (e.g., increases in greenhouse gas concentrations) directly affects thermodynamic variables, including overall increases in high temperatures and atmospheric evaporative demand, and regional changes in atmospheric moisture, which intensify heatwaves, droughts and heavy precipitation events when they occur (high confidence). Dynamic processes are often indirect responses to thermodynamic changes, are strongly affected by internal climate variability, and are also less well understood. As such, there is low confidence in how dynamic changes affect the location and magnitude of extreme events in a warming climate.

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Aerosol forcing also has a strong regional footprint associated with regional emissions, which affects temperature and precipitation extremes (high confidence) (Sections 11.3 and 11.4). From around the 1950s to 1980s, enhanced aerosol loadings led to regional cooling due to decreased global solar radiation (‘global dimming’) which was followed by a phase of ‘global brightening’ due to a reduction in aerosol loadings (Chapters 3 and 7; Wild et al., 2005). King et al. (2016b) show that aerosol-induced cooling delayed the timing of a significant human contribution to record-breaking heat extremes in some regions. However, the decreased aerosol loading since the 1990s has led to an accelerated warming of hot extremes in some regions. Based on Earth system model (ESM) simulations, Dong et al. (2017) suggest that a substantial fraction of the warming of the annual hottest days in Western Europe since the mid-1990s has been due to decreases in aerosol concentrations in the region. Dong et al. (2016b) also identify non-local effects of decreases in aerosol concentrations in Western Europe, which they estimate played a dominant role in the warming of the hottest daytime temperatures in north-east Asia since the mid-1990s, via induced coupled atmosphere–land surface and cloud feedbacks, rather than a direct impact of anthropogenic aerosol changes on cloud condensation nuclei.

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However, since AR5, the attribution of extreme weather events has emerged as a growing field of climate research with an increasing body of literature (see series of supplements to the annual State of the Climate report (Peterson et al., 2012, 2013a; Herring et al., 2014, 2015, 2016, 2018), including the number of approaches to examining extreme events(described in Easterling et al., 2016; Otto, 2017; Stott et al., 2016)). A commonly used approach – often called the risk-based approach in the literature, and referred to here as the ‘probability-based approach’ – produces statements such as ‘anthropogenic climate change made this event type twice as likely ’ or ‘anthropogenic climate change made this event 15% more intense’. This is done by estimating probability distributions of the index characterizing the event in today’s climate, as well as in a counterfactual climate, and either comparing intensities for a given occurrence probability (e.g., 1-in-100-year event) or probabilities for a given magnitude (see FAQ 11.3). There are a number of different analytical methods encompassed in the probability-based approach, building on observations and statistical analyses (e.g., van Oldenborgh et al., 2012), optimal fingerprint methods (Sun et al., 2014), regional climate and weather forecast models (e.g., Schaller et al., 2016), global climate models (GCMs) (e.g., Lewis and Karoly, 2013), and large ensembles of atmosphere-only GCMs (e.g., Lott et al., 2013). A key component in any event attribution analysis is the level of conditioning on the state of the climate system. In the least conditional approach, the combined effect of the overall warming and changes in the large-scale atmospheric circulation are considered and often utilize fully coupled climate models (Sun et al., 2014). Other more conditional approaches involve prescribing certain aspects of the climate system. These range from prescribing the pattern of the surface ocean change at the time of the event (e.g., Hoerling et al., 2013, 2014), often using Atmospheric Model Intercomparison Project (AMIP) style global models, where the choice of sea surface temperature and ice patterns influences the attribution results (Sparrow et al., 2018), to prescribing the large-scale circulation of the atmosphere and using weather forecasting models or methods (e.g., Pall et al., 2017; Patricola and Wehner, 2018; Wehner et al., 2018a). These highly conditional approaches have also been called ‘storylines’ (Cross-Working Group Box 1.1; Shepherd, 2016) and can be useful when applied to extreme events that are too rare to otherwise analyse, or where the specific atmospheric conditions were central to the impact. These methods are also used to enable the use of very high-resolution simulations in cases were lower-resolution models do not simulate the regional atmospheric dynamics well (Shepherd, 2016; Shepherd et al., 2018). However, the imposed conditions limit an overall assessment of the anthropogenic influence on an event, as the fixed aspects of the analysis may also have been affected by climate change. For instance, the specified initial conditions in the highly conditional hindcast attribution approach often applied to tropical cyclones (e.g., Takayabu et al., 2015; Patricola and Wehner, 2018) permit only a conditional statement about the magnitude of the storm if similar large-scale meteorological patterns could have occurred in a world without climate change, thus precluding any attribution statement about the change in frequency if used in isolation. Combining conditional assessments of changes in the intensity with a multi-model approach does allow for the latter as well (Shepherd, 2016).

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Numerous studies have compared the regional response to anthropogenic forcing at GWLs in annual and seasonal mean values and extremes of different climate and impact variables across different multi-model ensembles and/or different scenarios (e.g., Frieleret al. , 2012; Scheweet al. , 2014; Hergeret al. , 2015; Schleussneret al. , 2016; Seneviratneet al. , 2016; Wartenburgeret al. , 2017; Bettset al. , 2018; Dosio and Fischer, 2018; Samset et al. , 2019; Tebaldiet al. , 2020; see Sections 4.6.1, 8.5.3, 9.3.1, 9.5, 9.6.3, 10.4.3 and 11.2.4 for further details). The regional response patterns at given GWLs have been found to be consistent across different scenarios for many climate variables (Cross-Chapter Box 11.1 Figure 2; Pendergrasset al. , 2015; Seneviratneet al. , 2016; Wartenburgeret al. , 2017; Seneviratne and Hauser, 2020). The consistency tends to be higher for temperature-related variables than for variables in the hydrological cycle or variables characterizing atmospheric dynamics, and for intermediate to high-emissions scenarios than for low-emissions scenarios (e.g., for mean precipitation in the Representative Concentration Pathway (RCP) 2.6 scenario: Pendergrass et al., 2015; Wartenburger et al., 2017). Nonetheless, Cross-Chapter Box 11.1 Figure 2 illustrates that, even for mean precipitation, which is known to be forcing dependent (Sections 4.6.1 and 8.5.3), scenario differences in the response pattern at a given GWL are smaller than model uncertainty and internal variability in many regions (Herger et al., 2015). The response pattern is further found to be broadly consistent between models that reach a GWL relatively early, and those that reach it later under a given Shared Socio-economic Pathway (SSP; see Cross-Chapter Box 11.1, Figure 2g,h).

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Regional external forcings, including land-use changes and emissions of anthropogenic aerosols, play an important role in the changes of temperature extremes in some regions (high confidence) (Section 11.1.6). Deforestation may have contributed to about one third of the warming of hot extremes in some mid-latitude regions since the pre-industrial time (Lejeune et al., 2018). Aspects of agricultural practice, including no-till farming, irrigation, and overall cropland intensification, may cool hot temperature extremes (Davin et al., 2014; N.D. Mueller et al., 2016). For instance, cropland intensification has been suggested to be responsible for a cooling of the highest temperature percentiles in Midwest USA (N.D. Mueller et al., 2016). Irrigation has been shown to be responsible for a cooling of hot temperature extremes of up to 1°C–2°C in many mid-latitude regions in the present climate (Thieryet al., 2017, 2020), a process not represented in most of state-of-the-art ESMs (CMIP5, CMIP6). Double cropping may have led to increased hot extremes in the inter-cropping season in part of China (Jeong et al., 2014). Rapid increases in summer warming in western Europe and north-east Asia since the 1980s are linked to a reduction in anthropogenic aerosol precursor emissions over Europe (Nabat et al., 2014; Dong et al., 2016b, 2017), in addition to the effect of increased greenhouse gas forcing (see also Section 10.1.3.1). This effect of aerosols on temperature-related extremes is also noted for declines in short-lived anthropogenic aerosol emissions over North America (Mascioli et al., 2016). On the local scale, the urban heat island (UHI) effect results in higher temperatures in urban areas than in their surrounding regions, and contributes to warming in regions of rapid urbanization, in particular for nighttime temperature extremes (Box 10.3; Phelan et al., 2015; Chapman et al., 2017; Y. Sun et al., 2019). But these local and regional forcings are generally not or not well represented in the CMIP5 and CMIP6 simulations (see also Section 11.3.3), contributing to uncertainty in model simulated changes.

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In summary, greenhouse gas forcing is the dominant driver leading to the warming of temperature extremes. At regional scales, changes in temperature extremes are modulated by changes in large-scale patterns and modes of variability, feedbacks including soil-moisture–evapotranspiration–temperature or snow/ice–albedo–temperature feedbacks, and local and regional forcings such as land-use and land-cover changes, or aerosol concentrations, and decadal and multi-decadal natural variability. This leads to heterogeneity in regional changes and their associated uncertainties (high confidence). Changes in anthropogenic aerosol concentrations have likely affected trends in hot extremes in some regions. Irrigation and crop expansion have attenuated increases in summer hot extremes in some regions, such as the Midwestern USA (medium confidence). Urbanization has likely exacerbated the effects of global warming in cities, in particular for nighttime temperature extremes.

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The SREX (IPCC, 2012) assessed that it is likely anthropogenic influences have led to the warming of extreme daily minimum and maximum temperatures at the global scale. The AR5 concluded that human influence has very likely contributed to the observed changes in the intensity and frequency of daily temperature extremes on the global scale in the second half of the 20th century (IPCC, 2014). With regard to individual, or regionally or locally specific events, AR5 concluded that it is likely human influence has substantially increased the probability of occurrence of heatwaves in some locations.

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Studies since AR5 continue to attribute the observed increase in the frequency or intensity of hot extremes and the observed decrease in the frequency or intensity of cold extremes to human influence, dominated by anthropogenic greenhouse gas emissions, on global and continental scales, and for many AR6 regions. These include attribution of changes in the magnitude of annual TXx, TNx, TXn, and TNn, based on different observational datasets including, HadEX2 and HadEX3, CMIP5 and CMIP6 simulations, and different statistical methods (Kim et al., 2016; Z. Wang et al., 2017a; Seong et al., 2021). As is the case for an increase in mean temperature (Section 3.3.1), an increase in extreme temperature is mostly due to greenhouse gas forcing, offset by aerosol forcing. The aerosols’ cooling effect is clearly detectable over Europe and Asia (Seong et al., 2021). As much as 75% of the moderate daily hot extremes (above 99.9th percentile) over land are due to anthropogenic warming (Fischer and Knutti, 2015). New results are found to be more robust due to the extended period that improves the signal-to-noise ratio. The effect of anthropogenic forcing is clearly detectable and attributable in the observed changes in these indicators of temperature extremes, even at country and sub-country scales, such as in Canada (Wan et al., 2019). Changes in the number of warm nights, warm days, cold nights, and cold days, and other indicators such as the Warm Spell Duration Index (WSDI), are also attributed to anthropogenic influence (Christidis and Stott, 2016; Hu et al., 2020).

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Regional studies, including for Asia (Dong et al., 2018; Lu et al., 2018), Australia (Alexander and Arblaster, 2017), and Europe (Christidis and Stott, 2016), found similar results. A clear anthropogenic signal is also found in the trends in the Combined Extreme Index (CEI) for North America, Asia, Australia, and Europe (Dittus et al., 2016). While various studies have described increasing trends in several heatwave metrics (heatwave duration, the number of heatwave days, etc.) in different regions (e.g., Cowan et al., 2014; Bandyopadhyay et al., 2016; M. Sanderson et al., 2017), few recent studies have explicitly attributed these changes to causes; most of them stated that observed trends are consistent with anthropogenic warming. The detected anthropogenic signals are clearly separable from the response to natural forcing, and the results are generally insensitive to the use of different model samples, as well as different data availability, indicating robust attribution. Studies of monthly, seasonal, and annual records in various regions (Kendon, 2014; Lewis and King, 2015; Bador et al., 2016; Meehl et al., 2016; C. Zhou et al., 2019) and globally (King, 2017) show an increase in the breaking of hot records and a decrease in the breaking of cold records (King, 2017). Changes in anthropogenically attributablerecord-breaking rates are noted to be largest over the Northern Hemisphere land areas (Shiogama et al., 2016). Yin and Sun (2018) found clear evidence of an anthropogenic signal in the changes in the number of frost and ice days, when multiple model simulations were used. In some key wheat-producing regions of Southern Australia, increases in frost days or frost season length have been reported (Dittus et al., 2014; Crimp et al., 2016); these changes are linked to decreases in rainfall, cloud-cover, and subtropical ridge strength, despite an overall increase in regional mean temperatures (Dittus et al., 2014; Pepler et al., 2018).

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A significant advance since AR5 has been a large number of studies focusing on extreme temperature events at monthly and seasonal scales, using various extreme event attribution methods. Diffenbaugh et al. (2017) found that anthropogenic warming has increased the severity and probability of the hottest month by more than 80% of the available observational area on the global scale. Christidis and Stott (2014) provide clear evidence that warm events have become more probable because of anthropogenic forcings. Sun et al. (2014) found that human influence has caused a more than 60-fold increase in the probability of the extreme warm 2013 summer in eastern China since the 1950s. Human influence is found to have increased the probability of the historically hottest summers in many regions of the world, both in terms of mean temperature (B. Mueller et al., 2016) and wet bulb globe temperature (WBGT; C. Li et al., 2017). In most regions of the Northern Hemisphere, changes in the probability of extreme summer average WBGT were found to be about an order of magnitude larger than changes in the probability of extreme hot summers estimated by surface air temperature (C. Li et al., 2017). In addition to these generalized, global-scale approaches, extreme event studies have found an attributable increase in the probability of hot annual and seasonal temperatures in many locations, including Australia (Knutson et al., 2014b; Lewis and Karoly, 2014), China (Sun et al., 2014; Sparrow et al., 2018; Zhou et al., 2020), Korea (Y.-H. Kim et al., 2018) and Europe (King et al., 2015b).

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There have also been many extreme event attribution studies that examined short-duration temperature extremes, including daily temperatures, temperature indices, and heatwave metrics. Examples of these events from different regions are summarized in various annual Explaining Extreme Events supplements of the Bulletin of the American Meteorological Society (Peterson et al., 2012, 2013a; Herring et al., 2014, 2015, 2016, 2018, 2019, 2020), including a number of approaches to examine extreme events (described in Easterling et al., 2016; Stott et al., 2016; Otto, 2017). Several studies of recent events from 2016 onwards have determined an infinite risk ratio (a fraction of attributable risk, or FAR, of 1), indicating that the occurrence probability for such events is close to zero in model simulations without anthropogenic influences (see Herring et al., 2018, 2019, 2020; Imada et al., 2019; Vogel et al., 2019). Though it is difficult to accurately estimate the lower bound of the uncertainty range of the FAR in these cases (Paciorek et al., 2018), the fact that those events are so far outside the envelop of the models with only natural forcing indicates that it is extremely unlikely for those events to occur without human influence.

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Studies that focused on the attributable signal in observed cold extreme events show human influence reducing the probability of those events. Individual attribution studies on the extremely cold winter of 2011 in Europe (Peterson et al., 2012), in the eastern USA during 2014 and 2015 (Trenary et al., 2015, 2016; Wolter et al., 2015; Bellprat et al., 2016), in the cold spring of 2013 in the United Kingdom (Christidis et al., 2014), and of 2016 in eastern China (Qian et al., 2018; Y. Sun et al., 2018b) all showed a reduced probability due to human influence on the climate. An exception is the study of Grose et al. (2018), which found an increase in the probability of the severe western Australian frost of 2016 due to anthropogenically-driven changes in circulation patterns that drive cold outbreaks and frost probability.

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The relative strength of anthropogenic influences on temperature extremes is regionally variable, in part due to differences in changes in atmospheric circulation, land–surface feedbacks, and other external drivers such as aerosols. For example, in the Mediterranean and over western Europe, risk ratios on the order of 100 have been found (Kew et al., 2019; Vautard et al., 2020), whereas in the USA, changes are much less pronounced. This is probably a reflection of the land–surface feedback enhanced extreme 1930s temperatures that reduce the rarity of recent extremes, in addition to the definition of the events and framing of attribution analyses (e.g., spatial and temporal scales considered). Local forcing may mask or enhance the warming effect of greenhouse gases. In India, short-lived aerosols or an increase in irrigation may be masking the warming effect of greenhouse gases (Wehner et al., 2018c). Irrigation and crop intensification have been shown to lead to a cooling in some regions, in particular in North America, Europe, and India (high confidence) (N.D. Mueller et al., 2016; Thiery et al., 2017, 2020; Chen and Dirmeyer, 2019). Deforestation has contributed about one third of the total warming of hot extremes in some mid-latitude regions since pre-industrial times (Lejeune et al., 2018). Despite all of these differences, and larger uncertainties at the regional scale, nearly all studies demonstrated that human influence has contributed to an increase in the frequency or intensity of hot extremes and to a decrease in the frequency or intensity of cold extremes.

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Both SREX (Chapter 3, Seneviratne et al., 2012) and AR5 (Chapter 10, IPCC, 2014) concluded with medium confidence that anthropogenic forcing has contributed to a global-scale intensification of heavy precipitation over the second half of the 20th century. These assessments were based on the evidence of anthropogenic influence on aspects of the global hydrological cycle, in particular, the human contribution to the warming-induced observed increase in atmospheric moisture that leads to an increase in heavy precipitation, and limited evidence of anthropogenic influence on extreme precipitation of durations of one and five days.

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Since AR5 there has been new and robust evidence and improved understanding of human influence on extreme precipitation. In particular, detection and attribution analyses have provided consistent and robust evidence of human influence on extreme precipitation of one- and five-day durations at global to continental scales. The observed increases in Rx1day and Rx5day over the Northern Hemisphere land area during 1951–2005 can be attributed to the effect of combined anthropogenic forcing, including greenhouse gases and anthropogenic aerosols, as simulated by CMIP5 models and the rate of intensification with regard to warming is consistent with C-C scaling (Zhang et al., 2013). This is confirmed to be robust when an additional nine years of observational data and the CMIP6 model simulations were used (Cross-Chapter Box 3.2, Figure 1; Paik et al., 2020). The influence of greenhouse gases is attributed as the dominant contributor to the observed intensification. The global average of Rx1day in the observations is consistent with simulations by both CMIP5 and CMIP6 models under anthropogenic forcing, but not under natural forcing (Cross-Chapter Box 3.2, Figure 1). The observed increase in the fraction of annual total precipitation falling into the top fifth or top first percentiles of daily precipitation can also be attributed to human influence at the global scale (Dong et al., 2021). The CMIP5 models were able to capture the fraction of land experiencing a strong intensification of heavy precipitation during 1960–2010 under anthropogenic forcing, but not in unforced simulations (Fischer et al., 2014). But the models underestimated the observed trends (Borodina et al., 2017a). Human influence also significantly contributed to the historical changes in record-breaking one-day precipitation (Shiogama et al., 2016). There is also limited evidence of the influences of natural forcing. Substantial reductions in Rx5day and Simple Daily Intensity Index (SDII) for daily precipitation intensity over the global summer monsoon regions occurred during 1957–2000 after explosive volcanic eruptions (Paik and Min, 2018). The reduction in post-volcanic eruption extreme precipitation in the simulations is closely linked to the decrease in mean precipitation, for which both thermodynamic effects (moisture reduction due to surface cooling) and dynamic effects (monsoon circulation weakening) play important roles.

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There has been new evidence of human influence on extreme precipitation at continental scales, including the detection of the combined effect of greenhouse gases and aerosol forcing on Rx1day and Rx5day over North America, Eurasia, and mid-latitude land regions (Zhang et al., 2013) and of greenhouse gas forcing in Rx1day and Rx5day in the mid-to-high latitudes, western and eastern Eurasia, and the global dry regions (Paik et al., 2020). These findings are corroborated by the detection of human influence in the fraction of extreme precipitation in the total precipitation over Asia, Europe, and North America (Dong et al., 2021). Human influence was found to have contributed to the increase in frequency and intensity of regional precipitation extremes in North America during 1961–2010, based on optimal fingerprinting and event attribution approaches (Kirchmeier-Young and Zhang, 2020). Tabari et al. (2020) found the observed latitudinal increase in extreme precipitation over Europe to be consistent with model-simulated responses to anthropogenic forcing.

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Evidence of human influence on extreme precipitation at regional scales is more limited and less robust. In north-west Australia, the increase in extreme rainfall since 1950 can be related to increased monsoonal flow due to increased aerosol emissions, but cannot be attributed to an increase in greenhouse gases (Dey et al., 2019a). Anthropogenic influence on extreme precipitation in China was detected in one study (H. Li et al., 2017), but not in another using different detection and data-processing procedures (W. Li et al., 2018a), indicating the lack of robustness in the detection results. A still weak signal-to-noise ratio seems to be the main cause for the lack of robustness, as detection would become robust 20 years in the future (W. Li et al., 2018a). Krishnan et al. (2016) attributed the observed increase in heavy rain events (intensity >100 mm day–1) in the post-1950s over central India to the combined effects of greenhouse gases, aerosols, land-use and land-cover changes, and rapid warming of the equatorial Indian Ocean SSTs. Roxyet al. (2017) and Devanand et al. (2019) showed that the increase in widespread extremes over the South Asian Monsoon during 1950–2015 is due to the combined impacts of the warming of the Western Indian Ocean (Arabian Sea) and the intensification of irrigation water management over India.

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Anthropogenic influence may have affected the large-scale meteorological processes necessary for extreme precipitation and the localized thermodynamic and dynamic processes, both contributing to changes in extreme precipitation events. Several new methods have been proposed to disentangle these effects by either conditioning on the circulation state or attributing analogues. In particular, the extremely wet winter of 2013–2014 in the UK can be attributed, approximately to the same degree, to both temperature-induced increases in saturation vapour pressure and changes in the large-scale circulation (Vautard et al., 2016; Yiou et al., 2017). There are multiple cases indicating that very extreme precipitation may increase at a rate more than the C-C rate (7% per 1°C of warming) (Pall et al., 2017; Risser and Wehner, 2017; van der Wiel et al., 2017; van Oldenborgh et al., 2017; S.-Y.S. Wang et al., 2018).

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Event attribution studies found an influence of anthropogenic activities on the probability or magnitude of observed extreme precipitation events, including European winters (Schaller et al., 2016; Otto et al., 2018b), extreme 2014 precipitation over the northern Mediterranean (Vautard et al., 2015), parts of the USA for individual events (Knutson et al., 2014a; Szeto et al., 2015; Eden et al., 2016; van Oldenborgh et al., 2017), extreme rainfall in 2014 over Northland, New Zealand (Rosier et al., 2015) or China (Burke et al., 2016; Sun and Miao, 2018; Yuan et al., 2018b; Zhou et al., 2018). However, for other heavy rainfall events, studies identified a lack of evidence about anthropogenic influences (Imada et al., 2013; Schaller et al., 2014; Otto et al., 2015c; Siswanto et al., 2015). There are also studies where results are inconclusive because of limited reliable simulations (Christidis et al., 2013b; Angélil et al., 2016). Overall, both the spatial and temporal scales on which extreme precipitation events are defined are important for attribution; events defined on larger scales have larger signal-to-noise ratios and thus the signal is more readily detectable. At the current level of global warming, there is a strong enough signal to be detectable for large-scale extreme precipitation events, but the chance of detecting such signals for smaller-scale events decreases (Kirchmeier-Young et al., 2019).

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In summary, most of the observed intensification of heavy precipitation over land regions is likely due to anthropogenic influence, for which greenhouse gases emissions are the main contributor. New and robust evidence since AR5 includes attribution to human influence of the observed increases in annual maximum one-day and five-day precipitation and in the fraction of annual precipitation falling in heavy events. The evidence since AR5 also includes a larger fraction of land showing enhanced extreme precipitation and a larger probability of record-breaking one-day precipitation than expected by chance, both of which can only be explained when anthropogenic greenhouse gas forcing is considered. Human influence has contributed to the intensification of heavy precipitation in three continents where observational data are more abundant (high confidence) (North America, Europe and Asia). On the spatial scale of AR6 regions, there is limited evidence of human influence on extreme precipitation, but new evidence is emerging; in particular, studies attributing individual heavy precipitation events found that human influence was a significant driver of the events, particularly in the winter season.

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There are very few studies focused on the attribution of long-term changes in floods, but there are studies on changes in flood events. Most of the studies focus on flash floods and urban floods, which are closely related to intense precipitation events (Hannaford, 2015). In other cases, event attribution focused on runoff using hydrological models, and examples include river basins in the UK (Section 11.4.4; Schaller et al., 2016; Kay et al., 2018), the Okavango River in Africa (Wolski et al., 2014), and the Brahmaputra River in Bangladesh (Philip et al., 2019). Findings about anthropogenic influences vary between different regions and basins. For some flood events, the probability of high floods in the current climate is lower than in a climate without an anthropogenic influence (Wolski et al., 2014), while in other cases anthropogenic influence leads to more intense floods (Cho et al., 2016; Pall et al., 2017; van der Wiel et al., 2017; Philip et al., 2018a; Teufel et al., 2019). Factors such as land-cover change and river management can also increase the probability of high floods (Ji et al., 2020). These, along with model uncertainties and the lack of studies overall, suggest a low confidence in general statements to attribute changes in flood events to anthropogenic climate change. A few individual regions have been well studied, which allows for high confidence in the attribution of increased flooding in these cases. For example, flooding in the UK following increased winter precipitation (Schaller et al., 2016; Kay et al., 2018) can be attributed to anthropogenic climate change (Schaller et al., 2016; Vautard et al., 2016; Yiou et al., 2017; Otto et al., 2018b).

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Attributing changes in heavy precipitation to anthropogenic activities (Section 11.4.4) cannot be readily translated to attributing changes in floods to human activities, because precipitation is only one of the multiple factors, albeit an important one, that affect floods. For example, Teufel et al. (2017) showed that, while human influence increased the odds of the flood-producing rainfall for the 2013 Alberta flood in Canada, it was not detected to have influenced the probability of the flood itself. Schaller et al. (2016) showed that human influence on the increase in the probability of heavy precipitation translated linearly into an increase in the resulting river flow of the Thames in the UK in winter 2014, but its contribution to the inundation was inconclusive.

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Gudmundsson et al. (2021) compared the spatial pattern of the observed regional trends in high river flows (>90th percentile) over 1971–2010 with that simulated by global hydrological models. The hydrological models were driven by outputs of climate model simulations under all historical forcing and pre-industrial forcing conditions. They found complex spatial patterns of extreme river flow trends. They also found the observed spatial patterns of trends can be reproduced only if anthropogenic climate change is considered, and that simulated effects of water and land management cannot reproduce the observed spatial pattern of trends. As there is only one study and multiple caveats associated with the study, including relatively poor observational data coverage, there is low confidence about human influence on the changes in high river flows on the global scale.

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Attribution studies for recent meteorological drought events are available for various regions. In Western and Central Europe, a multi-method and multi-model attribution study on the 2015 Central European drought did not find conclusive evidence for whether human-induced climate change was a driver of the rainfall deficit, as the results depended on model and method used (Hauser et al., 2017). In the Mediterranean region, a human contribution was found in the case of the 2014 meteorological drought in the southern Levant based on a single-model study (Bergaoui et al., 2015). In Africa, there is some evidence of a contribution of human emissions to single meteorological drought events, such as the 2015–2017 southern African drought (Funk et al., 2018a; Yuan et al., 2018a; Pascale et al., 2020), and the three-year (2015–2017) drought in the western Cape Town region of South Africa (Otto et al., 2018c). An attributable signal was not found in droughts that occurred in different years with different spatial extents in the last decade in North and South Eastern Africa (Marthews et al., 2015; Uhe et al., 2017; Otto et al., 2018a; Philip et al., 2018b; Kew et al., 2021). However, an attributable increase in 2011 long rain failure was identified (Lott et al., 2013). Further studies have attributed some African meteorological drought events to large-scale modes of variability, such as the strong 2015 El Niño (Box 11.4; Philip et al., 2018b) and increased SSTs overall (Funk et al., 2015a, 2018b). Natural variability was dominant in the California droughts of 2011–2012 to 2013–2014 (Seager et al., 2015a). In Asia, no climate change signal was found in the record dry spell over Singapore and Malaysia in 2014 (Mcbride et al., 2015) or the drought in central south-west Asia in 2013–2014 (Barlow and Hoell, 2015). Nevertheless, the South East Asia drought of 2015 has been attributed to anthropogenic warming effects (Shiogama et al., 2020). Recent droughts occurring in South America, specifically in the southern Amazon region in 2010 (Shiogama et al., 2013) and in north-east South America in 2014 (Otto et al., 2015b) and 2016 (Martins et al., 2018) were not attributed to anthropogenic climate change. Nevertheless, the central Chile drought between 2010 and 2018 has been suggested to be partly associated to global warming (Boisier et al., 2016; Garreaud et al., 2020). The 2013 New Zealand meteorological drought was attributed to human influence by Harrington et al. (2014, 2016) based on fully coupled CMIP5 models, but no corresponding change in the dry end of simulated precipitation from a stand-alone atmospheric model was found by Angélil et al. (2017).

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Event attribution studies also highlight a complex interplay of anthropogenic and non-anthropogenic climatological factors for some events. For example, anthropogenic warming contributed to the 2014 drought in North Eastern Africa by increasing east African and west Pacific temperatures, and increasing the gradient between standardized western and central Pacific SSTs, causing reduced rainfall (Funk et al., 2015a). As different methodologies, models and data sources have been used for the attribution of precipitation deficits, Angélil et al. (2017) re-examined several events using a single analytical approach and climate model and observational datasets. Their results showed a disagreement in the original anthropogenic attribution in a number of precipitation deficit events, which increased uncertainty in the attribution of meteorological droughts events.

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There is a growing number of studies on the detection and attribution of long-term changes in soil moisture deficits. Mueller and Zhang (2016) concluded that anthropogenic forcing contributed significantly to soil moisture drying in the warm season in the Northern Hemisphere from 1951 to 2005 and also led to an increase in the land surface area affected by soil moisture deficits, which can be reproduced by CMIP5 models only if anthropogenic forcings are involved. Gu et al. (2019b) similarly identified a global-scale soil moisture drying tendency in land surface model data from the Global Land Data Assimilation System 2 over the time frame 1948–2005, which was attributed to anthropogenic forcing based on evaluation with CMIP5 models using optimal fingerprinting. Padrón et al. (2019) analysed long-term reconstructed and CMIP5 simulated dry season water availability, defined as precipitation minus ET (i.e., equivalent to soil moisture and runoff availability), also related to agricultural and ecological droughts. They found an intensification of dry-season precipitation minus evapotranspiration deficits over a predominant fraction of the land area in the last three decades, which can only be explained by anthropogenic forcing and is mostly related to increases in ET. Similarly, Williams et al. (2020) concluded that human-induced climate change contributed to the strong soil moisture deficits recorded in the last two decades in Western North America through VPD increases associated with higher air temperatures and lower air humidity. There are few studies analysing the attribution of particular episodes of soil moisture deficits to anthropogenic influence. Nevertheless, the available modelling studies coincide in supporting an anthropogenic attribution associated with more extreme temperatures, exacerbating AED and increasing ET, and thus depleting soil moisture, as observed in southern Europe in 2017 (García-Herrera et al., 2019) and in Australia in 2018 (Lewis et al., 2020) and 2019 (van Oldenborgh et al., 2021), the latter event having strong implications in the propagation of widespread megafires (Nolan et al., 2020).

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It is often difficult to separate the role of climate trends from changes in land use, water management and demand for changes in hydrological deficits, especially on a regional scale. However, a global study based on a recent multi-model experiment with global hydrological models and covering several AR6 regions suggests a dominant role of anthropogenic radiative forcing for trends in low, mean and high flows, while simulated effects of water and land management do not suffice to reproduce the observed spatial pattern of trends (Gudmundsson et al., 2021). Regional studies also suggest that climate trends have been dominant compared to land use and human water management for explaining trends in hydrological droughts in some regions, for instance in Ethiopia (Fenta et al., 2017), China (Xie et al., 2015), and North America for the Missouri and Colorado basins, as well as in California (Shukla et al., 2015; Udall and Overpeck, 2017; Ficklin et al., 2018; K. Xiao et al., 2018; Glas et al., 2019; Martin et al., 2020; Milly and Dunne, 2020).

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In other regions, the influence of human water uses can be more important to explain hydrological drought trends (Y. Liu et al., 2016; Mohammed and Scholz, 2016). There is medium confidence that human-induced climate change has contributed to an increase of hydrological droughts in the Mediterranean (Giuntoli et al., 2013; Vicente-Serrano et al., 2014; Gudmundsson et al., 2017), but also medium confidence that changes in land use and terrestrial water management contributed to these trends (Section 11.9; Teuling et al., 2019; Vicente-Serrano et al., 2019). A global study with a single hydrological model estimated that human water consumption has intensified the magnitude of hydrological droughts by 20–40% over the last 50 years, and that the human water use contribution to hydrological droughts was more important than climatic factors in the Mediterranean, and central USA, as well as in parts of Brazil (Wada et al., 2013). However, Gudmundsson et al. (2021) concluded that the contribution of human water use is smaller than that of anthropogenic climate change to explain spatial differences in the trends of low flows based on a multi-model analysis. There is still limited evidence and thus low confidence in assessing these trends at the scale of single regions, with few exceptions (Section 11.9).

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Different studies using atmospheric-based drought indices suggest an attributable anthropogenic signal, characterized by the increased frequency and severity of droughts (Cook et al., 2018), associated to increased AED (Section 11.6.4.2). The majority of studies are based on the PDSI-PM. Williams et al. (2015) and Griffin and Anchukaitis (2014) concluded that increased AED has had an increased contribution to drought severity over the last decades, and played a dominant role in the intensification of the 2012–2014 drought in California. The same temporal pattern and physical mechanism was stressed by Z. Li et al. (2017) in central Asia. Marvel et al. (2019) compared tree ring-based reconstructions of the PDSI-PM over the past millennium with PDSI-PM estimates based on output from CMIP5 models. The comparisons suggested a contribution of greenhouse gas forcing to the changes since the beginning of the 20th century, although characterized with temporal differences that could be driven by temporal variations in the aerosol forcing. This was in agreement with the dominant external forcings of aridification at global scale between 1950 and 2014 (Bonfils et al., 2020). In the Mediterranean region, there is medium confidence of drying attributable to antropogenic forcing as a consequence of the strong AED increase (Gocic and Trajkovic, 2014; Azorin-Molina et al., 2015; Liuzzo et al., 2016; Maček et al., 2018), which has enhanced the severity of drought events (Vicente-Serrano et al., 2014; Stagge et al., 2017; González-Hidalgo et al., 2018). In particular, this effect was identified to be the main driver of the intensification of the 2017 drought that affected south-western Europe, and was attributed to the human forcing (García-Herrera et al., 2019). Nangombe et al. (2020) and L. Zhang et al. (2020) concluded from differences between precipitation and AED that anthropogenic forcing contributed to the 2018 droughts that affected southern Africa and south-eastern China, respectively, principally as consequence of the high AED that characterized these two events.

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The SREX (Chapter 3) concluded that there is low confidence in observed long-term (40 years or more) trends in TC intensity, frequency, and duration, and any observed trends in phenomena such as tornadoes and hail; it is likely that extratropical storm tracks have shifted poleward in both the Northern and Southern Hemispheres, and that heavy rainfalls and mean maximum wind speeds associated with TCs will increase with continued greenhouse gas warming; it is likely that the global frequency of TCs will either decrease or remain essentially unchanged, while it is more likely than not that the frequency of the most intense storms will increase substantially in some ocean basins; there is low confidence in projections of small-scale phenomena such as tornadoes and hail storms; and there is medium confidence that there will be a reduced frequency and a poleward shift of mid-latitude cyclones due to future anthropogenic climate change.

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Since SREX, several IPCC Reports also assessed storms. The AR5 (Chapter 2, Hartmann et al., 2013) assessment observed with low confidence long-term trends in TC metrics, but revised the statement from SREX to state that it is virtually certain that there are increasing trends in North Atlantic TC activity since the 1970s, with medium confidence that anthropogenic aerosol forcing has contributed to these trends. The AR5 concluded that it is likely that TC precipitation and mean intensity will increase and more likely than not that the frequency of the strongest storms will increase with continued greenhouse gas warming. confidence in projected trends in overall TC frequency remained low. confidence in observed and projected trends in hail storm and tornado events also remained low. The SROCC (Chapter 6, Collins et al., 2019) assessed past and projected TCs and ETCs, supporting the AR5 conclusions with some additional detail. Literature subsequent to AR5 adds support to the likelihood of increasing trends in TC intensity, precipitation, and frequency of the most intense storms, while some newer studies have added uncertainty to projected trends in overall frequency. A growing body of literature since AR5 on the poleward migration of TCs led to a new assessment in SROCC of low confidence that the migration in the western North Pacific represents a detectable climate change contribution from anthropogenic forcing. The SR1.5 (Chapter 3, Hoegh-Guldberg et al., 2018) essentially confirmed the AR5 assessment of TCs and ETCs, adding that heavy precipitation associated with TCs is projected to be higher at 2°C compared to 1.5°C global warming (medium confidence).

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The genesis, development, and tracks of TCs depend on conditions of the larger-scale circulations of the atmosphere and ocean (Christensen et al., 2013). Large-scale atmospheric circulations, such as the Hadley and Walker circulations and the monsoon circulations can significantly affect TCs, as can internal variability acting on various time scales (Annex IV), from intra-seasonal (e.g., the Madden–Julian and Boreal Summer Intraseasonal oscillations and equatorial waves) and interannual (e.g., the El Niño–Southern Oscillation and Pacific and Atlantic Meridional Modes), to inter-decadal (e.g., Atlantic Multidecadal Variability and Pacific Decadal Variability). This broad range of natural variability makes detection of anthropogenic effects difficult, and uncertainties in the projected changes of these modes of variability increase uncertainty in the projected changes in TC activity. Aerosol forcing also affects sea surface temperature (SST) patterns and cloud microphysics, and it is likely that observed changes in TC activity are partly caused by changes in aerosol forcing (Evan et al., 2011; Ting et al., 2015; Sobel et al., 2016, 2019; Takahashi et al., 2017; Zhao et al., 2018; Reed et al., 2019). Among possible changes from these drivers, there is medium confidence that the Hadley cell has widened and will continue to widen in the future (Sections 2.3, 3.3 and 4.5). This likely causes latitudinal shifts of TC tracks (Sharmila and Walsh, 2018). Regional TC activity changes are also strongly affected by projected changes in SST warming patterns (Yoshida et al., 2017), which are highly uncertain (Chapters 4 and 9).

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There is general agreement in the literature that anthropogenic greenhouse gases and aerosols have measurably affected observed oceanic and atmospheric variability in TC-prone regions (see Chapter 3). This underpinned the SROCC assessment of medium confidence that humans have contributed to the observed increase in Atlantic hurricane activity since the 1970s (Chapter 5, Bindoff et al., 2013). Literature subsequent to AR5 lends further support to this statement (Knutson et al., 2019). However, there is still no consensus on the relative magnitude of human and natural influences on past changes in Atlantic hurricane activity, and particularly on which factor has dominated the observed increase (Ting et al., 2015) and it remains uncertain whether past changes in Atlantic TC activity are outside the range of natural variability. A recent result using high-resolution dynamical model experiments suggested that the observed spatial contrast in TC trends cannot be explained only by multi-decadal natural variability, and that external forcing plays an important role (Murakami et al., 2020).Observational evidence for significant global increases in the proportion of major TC intensities (Kossin et al., 2020) is consistent with both theory and numerical modelling simulations, which generally indicate an increase in mean TC peak intensity and the proportion of very intense TCs in a warming world (Knutson et al., 2015, 2020; Walsh et al., 2015, 2016). In addition, high-resolution coupled model simulations provide support that natural variability alone is unlikely to explain the magnitude of the observed increase in TC intensification rates and upward TC intensity trend in the Atlantic basin since the early 1980s (Bhatia et al., 2019; Murakami et al., 2020).

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The cause of the observed slowdown in TC translation speed is not yet clear. Yamaguchi et al. (2020) used large ensemble simulations to argue that part of the slowdown is due to actual latitudinal shifts of TC tracks, rather than data artefacts, in addition to atmospheric circulation changes. G. Zhang et al. (2020) used large ensemble simulations to show that anthropogenic forcing can lead to a robust slowdown, particularly outside of the tropics at higher latitudes. Yamaguchi and Maeda (2020b) found a significant slowdown in the western North Pacific over the past 40 years and attributed the slowdown to a combination of natural variability and global warming. The slowing trend since 1900 over the USA is robust and significant after removing multi-decadal variability from the time series (Kossin, 2019). Among the hypotheses discussed is the physical linkage between warming and slowing circulation (Held and Soden, 2006; see also Section 8.2.2.2), with expectations of Arctic amplification and weakening circulation patterns through weakening meridional temperature gradients (Coumou et al., 2018; see also Cross-Chapter Box 10.1), or through changes in planetary wave dynamics (Mann et al., 2017). The tropics expansion and the poleward shift of the mid-latitude westerlies associated with warming is also suggested as the reason of the slowdown (G. Zhang et al., 2020). However, the connection of these mechanisms to the slowdown has not been robustly shown. Furthermore, slowing trends have not been unambiguously observed in circulation patterns that steer TCs, such as the Walker and Hadley circulations (Section 2.3.1.4), although these circulations generally slow down in numerical simulations under global warming (Sections 4.5.1.6 and 8.4.2.2).

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The observed poleward trend in western North Pacific TCs remains significant after accounting for the known modes of dominant interannual to decadal variability in the region (Kossin et al., 2016a), and is also found in CMIP5 model-simulated TCs (in the recent historical period 1980–2005), although it is weaker than observed and is not statistically significant (Kossin et al., 2016a). However, the trend is significant in 21st-century CMIP5 projections under the RCP8.5 scenario, with a similar spatial pattern and magnitude to the past observed changes in that basin over the period 1945–2016, supporting a possible anthropogenic greenhouse gas contribution to the observed trends (Kossin et al., 2016a; Knutson et al., 2019).

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The recent active TC seasons in some basins have been studied to determine whether there is anthropogenic influence. For 2015, Murakami et al. (2017b) explored the unusually high TC frequency near Hawaii and in the eastern Pacific basin. W. Zhang et al. (2016b) considered unusually high Accumulated Cyclone Energy (ACE) in the western North Pacific; and S.-H. Yang et al. (2018) and Yamada et al. (2019) looked at TC intensification in the western North Pacific. These studies suggest that the anomalous TC activity in 2015 was not solely explained by the effect of an extreme El Niño (see Box 11.4) and that there was also an anthropogenic contribution, mainly through the effects of SSTs in subtropical regions. In the post-monsoon seasons of 2014 and 2015, tropical storms with lifetime maximum winds greater than 46 m s−1 were first observed over the Arabian Sea, and Murakami et al. (2017a) showed that the probability of late-season severe tropical storms is increased by anthropogenic forcing compared to the preindustrial era. Murakami et al. (2018) concluded that the active 2017 Atlantic hurricane season was mainly caused by pronounced SSTs in the tropical North Atlantic and that these types of seasonal events will intensify with projected anthropogenic forcing. The trans-basin SST change, which might be driven by anthropogenic aerosol forcing, also affects TC activity. Takahashi et al. (2017) suggested that a decrease in sulphate aerosol emissions caused about half of the observed decreasing trends in TC genesis frequency in the south-eastern region of the western North Pacific during 1992–2011.

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Event attribution is used in TC case studies to test whether the severities of recent intense TCs are explained without anthropogenic effects. In a case study of Hurricane Sandy (2012), Lackmann (2015) found no statistically significant impact of anthropogenic climate change on storm intensity, while projections in a warmer world showed significant strengthening. However, Magnusson et al. (2014) found that, in European Centre for Medium-Range Weather Forecast (ECMWF) simulations, the simulated cyclone depth and intensity, as well as precipitation, were larger when the model was driven by the warmer actual SSTs than the climatological average SSTs. In Super Typhoon Haiyan, which struck the Philippines on 8 November 2013, Takayabu et al. (2015) took an event attribution approach with cloud system-resolving (around 1 km) downscaling ensemble experiments to evaluate the anthropogenic effect on typhoons, and showed that the intensity of the simulated worst-case storm in the actual conditions was stronger than that in a hypothetical condition without historical anthropogenic forcing in the model. However, in a similar approach with two coarser parametrized convection models, Wehner et al. (2019) found conflicting human influences on Haiyan’s intensity. Patricola and Wehner (2018) found little evidence of an attributable change in intensity of hurricanes Katrina (2005), Irma (2017), and Maria (2017) using a regional climate model configured between 3 km and 4.5 km resolution. They did, however, find attributable increases in heavy precipitation totals. These results imply that higher resolution, such as in a convective permitting 5 km or less mesh model, is required to obtain a robust anthropogenic intensification of a strong TC by simulating realistic rapid intensification (Kanada and Wada, 2016; Kanada et al., 2017a), and that whether the TC intensification can be attributed to the recent warming depends on the case.

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The dominant factor in the extreme rainfall amounts during Hurricane Harvey’s passage onto the USA in 2017 was its slow translation speed. But studies published after the event have argued that anthropogenic climate change contributed to an increase in rain rate, which compounded the extreme local rainfall caused by the slow translation. Emanuel (2017) used a large set of synthetically-generated storms and concluded that the occurrence of extreme rainfall as observed in Harvey was substantially enhanced by anthropogenic changes to the larger-scale ocean and atmosphere characteristics; Trenberth et al. (2018) linked Harvey’s rainfall totals to the anomalously large ocean heat content from the Gulf of Mexico; and van Oldenborgh et al. (2017) and Risser and Wehner (2017) applied extreme value analysis to extreme rainfall records in the Houston, Texas region, both attributing large increases to climate change. Large precipitation increases during Harvey due to global warming were also found using climate models (van Oldenborgh et al., 2017; S.-Y.S. Wang et al., 2018). Harvey precipitation totals were estimated in these papers to be three to 10 times more probable due to climate change. A best estimate from a regional climate and flood model is that urbanization increased the risk of the Harvey flooding by a factor of 21 (W. Zhang et al., 2018), using a regional climate and flood model, found that surface roughness from urbanization increased the risk of the Harvey flooding by a factor of 21. Anthropogenic effects on precipitation increases were also predicted in advance from a forecast model for Hurricane Florence in 2018 (Reed et al., 2020).

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In summary, it is very likely that the recent active TC seasons in the North Atlantic, the North Pacific, and Arabian basins cannot be explained without an anthropogenic influence. The anthropogenic influence on these changes is principally associated to aerosol forcing, with stronger contributions to the response in the North Atlantic. It is more likely than not that the slowdown of TC translation speed over the USA has contributions from anthropogenic forcing. It is likely that the poleward migration of TCs in the western North Pacific and the global increase in TC intensity rates cannot be explained entirely by natural variability. Event attribution studies of specific strong TCs provide limited evidence for anthropogenic effects on TC intensifications so far, but high confidence for increases in TC heavy precipitation. There is high confidence that anthropogenic climate change contributed to extreme rainfall amounts during Hurricane Harvey (2017) and other intense TCs.

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The spatial extent, or ‘size’, of the TC wind field is an important determinant of storm surge and damage. No detectable anthropogenic influences on TC size have been identified to date, because TCs in observations vary in size substantially (Chan and Chan, 2015) and there is no definite theory on what controls TC size, although this is an area of active research (Chavas and Emanuel, 2014; Chan and Chan, 2018). However, projections by high-resolution models indicate future broadening of TC wind fields when compared to TCs of the same categories (Yamada et al., 2017), while Knutson et al. (2015) simulate a reasonable interbasin distribution of TC size climatology, but project no statistically significant change in global average TC size. A plausible mechanism is that, as the tropopause height becomes higher with global warming, the eye wall areas become wider because the eye walls are inclined outward with height to the tropopause. This effect is only reproduced in high-resolution convection-permitting models capturing eye walls, and such modelling studies are not common. Moreover, the projected TC size changes are generally on the order of 10% or less, and these size changes are still highly variable between basins and studies. Thus, the projected change in both magnitude and sign of TC size is uncertain.

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The coastal effects of TCs depend on TC intensity, size, track, and translation speed. Projected increases in sea level, average TC intensity, and TC rainfall rates each generally act to further elevate future storm surge and fresh-water flooding (see Section 9.6.4.2). Changes in TC frequency could contribute toward increasing or decreasing future storm surge risk, depending on the net effects of changes in weaker vs stronger storms. Several studies (McInnes et al., 2014, 2016; Little et al., 2015; Garner et al., 2017; Timmermans et al., 2017, 2018) have explored future projections of storm surge in the context of anthropogenic climate change with the influence of both sea level rise and future TC changes. Garner et al. (2017) investigated the near-future changes in the New York City coastal flood hazard, and suggested a small change in storm-surge height because effects of TC intensification are compensated by the offshore shifts in TC tracks, but concluded that the overall effect due to the rising sea levels would increase the flood hazard. Future projection studies of storm surge in East Asia, including China, Japan and Korea, also indicate that storm surges due to TCs become more severe (J.A. Yang et al., 2018; Mori et al., 2019, 2021; J. Chen et al., 2020b). For the Pacific Islands, McInnes et al. (2014) found that the future projected increase in storm surge in Fiji is dominated by sea level rise, and projected TC changes make only a minor contribution. Among various storm surge factors, there is high confidence that sea level rise will lead to a higher possibility of extreme coastal water levels in most regions, with all other factors assumed equal.

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The probability of co-occurring meteorological droughts and heatwaves has increased in the observational period in many regions and will continue to do so under unabated warming (Herrera-Estrada and Sheffield, 2017; Zscheischler and Seneviratne, 2017; Hao et al., 2018; Sarhadi et al., 2018; Alizadeh et al., 2020; Wu et al., 2021). Overall, projections of increases in co-occurring drought and heatwaves are reported in northern Eurasia (Schubert et al., 2014), Europe (Orth et al., 2016a; Sedlmeier et al., 2018), south-east Australia (Kirono et al., 2017), multiple regions of the USA (Diffenbaugh et al., 2015; Herrera-Estrada and Sheffield, 2017), north-west China (X. Li et al., 2019; Kong et al., 2020) and India (Sharma and Mujumdar, 2017). The dominant signal is related to the increase in heatwave occurrence, which has been attributed to anthropogenic forcing (Section 11.3.4). This means that, even if drought occurrence is unaffected, compound hot and dry events will be more frequent (Sarhadi et al., 2018; Yu and Zhai, 2020).

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Drought and heatwaves are also associated with fire weather, related through high temperatures, low soil moisture, and low humidity. Fire weather refers to weather conditions conducive to triggering and sustaining wildfires, which generally include temperature, soil moisture, humidity, and wind (Chapter 12). Concurrent hot and dry conditions amplify conditions that promote wildfires (Schubert et al., 2014; Littell et al., 2016; Dowdy, 2018; Hope et al., 2019). Burnt area extent in western USA forests (Abatzoglou and Williams, 2016) and particularly in California (Williams et al., 2019) has been linked to anthropogenic climate change via a significant increase in vapour pressure deficit, a primary driver of wildfires. A study of the western USA examined the correlation between historical water-balance deficits and annual area burned, across a range of vegetation types, from temperate rainforest to desert (McKenzie and Littell, 2017). The relationship between temperature and dryness, and wildfire, varied with ecosystem type, and the fire–climate relationship was nonstationary and vegetation-dependent. In many fire-prone regions, such as the Mediterranean and China’s Daxing’anling region, projections for increased severity of future drought and heatwaves may lead to an increased frequency of wildfires relative to observed climatology (Tian et al., 2017; Ruffault et al., 2018). Observations show a long-term trend towards more dangerous weather conditions for bushfires in many regions of Australia, which is attributable (at least in part) to anthropogenic climate change (Dowdy, 2018). There is emerging evidence that recent regional surges in wildland fires are being driven by changing weather extremes (Cross-Chapter Box 3; Jia et al., 2019; SRCCL Chapter 2). Between 1979 and 2013, the global burnable area affected by long fire weather seasons doubled, and the mean length of the fire weather season increased by 19% (Jolly et al., 2015). However, at the global scale, the total burned area has been decreasing between 1998 and 2015 due to human activities mostly related to changes in land use (Andela et al., 2017). Given the projected high confidence increase in compound hot and dry conditions, there is high confidence that fire weather conditions will become more frequent at higher levels of global warming in some regions. This assessment is also consistent with Chapter 12’s examination of regional projected changes in fire weather. The SRCCL (Chapter 2) assessed with high confidence that future climate variability is expected to enhance the risk and severity of wildfires in many biomes such as tropical rainforests.

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El Niño–Southern Oscillation (ENSO) is one of the phenomena that have the ability to bring multitudes of extremes in different parts of the world, especially in extreme El Niño (Annex IV.2.3) cases. Additionally, the background climate warming associated with greenhouse gas forcing can significantly exacerbate extremes in parts of the world, even under normal El Niño conditions. The 2015–2016 extreme El Niño event was one of the three extreme El Niño events since the 1980s and the availability of satellite rainfall observations. According to some measures, it was the strongest El Niño in the past 145 years (Barnard et al., 2017). The 2015–2016 warmth was unprecedented at the central equatorial Pacific (Niño4: 5°N–5°S, 150°E–150°W), and this exceptional warmth was unlikely to have occurred entirely naturally, appearing to reflect an anthropogenically forced trend (Newman et al., 2018). In particular, its signal was seen in very high monthly global mean surface temperature (GMST) values in late 2015 and early 2016, contributing to the highest record of GMST in 2016 (Section 2.3.1.1). Both the ENSO amplitude and the frequency of high-magnitude events since 1950 is higher than over the pre-industrial period (medium confidence) (Section 2.4.2), suggesting that global extremes similar to those associated with the 2015–2016 extreme El Niño would occur more frequently under further increases in global warming. A brief summary of extreme events that happened in 2015–2016 is provided in Sections 6.2.2 and 6.5.1.1 of the Special Report on the Ocean and Cryosphere in a Changing Climate (SROCC). We provide some highlights illustrating extremes that occurred in different parts of the world during the 2015–2016 extreme El Niño in Box 11.4, Table 1, as well as in the short summary that follows.

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Several regions were strongly affected by droughts in 2015, including Indonesia, Australia, the Amazon region, Ethiopia, southern Africa, and Europe. As a result, global measurements of land water anomalies were particularly low in that year (Humphrey et al., 2018). In 2015, Indonesia experienced a severe drought and forest fire, causing pronounced impact on economy, ecology and human health due to haze crisis(Fieldet al. , 2016; Huijnenet al. , 2016; Patraet al. , 2017; Hartmannet al. , 2018). The northern part of Australia experienced high temperatures and low precipitation between late 2015 and early 2016, and the extensive mangrove trees were damaged along the Gulf of Carpentaria in Northern Australia (Duke et al., 2017). The Amazon region experienced the most intense droughts of this century in 2015–2016. This drought was more severe than the previous major droughts that occurred in the Amazon in 2005 and 2010 (Lewis et al., 2011; Erfanian et al., 2017; Panisset et al., 2018). The 2015–2016 Amazon drought impacted the entirety of South America north of 20°S during the austral spring and summer (Erfanian et al., 2017). It also increased forest fire incidence by 36% compared to the preceding 12 years (Aragão et al., 2018) and, as a consequence, increased the biomass burning outbreaks and the carbon monoxide (CO) concentration in the area, affecting air quality (Ribeiro et al., 2018). This out-of-season drought affected the water availability for human consumption and agricultural irrigation. It also left rivers with very low water levels and large sandbanks, preventing ship transportation of food, medicines, and fuels (INMET, 2017). Eastern African countries were impacted by drought in 2015. The drought in Ethiopia was the worst in several decades and was associated with the 2015–2016 extreme El Niño (Blunden and Arndt, 2016; Philip et al., 2018b). It was suggested that anthropogenic warming contributed to the 2015 Ethiopian and southern African droughts by increasing SSTs and local air temperatures (Funk et al., 2016, 2018b; Yuan et al., 2018a). It has also been suggested that the 2015–2016 extreme El Niño affected circulation patterns in Europe during the 2015–2016 winter (Geng et al., 2017; Scaife et al., 2017).

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In 2015, tropical cyclone activity was notably high in the North Pacific (Blunden and Arndt, 2016). Over the western North Pacific, there were 13 Category 4 and 5 tropical cyclones (TCs), more than twice the area’s typical annual value of 6.3 (W. Zhang et al., 2016b). Similarly, a record-breaking number of TCs were observed in the eastern North Pacific, particularly in the western part of that domain (Collins et al., 2016; Murakami et al., 2017b). These extraordinary TC activities were related to the average SST anomaly during that year, which were associated with the 2015–2016 extreme El Niño and the positive phase of the Pacific Meridional Mode (Murakami et al., 2017b; Hong et al., 2018; Yamada et al., 2019). However, it has been suggested that the intense TC activities in both the western and the eastern North Pacific in 2015 were not only due to the El Niño, but also to a contribution of anthropogenic forcing (Murakami et al., 2017b; S.-H. Yang et al., 2018). The impact of the Indian Ocean SST was also suggested to contribute to the extreme TC activity in the western North Pacific in 2015 (Zhan et al., 2018). In contrast, in Australia, it was the least active TC season since satellite records began in 1969–1970 (Blunden and Arndt, 2017).

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This precipitation event and the subsequent heatwave are related to abnormal condition of the jet stream and North Pacific Subtropical High in this month (Shimpo et al., 2019; Ren et al., 2020), which caused extreme conditions from Europe, Eurasia, and North America (Box 11.4, Figure 2; Kornhuber et al., 2019). A combination of the positive anomaly of the North Atlantic Oscillation (NAO, Annex IV.2.1) and the meandering jets is necessary to explain the pattern of the observed anomalies (Drouard et al. , 2019). A role of Atlantic SST anomaly on the meandering jets and the subtropical high have been suggested (B. Liu et al., 2019). These dynamic and thermodynamic components generally have substantial influence on extreme rainfall in East Asia (Oh et al., 2018), but it is under investigation whether these factors were due to anthropogenic forcing.

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Regarding the hot extremes that occurred across the Northern Hemisphere in the 2018 boreal May–July period, Vogel et al. (2019) found that the event was unprecedented in terms of the total area affected by hot extremes (on average, about 22% of populated and agricultural areas in the Northern Hemisphere) for that period, but was consistent with a +1°C climate which was the estimated global mean temperature anomaly around that time (for 2017; SR1.5). This study also found that events similar to the 2018 May–July temperature extremes would approximately occur two out of three years under +1.5°C of global warming, and every year under +2°C of global warming. Imada et al. (2019) also suggest that the mean annual occurrence of extreme hot days in Japan will be expected to increase by 1.8 times under a global warming level of 2°C above pre-industrial levels. Kawase et al. (2020) showed that the extreme rainfall in Japan during this event was increased by approximately 7% due to recent rapid warming around Japan. Imada et al. (2020) showed that the probability of the Heavy Rain Event of July 2018 in Japan was increased from 0.22% to 2.00% due to anthropogenic warming. Hence, it is virtually certain that these 2018 concurrent events would not have occurred without human-induced global warming. Concurrent events of this type are also projected to happen more frequently under higher levels of global warming. However, there is currentlylow confidence in projected changes in the frequency or strength of the anomalous circulation patterns leading to concurrent extremes (e.g., Cross-Chapter Box 10.1).

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van Oldenborgh, G.J. et al., 2018: Extreme heat in India and anthropogenic climate change. Natural Hazards and Earth System Sciences, 18(1), 365–381, doi: 10.5194/nhess-18-365-2018.

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van Oldenborgh, G.J. et al., 2021: Attribution of the Australian bushfire risk to anthropogenic climate change. Natural Hazards and Earth System Sciences, 21(3), 941–960, doi: 10.5194/nhess-21-941-2021.

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Wang, Y., K.-H. Lee, Y. Lin, M. Levy, and R. Zhang, 2014: Distinct effects of anthropogenic aerosols on tropical cyclones. Nature Climate Change, 4, 368, doi: 10.1038/nclimate2144.

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Wehner, M.F. et al., 2018c: Early 21st century anthropogenic changes in extremely hot days as simulated by the C20C+ detection and attribution multi-model ensemble. Weather and Climate Extremes, 20, 1–8, doi: 10.1016/j.wace.2018.03.001.

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Williams, A.P. et al., 2015: Contribution of anthropogenic warming to California drought during 2012–2014. Geophysical Research Letters, 42(16), 6819–6828, doi: 10.1002/2015gl064924.

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Williams, A.P. et al., 2019: Observed Impacts of Anthropogenic Climate Change on Wildfire in California. Earth’s Future, 7(8), 892–910, doi: 10.1029/2019ef001210.

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Williams, A.P. et al., 2020: Large contribution from anthropogenic warming to an emerging North American megadrought. Science, 368(6488), 314–318, doi: 10.1126/science.aaz9600.

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Yin, H. and Y. Sun, 2018: Detection of Anthropogenic Influence on Fixed Threshold Indices of Extreme Temperature. Journal of Climate, 31(16), 6341–6352, doi: 10.1175/jcli-d-17-0853.1.

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Yin, H., Y. Sun, H. Wan, X. Zhang, and C. Lu, 2017: Detection of anthropogenic influence on the intensity of extreme temperatures in China. International Journal of Climatology, 37(3), 1229–1237, doi: 10.1002/joc.4771.

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Yuan, X., L. Wang, and E.F. Wood, 2018a: Anthropogenic Intensification of Southern African Flash Droughts as Exemplified by the 2015/16 Season [in “Explaining Extreme Events of 2016 from a Climate Perspective”]. Bulletin of the American Meteorological Society, 99(1), S86–S90, doi: 10.1175/bams-d-17-0077.1.

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Zhang, W. et al., 2016b: Influences of Natural Variability and Anthropogenic Forcing on the Extreme 2015 Accumulated Cyclone Energy in the Western North Pacific. Bulletin of the American Meteorological Society, 97(12), S131–S135, doi: 10.1175/bams-d-16-0146.1.

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Zhang, W. et al., 2020: Anthropogenic Influence on 2018 Summer Persistent Heavy Rainfall in Central Western China. Bulletin of the American Meteorological Society, 101(1), S65–S70, doi: 10.1175/bams-d-19-0147.1.

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Freshwater is the most essential natural resource on the planet (Mekonnen and Hoekstra, 2016; Djehdian et al., 2019) and underpins almost all Sustainable Development Goals (SDGs), which require access to adequate and safe resources for drinking and sanitation (SDG 6) and many other purposes. Freshwater supports a range of human activities from irrigation to industrial processes including the generation of hydro-electricity and the cooling of thermoelectric power plants (Bates et al., 2008; Schewe et al., 2014). These activities require sufficient quantities of freshwater that can be drawn from rivers, lakes, groundwater stores, and in some cases, desalinated sea water (Schewe et al., 2014). Recent estimates of global water pools and fluxes suggest that half of global river discharge is redistributed each year by human water use (Abbott et al., 2019). This emphasizes the need to consider both anthropogenic climate change and direct human influences, such as population increase or migration, economic development, urbanization, and land use change, when planning water-related mitigation or adaptation strategies (Jiménez Cisneros et al., 2014).

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Understanding the interactions between the water and energy cycles is one of the four core projects of the World Climate Research Programme (WCRP). Latent heat fluxes, released by condensation of atmospheric water vapour and absorbed by evaporative processes, are critical to driving the circulation of the atmosphere on scales ranging from individual thunderstorm cells to the global circulation of the atmosphere (Stocker et al., 2013; Miralles et al., 2019). Water vapour is the most important gaseous absorber in the Earth’s atmosphere, playing a key role in the Earth’s radiative budget (Schneider et al., 2010). As atmospheric water vapour content increases with temperature, it has a considerable influence on climate change (Section 7.4.2.2). Additionally, a small fraction of the atmospheric water content is liquid or solid and has a major effect on both solar and longwave radiative fluxes, from the Earth’s surface to the top of the atmosphere. The cloud response to anthropogenic radiative forcings, both in the tropics and in the extratropics (Zelinka et al., 2020), is therefore also crucial for understanding climate change (Section 7.4.2.4).

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Based on long-term observational evidence (Hartmann et al., 2013), AR5 concluded it was likely that anthropogenic influence has affected the water cycle since the 1960s (IPCC, 2018). Detectable human influ­ence on changes to the water cycle were found in atmospheric moisture content (medium confidence), global-scale changes of precipitation over land (medium confidence), intensification of heavy precipitation events over land regions where sufficient data networks exist (medium confidence), and very likely changes to ocean salinity through its connection with evaporation minus precipitation change patterns (Sections 2.5, 2.6, 3.3, 7.6, 10.3 and 10.4; Stocker et al., 2013). The AR5 also reported that it is very likely that global surface air specific humidity increased since the 1970s. There was low confidence in the observations of global-scale cloud variability and trends, medium confidence in reductions of pan-evaporation, and medium confidence in the non-monotonic changes of global evapotranspiration since the 1980s. In terms of streamflow and runoff, AR5 identified that there is low confidence in the observed increasing trends of global river discharge during the 20th century. Similarly, AR5 concluded that there is low confidence in any global-scale observed trend in drought or dryness (lack of rainfall) since the mid-20th century. Yet, the frequency and intensity of drought likely increased in the Mediterranean and West Africa, while theylikely decreased in central North America and north-western Australia since 1950.

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The SR1.5 assessed the impacts of global warming of 1.5°C above pre-industrial levels. The dominant human influence on observed global warming and related water cycle changes was confirmed. Further evidence that anthropogenic global warming has caused an increase in the frequency, intensity and/or amount of heavy precipitation events at the global scale (medium confidence), as well as in drought occurrence in the Mediterranean region (medium confidence) was also reported. Chapter 3 of SR1.5 (Hoegh-Guldberg et al., 2018) highlights that each half degree of additional global warming influences the climate response. Heavy precipitation shows a global tendency to increase more at 2°C compared to 1.5°C, though there is low confidence in projected regional differences in heavy precipitation at 1.5°C compared to 2°C global warming, except at high latitudes or at high altitude where there is medium confidence. A key finding is that ‘limiting global warming to 1.5°C compared to 2°C would approximately halve the proportion of the world population expected to suffer water scarcity, although there is considerable variability between regions (medium confidence)’ (SR1.5). This is consistent with greater adverse impacts found at 2°C compared to 1.5°C for a number of dryness or drought indices (Schleussner et al., 2016; Lehner et al., 2017; Greve et al., 2018). There is also medium confidence that land areas with increased runoff and exposure to flood hazards will increase more at 2°C compared to 1.5°C of global warming.

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The AR5 report was a major step forward in the assessment of the human influence on the Earth’s water cycle, yet regional projections of precipitation and water resources often remained very uncertain for a range of reasons including modelling uncertainty and the large influence of internal variability (Sections 1.4.3 and 8.5.2; Hawkins and Sutton, 2011; Deser et al. , 2012). Since AR5, longer and more homogeneous observational and reanalysis datasets have been produced along with new ensembles of historical simulations driven by all or individual anthropogenic forcings. These factors, together with improved detection-attribution tools, has enabled a more comprehensive assessment and a better understanding of recent observed water cycle changes, including the competing effects of GHGs and aerosol emissions. New paleoclimate reconstructions have been also developed, particularly from the SH, that were not available at the time of AR5. There have also been advances in modelling clouds, precipitation, surface fluxes, vegetation, snow, floodplains, groundwater and other processes relevant to the water cycle. Convection permitting and cloud-resolving models have been implemented over increasingly large domains and can be used as benchmarks for the evaluation of the current-generation climate models. The added value of increased resolution in global or regional climate models can be also assessed more thoroughly based on dedicated model intercomparison projects (Sections 10.3.3 and 8.5.1). Ongoing research activities on decadal predictions and observational constraints are aimed at narrowing the plausible range of near-term (20212040) to long-term (20812100) water cycle changes.

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This chapter assesses water cycle changes and considers climate change from the perspective of its effects on water availability (including streamflow and soil moisture, snow mass and glaciers, groundwater, wetlands and lakes) rather than only precipitation. The chapter highlights the sensitivity of the water cycle to multiple drivers and the complexity of its responses, depending on regions, seasons and time scales. Anthropogenic drivers include not only emissions of GHGs but also different species of aerosols, land and water management practices. Emphasis is placed on assessing the full range of projections, including ‘low likelihood, high impact’ climate trajectories such as the potential for abrupt changes in the water cycle.

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Climate drivers that instantaneously affect the surface much more than the atmospheric energy budget (such as solar forcing and sulphate aerosol) produce only a small rapid adjustment of the global water cycle and therefore largerηathan drivers that immediately modulate the atmospheric energy budget such as GHGs and absorbing aerosol (Salzmann, 2016; Samset et al. , 2016; Lin et al. , 2018; F. Liu et al. , 2018). Thus, global precipitation appears more sensitive to radiative forcing from sulphate aerosols (2.8 ± 0.7% °C–1; ηaη) than GHGs (1.4 ± 0.5% °C–1; ηa< η) while the response to black carbon aerosol can be negative (3.5 ± 5.0% °C–1; ηa<< η) due to strong atmospheric solar absorption (Samset et al., 2016). Therefore, artificially reducing surface-absorbed sunlight through solar radiation modification strategies to mitigate GHG warming will not mitigate precipitation changes (see Sections 4.6.3.3, 6.4.7 and 8.6.3). Aerosol-induced precipitation changes depend upon the type of aerosol species and their spatial distribution. Global mean precipitation increases after complete removal of present-day anthropogenic aerosol emissions (see also Section 4.4.4) in four different climate models (ηa= 1.65.5% °C–1) are mainly attributed to sulphate aerosol as opposed to other aerosol species (Samset et al., 2018b). Idealized modelling studies show that sulphate aerosol increases over Europe produce a larger global precipitation response than an equivalent increase in aerosol burden or radiative forcing overAsia, explained by differences in cloud climatology and cloud-aerosol interaction (Kasoar et al., 2018; L. Liu et al., 2018). The vertical profiles of black carbon and ozone further influence the magnitude of the rapid global precipitation response, yet are difficult to observe and simulate (Allen and Landuyt, 2014; MacIntosh et al., 2016; Stjern et al., 2017; Sand et al., 2020).

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Hydrological sensitivity is generally lower over land but with a large uncertainty range (η=0.1 to 3.0% °C–1) relative to the oceans (η= 2.3 to 3.3% °C–1) based on multi-model 4 × CO2 CMIP6simulations (Pendergrass, 2020b), broadly consistent with comparable CMIP5 experiments (T.B. Richardson et al. , 2018a; Samset et al. , 2018a). Suppressed hydrological sensitivity over land (Figures 8.3d and 8.4) is associated with greater warming compared with the oceans, which alters atmospheric circulation and precipitation patterns (Saint-Lu et al., 2020). Also, since oceans supply much of the moisture to fuel precipitation over land, the slower ocean warming rate means there is insufficient moisture supplied to maintain continental relative humidity levels (Byrne and O’Gorman, 2018), which can inhibit convection (J. Chen et al., 2020a). Land surface feedbacks involving soil-vegetation-atmosphere coupling further drive continental drying (Berg et al., 2016; Kumar et al., 2016; Chandan and Peltier, 2020). The suppressed hydrological sensitivity is counteracted by rapid precipitation responses in most GHG-forced simulations, explained by increases in surface downward longwave radiation due to CO2 increases that rapidly warm the land, destabilize the troposphere and strengthen vertical motion in the short term (Chadwick et al., 2014; T.B. Richardson et al., 2016, 2018a). There is medium understanding of how land–sea warming contrast governs rapid precipitation responses based on idealized modelling that shows similar spatial patterns of precipitation response to radiative forcing from GHGs, solar forcing and absorbing aerosols (Xie et al., 2013; Samset et al., 2016; Kasoar et al., 2018). Rapid precipitation adjustments to CO2 have been counteracted by cooling from anthropogenic aerosol increases over land (Box 8.1) but this compensation is expected to diminish as aerosol forcing declines (T.B. Richardson et al., 2018a). Thefast and slow precipitation responses over global land combine on average during transient climate change (Figure 8.3d). This explains a consistent land and ocean mean precipitation increase in projections (Table 4.3) but this is determined by a complex and model-dependent evolution of continental water cycle changes over space and time.

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Increases in global precipitation over time, as the climate warms, are partly offset by the overall cooling effects of anthropogenic aerosol and by rapid atmospheric adjustments to increases in GHGs and absorbing aerosol. This explains why multi-decadal trends in global precipitation responses in the satellite era (Adler et al., 2017; Allan et al., 2020) are small and difficult to interpret given observational uncertainty, internal variability and volcanic forcings. The delayed warming effect of rising CO2 concentration, combined with declining aerosol cooling, are expected to increase the importance of the slow temperature-related effects on the energy budget relative to the more rapid direct radiative forcing effects as transient climate change progresses (Shine et al., 2015; Salzmann, 2016; Myhre et al., 2018b).

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Monsoons are key components of the tropical overturning circulation that can be understood as a balance between net energy input (e.g., radiative and turbulent fluxes) and the export of moist static energy. This is determined by contrasting surface heat capacity between ocean and land and modified through changes in atmospheric dynamics, tropical tropospheric stability and land surface properties (Jalihal et al., 2019). Thermodynamic increases in moisture transport are expected to increase monsoon strength and area (Christensen et al., 2013). Since AR5, evidence continues to demonstrate that monsoon circulation is sensitive to spatially varying radiative forcing by anthropogenic aerosols (Hwang et al. , 2013; R.J. Allen et al. , 2015; Z. Li et al. , 2016b) and GHGs (Dong andSutton, 2015). Changes in SST patterns also play a role (Guo et al., 2016; W. Zhou et al., 2019; Cao et al., 2020) by altering cross-equatorial energy transports and land–ocean temperature contrasts. This evidence continues to support a thermodynamic strengthening of monsoon precipitation that is partly offset by slowing of the tropical circulation but with weak evidence and low agreement for regional aspects of circulation changes. Disagreement between paleoclimate and modern observations, physical theory and numerical simulations of global monsoons have been partly reconciled (Section 3.3.3.2) through improved understanding of regional processes (Harrison et al. , 2015; R. Bhattacharya et al. , 2017; Bhattacharya et al. , 2018; Biasutti et al. , 2018; D’Agostino et al. , 2019; Jalihal et al. , 2019; Seth et al. , 2019), although interpreting past changes in the context of future projections requires careful account of differing forcings and feedbacks (D’Agostinoet al., 2019). Assessment of past changes and future projections in regional monsoons are provided in Sections 2.3.1.4.2, 8.3.2.4 and 8.4.2.4.

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Regional changes in aridity – broadly defined as a deficit of moisture – are expected to occur in response to anthropogenic forcings as a consequence of shifting precipitation patterns, warmer temperatures, changes in cloudiness (affecting solar radiation), declining snowpack, changes in winds and humidity, and vegetation cover (Figure 8.6). Evapotranspiration (see Annex VII: Glossary) is a key component of aridity, and is composed of two main processes: evaporation from soil, water and vegetation surfaces; and transpiration, the exchange of moisture between plants and atmosphere through plant stomata. On a global level, warmer temperatures increase evaporative demand in the atmosphere, and thus (assuming sufficient soil moisture is available) increase moisture loss from evapotranspiration (high confidence) (Dai et al., 2018; Vicente-Serrano et al., 2020). On a regional level, aridity is further modulated by seasonal rainfall patterns, runoff, water storage, and interactions with vegetation.

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The SRCCL assessed with medium confidence that mean and extreme precipitation is increased over and downwind of urban areas (Jia et al., 2019). There is medium confidence that altered thermodynamic and aerodynamic properties of the land surface from urbanization affects evaporation and increases precipitation over or downwind of cities (Box 10.3) due to altered stability and turbulence (Han et al. , 2014; Pathirana et al. , 2014; Jiang et al. , 2016; D’Odorico et al. , 2018; Sarangi et al. , 2018; Boyaj et al. , 2020). However, reduced biogenic aerosol, but increased anthropogenic aerosol emissions modify cloud microphysics and precipitation processes (Box 8.1; Schmidand Niyogi, 2017; D’Odorico et al. , 2018; Fan et al. , 2020; Zheng et al. , 2020). Urbanization also decreases permeability of the surface, leading to increased surface runoff (Chen et al., 2017; Jia et al., 2019). Large-scale infrastructure, such as the construction and operation of dikes, weirs, and hydropower plants, also alters surface energy and moisture fluxes, potentially influencing the regional water cycle. Limited modellingevidence suggests that large-scale solar and wind farms can increase precipitation locally (over the Sahel and North America) when dynamic vegetation responses are represented (Y. Li et al., 2018; Pryor et al., 2020), with remote effects also possible (Lu et al., 2021).

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Aerosols scatter and absorb solar radiation which reduces the energy available for surface evaporation and subsequent precipitation. In addition, cooling is incurred by the radiation that is reflected back to space directly by the aerosols and indirectly by the aerosol effect on cloud brightening. Northern Hemisphere (NH) station data indicate decreasing precipitation trends during the 1950s to the 1980s, which have since partially recovered (Wild, 2012; Bonfils et al., 2020). These changes are attributable with high confidence to anthropogenic aerosol emissions from North America and Europe causing dimming through reduced surface solar radiation. This peaked during the late-1970s and partially recovered thereafter following improved air quality regulations (Section 6.2.1; Box 8.1, Figure 1).

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Absorption of solar radiation by anthropogenic aerosols such as black carbon warms the lower troposphere and increases moist static energy, but also results in larger convection inhibition that suppresses light rainfall (Box 8.1, Figure 2; Y. Wang et al., 2013). Release of aerosol-induced instability, often triggered by topographical barriers, produces intense rainfall, flooding (Fan et al., 2015; Lee et al., 2016) and severe convective storms (medium confidence) (Saide et al., 2015). In particular, aerosols induce intense convection at the Himalaya foothills during the pre-monsoon season, which generates a regional convergence there (medium confidence). This mechanism is termed the ‘elevated heat pump hypothesis’ (Lau and Kim, 2006; D’Errico et al., 2015).

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Cloud droplets nucleate on pre-existing aerosol particles which act as cloud condensation nuclei (CCN). Anthropogenic aerosols add CCN, compared to a pristine background, and produce clouds with more numerous and smaller droplets, slower to coalesce into raindrops and to freeze into ice hydrometeors at temperatures below 0°C. Adding CCN suppresses light rainfall from shallow and short-lived clouds, but it is compensated by heavier rainfall from deep clouds. Adding aerosols to clouds in extremely clean air invigorates them by more efficient vapour condensation on the added drop surfaces (Koren et al., 2014; Fan et al., 2018). Clouds forming in more polluted air masses (hence with more numerous and smaller drops) need to grow deeper to initiate rain (Freud and Rosenfeld, 2012; Konwar et al., 2012; Campos Braga et al., 2017). This leads to larger amount of cloud water evaporating aloft while cooling and moistening the air there at the expense of the lower levels, which leads to convective invigoration (Dagan et al., 2017; Chua and Ming, 2020), followed by convergence, air mass destabilization and added rainfall in an amplifying feedback loop

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The flux of freshwater between the ocean and atmosphere is determined by the difference between precipitation and evaporation (P–E). Evaporation is measured in very few locations across the global ocean, so that directly assessing P–E over the ocean is very challenging and relies on indirect reanalysis estimates (Robertson et al., 2020). The AR5 presented robust evidence ofan amplifiedoceanic pattern in P–E since the 1960s from both regional and global surface and subsurface salinity measurements and reanalyses. This pattern is consistent with our theoretical understanding of human-induced changes in the water cycle, leading to the conclusion that these changes are very likely the result of anthropogenic forcings (Section 9.2.2.2).

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Global and regional changes in precipitation frequency and intensity have been observed over recent decades. An analysis of 1875 rain gauge records worldwide over the period 1961–2018 indicates that there has been a general increase in the probability of precipitation exceeding 50 mm day–1, mostly due to an overall boost in rain intensity (Benestad et al., 2019). Such changes in precipitation intensity and frequency have not been formally attributed to human activities, but are consistent with the heating effect of increasing CO2 levels on the distribution of daily precipitation rates (Section 8.2.3.2) and with a distinct overall intensification of heavy precipitation events found in both observations and CMIP5 models, though with an underestimated magnitude (Fischer and Knutti, 2014). Beyond amplified precipitation extremes (Section 11.4.2), CMIP5 models also indicate that anthropogenic forcings have increased temporal variability of annual precipitation amount over land from 1950 to 2005, which is most pronounced in annual mean daily precipitation intensity (Konapala et al., 2017).

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Anthropogenic aerosols can alter precipitation intensities both through radiative and microphysical effects (Box 8.1 and Section 8.5.1.1.2). Precipitation suppression through aerosol microphysical effects has been observed in shallow cloud regimes over South America and the south-eastern Atlantic, associated with local biomass burning (Andreae et al., 2004; Costantino and Bréon, 2010), and in industrial regions in Australia (Rosenfeld, 2000; Hewson et al., 2013; Heinzeller et al., 2016). In contrast, precipitation intensification through aerosol microphysical effects in deep convective clouds is seen in many regions such as the Amazon, southern USA, India, and Korea. This is associated with anthropogenic aerosols from cities (Hewson et al., 2013; Fan et al., 2018; S.S. Lee et al., 2018; Sarangi et al., 2018).

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Rainfall increases have been observed over northern Australia since the 1950s, with most of the increases occurring in the north-west (Dey et al., 2019a, b; Dai, 2021) and decreases observed in the north-east (J. Li et al., 2012) since the 1970s. In contrast, there has been a decline in rainfall over southern Australia related to changes in the intensification and position of the subtropical ridge (CSIRO and BoM, 2015) and anthropogenic effects (Knutson and Zeng, 2018). The drying trend over south-west Australia is most pronounced during May to July, where rainfall has declined by 20% below the 1900–1969 average since 1970 and by about 28% since 2000 (BoM and CSIRO, 2020).

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Over South America, there is observational and paleoclimate evidence of declining precipitation amount during the past 50 years over the Altiplano and central Chile, primarily explained by the PDO but with at least 25% of the decline attributed to anthropogenic influence (Morales et al., 2012; Neukom et al., 2015; Boisier et al., 2016; Seager et al., 2019b; Garreaud et al., 2020). In contrast, a significant rainfall increase has been detected over the Peruvian–Bolivian Altiplano (from observational data and satellite-based estimations) since the 1980s (Figure 8.7; Imfeld et al., 2020; Segura et al., 2020). Long-term (19022005) precipitation data indicate positive trends over south-eastern South America and negative trends over the southern Andes, with at least a partial contribution from anthropogenic forcing (Gonzalez et al., 2014; Vera and Díaz, 2015; Díaz and Vera, 2017; Boisier et al., 2018; Knutson and Zeng, 2018; see further assessment in Section 10.4.2.2 and Atlas.7.2.2). The Peruvian Amazon has exhibited significant rainfall decreases during the dry season since 1980 (Lavado et al., 2013; Ronchail et al., 2018). Increases in wet season rainfall in the northern and central Amazon since the 1980s and decreases during the dry season in the southern Amazon (Barreiro et al., 2014; Gloor et al., 2015; Martín-Gómez and Barreiro, 2016; J.C. Espinoza et al., 2018; X.Y. Wang et al., 2018; Haghtalab et al., 2020) are not explained by radiative forcing based on CMIP6 experiments (Figure 8.7) and trends are insignificant over longer periods since 1930 (Kumar et al., 2013)or more recently, since 1973 (Almeida et al., 2017). See (Section 8.3.2.4.5 for monsoon-related changes. For the tropical Andes region, trends in annual precipitation show heterogenous patterns, ranging between –4% per decade and +4% per decade in the northern and southern tropical Andes for a 30-year period at the end of the 20th century, although increases during 19651984 and decreases since 1984 have been registered in Bolivia (Carmona and Poveda, 2014; Pabón-Caicedo et al., 2020).

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Observed precipitation records since the early 1900s show increases in precipitation totals over central and north-eastern North America that are attributable to anthropogenic warming but larger in magnitude than found in CMIP5 simulations (Knutson and Zeng, 2018; Guo et al., 2019). Decreases in precipitation amount over the central and south-western USA and increases over the north-central USA during 19832015 (Cui et al., 2017; P. Nguyen et al., 2018), are not clearly associated with forced responses in CMIP6 simulations (Figure 8.7; see also Section 10.4.2.3). Over Europe, precipitation trends since 1979 do not show coherence across datasets (Zolina et al., 2014; P. Nguyen et al., 2018). Longer records since 1910 show increases for much of Scandinavia, north-western Russia, and parts of north-western Europe/United Kingdom and Iceland (Knutson and Zeng, 2018). Records since 1930 show increases of annual preciptation amount over western Russia (see also Atlas.8.2). Widespread increases in daily precipitation intensity appear clearly over regions with a high density of rain gauges, such as Europe and North America over the 19512014 period (Alexander, 2016). Observations during 19662016 over northern Eurasia show increases in the contribution of heavy convective showers to total precipitation by 12% on average (with local trends of up to 5%) for all seasons except for winter (Chernokulsky et al., 2019). Increases in convective precipitation intensity have been identified, particularly on sub-daily time scales, using a range of modelling and observational data (Berg et al., 2013; Kanemaru et al., 2017; Pfahl et al., 2017).

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In summary, regional changes in precipitation amounts can be obscured by the contrasting responses to GHG and aerosol forcings across much of the 20th century and can thus be dominated by internal variability at decadal to multi-decadal time scales (high confidence). There is, however, a detectable increase in northern high-latitude annual precipitation over land which has been primarily driven by human-induced global warming (high confidence) (Section 3.3.2). Human influence has strengthened the zonal mean precipitation contrast between the wet tropics and dry subtropics since the 1980s (medium confidence), although regional studies suggest a more complex precipitation response to evolving anthropogenic forcings. There is high confidence that daily mean precipitation intensities have increased since the mid-20th century in a majority of land regions with available observations and it is likely that such an increase is mainly due to GHG forcing (see Section 11.4). Section 8.3.2.4 assesses monsoon precipitation changes in detail.

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An increasing number of studies have identified signals of attribution in the recent observed trends in evapotranspiration. Douville et al. (2013) found that the post-1960 rise in evapotranspiration in both the mid-latitudes and northern high latitudes was related to anthropogenic radiative forcing. An analysis of CMIP5simulations suggests that anthropogenic forcing accounts for a large fraction of the global mean evapotranspiration trend from 1982 to 2010 (Dong and Dai, 2017). Padrón et al. (2020) determined that increases in evapotranspiration were responsible for the majority of the anthropogenic pattern in dry-season water availability that dominates global trends since 1984. These findings are further supported by CMIP6 model results (Figure 8.8) that show that the recent summer increase in evapotranspiration in the northern mid- and high latitudes is due to GHG forcing and decreasing anthropogenic aerosol emissions over Europe.

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In summary, there is high confidence that terrestrial evapotranspiration has increased since the 1980s. There is medium confidence that this trend is driven by both increasing atmospheric water demand and vegetation greening, and high confidence that it can be partly attributed to anthropogenic forcing. There is low confidence about the extent to which increases in plant water use efficiency have influenced observed changes in evapotranspiration.

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The AR5 reported low confidence in the assessment of trends in global river discharge during the 20th century. This is because many streamflow observations have been impacted by land use and dam construction, and the largest river basins worldwide differ in many characteristics, including geography and morphology. In regions with seasonal snow storage, AR5 WGII assessed that there is robust evidence and high agreement that warming has led to earlier spring discharge maxima and robust evidence of earlier breakup of Arctic river ice, as well as indications that warming has led to increased winter flows and decreased summer flows where streamflows are lower and that the observed increases in extreme precipitation led to greater probability of flooding at regional scales with medium confidence. The SROCC found robust evidence and high agreement that discharge due to melting glaciers has already reached its maximum point and has begun declining with smaller glaciers, but onlylow confidence that anthropogenic climate change has already affected the frequency and magnitude of floods at the global scale.

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Significant trends in streamflow and continental runoff were observed in 55 out of 200 large river basins during 19482012, with an even distribution of increasing and decreasing trends (Section 2.3.1.3.6; Dai, 2016). A global detection and attribution study shows that the simulation of spatially heterogeneous historical trends in streamflow is consistent with observed trends only if anthropogenic forcings are considered (Gudmundsson et al., 2019). Section 3.3.2.4 assesses with medium confidence that anthropogenic climate change has altered regional and local streamflows, although a significant trend has not been observed in the global average (Sections 2.3.1.3.6 and 3.3.2.3). Multiple human-induced and natural drivers have been shown to play an important but variable role in observed regional trends of streamflow for several different areas (Fenta et al. , 2017; Ficklin et al. , 2018; Glas et al. , 2019; Vicente-Serrano et al. , 2019). For instance, decreasing runoff during the dry season has been observed over the Peruvian Amazon since the 1980s (Lavado et al., 2013; Ronchail et al., 2018). Up to 30–50% of the recent multi-decadal decline in streamflow across the Colorado River Basin can be attributed to anthropogenic warming and its impacts on snow and evapotranspiration (Woodhouse et al., 2016; McCabe et al., 2017; Udall and Overpeck, 2017; Xiao et al., 2018; Milly and Dunne, 2020). In the Upper Missouri River basin, Martin et al. (2020) found that warming temperatures have contributed to streamflow reductions since at least the late 20th century. Cold regions in the NH have experienced an earlier occurrence of snowmelt floods, an overall increase in water availability and streamflow during winter, and a decrease in water availability and streamflow during the warm season (Aygün et al., 2019).

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Observed changes in flooding are assessed in Section 11.5.2 and are summarized as follows. For changes in the magnitude of peak flow, recent studies show strong spatial heterogeneity in the sign, size and significance of trends. For changes in timing of peak flows, recent studies further support observed changes in snowmelt-driven rivers. Observed changes in runoff and flood magnitude cannot be explained by precipitation changes alone given the possible season- and region-dependent decreases in antecedent soil moisture and snowmelt, which can partly offset the increase in precipitation intensity (Sharma et al., 2018), or the expected effect of urbanization and deforestation which can, on the contrary, amplify the runoff response (Chen et al., 2017; Abbott et al., 2019; Cavalcante et al., 2019). Simulations of mean and extreme river flows are consistent with the observations only when anthropogenic radiative forcing is considered (Gudmundsson et al., 2021).

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Several studies have identified a persistent ‘fingerprint’ of anthropogenic forcing in global trends in aridity spanning the last 120 years. Using a combination of tree ring data, CMIP5 model simulations, and reanalysis products, Marvel et al. (2019) determined that the dominant trend in aridity since 1900, characterized by drying in North and Central America and the Mediterranean, is detectable and attributable to external forcing from 1900 to 1949. This trend weakens from 1950 to 1975, possibly due to aerosol forcing (Marvel et al., 2019), but then emerges again from 1981 to present, although it is not detectable in the GLEAM nor MERRA-2 soil moisture reanalysis products. Likewise, Bonfils et al. (2020) investigated changes in precipitation, temperature and continental aridity in CMIP5 historical simulations and found that the dominant multivariate fingerprint, an amplification of wet–dry latitudinal patterns and progressive continental aridification, was associated with greenhouse gas emissions (Figure 8.9a, d), and the second leading fingerprint was associated with anthropogenic aerosols (Figure 8.9e, h). This study found that the anthropogenic greenhouse gas signal is statistically detectable in reanalyses over the 1950–2014 period (signal-to-noise ratio above 1.96). Gu et al. (2019) found that a global trend in declining soil moisture is detectable in the GLDAS-2 reanalysis product and is attributable to greenhouse gas forcing. Padrón et al. (2020) reconstructed the global patterns of dry season water availability from 1902–2014, and found itextremely likely (99% range) that trends in the last three decades of the analysis period could be attributed to anthropogenic forcing, mainly due to increases in evapotranspiration. It is very likely (>90% range) that anthropogenic forcing has affected global patterns of soil moisture over the 20th century.

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Several studies have analyzed CMIP5 and land surface models and detected a significant summer drying trend in the NH across the late 20th century that is attributable to anthropogenic forcings (Mueller and Zhang, 2016; Douville and Plazzotta, 2017). This trend is mainly driven by dryland areas such as the western USA and west-central Asia, where both reanalysis products and satellite data confirm there has been a persistent decline in soil moisture since 1990 (Y. Liu et al., 2019a). In the western USA, snow deficits have very likely contributed to recent drying (Mote et al., 2018). Spring snow water equivalent across the Sierra Nevada Mountains reached a record low in 2015 (Margulis et al., 2016; Mote et al., 2016), possibly the lowest of the last five hundred years (Belmecheri et al., 2016). Over the longer California drought (2011–2015) anthropogenic warming alone reduced snowpack levels in the Sierras by 25% (Berg and Hall, 2017). The north-western USA also experienced snow drought in 2015, despite near-normal levels of total cold season precipitation (Mote et al., 2016; Marlier et al., 2017). There is high confidence that anthropogenic warming contributed to these recent snow droughts (Belmecheri et al., 2016; Mote et al., 2016).

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In the western USA, anthropogenic warming is amplifying drought and aridity by increasing evaporative demand and water loss to the atmosphere (Weiss et al., 2009; Overpeck, 2013; Cook et al., 2014; Griffin and Anchukaitis, 2014; Williams et al., 2020). For the California drought between 2012­–2014, Griffin and Anchukaitis (2014) used paleoclimate reconstructions to determine that while rainfall deficits were not unprecedented, record-high temperatures drove an exceptional decline in soil moisture relative to the last millennium. Williams et al. (2015) concluded that anthropogenic warming accounted for 8–27% of these soil moisture deficits. Robeson (2015) estimated that the California drought was a 1-in-10,000 year event. Tree ring reconstructions indicate that prolonged megadroughts have occurred in the western USA throughout the last 1200 years (Cook et al. , 2004, 2010; B.I. Cook et al. , 2015), forced by internal variability (Coats et al., 2016; Cook et al., 2016b). However, Williams et al. (2020) determined that 2000–2018 drought across the south-western USA was the second driest 19-year period since 800 CE, and attributed nearly half the magnitude of this event to anthropogenic forcing (see also Section 10.4.2.3). Evidence for human signals in drought can also be found in western North American streamflow records, as noted above in Section 8.3.1.5. There is high confidence that anthropogenic forcing has contributed to recent droughts and drying trends in western North America.

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Large areas of east-central Asia experienced drying in the early 2000s as a result of warmer temperatures, lower humidity, and declining soil moisture(Wei and Wang, 2013; Z. Liet al. , 2017; Hesslet al. , 2018). Paleoclimate data from the Mongolian plateau suggest that this recent central Asian drought exceeds the 900-year return interval, but is not unprecedented in the last 2060 years (Hessl et al., 2018). There is low confidence due to limited evidence that recent droughts in central Asia can be attributed to anthropogenic forcing.

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The Mediterranean region has experienced notable changes in drought and aridity. A number of studies have identified a decline in precipitation since 1960 and attributed this to anthropogenic forcing (Hoerling et al., 2012; Gudmundsson and Seneviratne, 2016; Knutson and Zeng, 2018; Seager et al., 2019b). Kelley et al. (2015) showed that climate change caused a three-fold increase in the likelihood of the 2007–2010 meteorological drought in the eastern Mediterranean. However, historical trends in precipitation across the Mediterranean are spatially variable and contain substantial decadal variability, such that an anthropogenic influence may not be detectable in all areas (Zittis, 2018; Vicente-Serrano et al., 2021). Records of soil moisture provide a clearer signal, indicating that higher temperatures and increased atmospheric demand have played a strong role in driving Mediterranean aridity (Vicente-Serrano et al., 2014). Hydrological modeling suggests that the recent decline in soil moisture in the Mediterranean is unprecedented in the last 250 years (Hanel et al., 2018). Paleoclimate evidence extends this view, additionally indicating that dryness in the Mediterranean is approaching an extreme condition compared to the last millennium (Markonis et al., 2018) and that the 15-year drought in the Levant (1998–2012) has an 89% likelihood of being the driest of the last 900 years (Cook et al., 2016a). Marvel et al. (2019) found that the Mediterranean region contributes strongly to the anthropogenic warming component of the global trend in aridity. There is high confidence that anthropogenic forcings are causing increased aridity and drought severity in the Mediterranean region.

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Both central and north-eastern Africa have experienced a decline in rainfall since about 1980 (high confidence) (Lyon and Dewitt, 2012; Lyon, 2014; Hua et al., 2016; Nicholson, 2017). In Central Africa, the decline has been attributed to atmospheric responses to Indo-Pacific sea surface temperature variability (Hua et al., 2018). In north-eastern Africa, droughts have become longer and more intense in recent decades, continuing across rainy seasons (Hoell et al., 2017b; Nicholson, 2017), and this trend appears to be unusual in the context of the last 1500 years (Tierney et al., 2015). Knutson and Zeng (2018) attribute decreased annual precipitation over the Sudan to anthropogenic forcing, but other studies argue that the recent trend cannot yet be distinguished from natural variability, at least over parts of this region (Hoell et al., 2017b; Philip et al., 2018). There remains low confidence due to limited evidence that drying the north-eastern Africa is attributable to human influence. In the Western Cape region of South Africa, human influence increased the likelihood of the severe 2015–2017 drought by a factor of 3–6, depending on the analysis (Otto et al., 2018; Pascale et al., 2020). Anthropogenic forcing also contributed to the 2018 drought, mainly by increasing evapotranspiration (Nangombe et al., 2020). While some analysis of instrumental precipitation data in this region detect a slight long-term drying trend consistent with the simulated anthropogenic response (Seager et al., 2019b), there is strong multi-decadal variability in the data (Wolski et al., 2021). However, a study of streamflow in southern Africa detected a significant decline (Gudmundsson et al., 2019; see also Section 10.6.2). There is medium confidence in the long-term drying trend in this region and its attribution to anthropogenic forcing, and medium confidence that anthropogenic warming has contributed to recent severe drought events.

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Several subtropical, semi-arid regions in the Southern Hemisphere have experienced long-term drying trends in the late 20th century. South-western South America (central Chile) experienced a multi-decadal decline in precipitation and streamflow culminating in a post-2010 megadrought that has been partly attributed to anthropogenic GHG emissions and ozone depletion (Boisier et al. , 2016, 2018; Saurral et al. , 2017; Knutson and Zeng, 2018; Seager et al. , 2019b; Garreaud et al. , 2020). There is medium confidence that drying in central Chile can be attributed to human influence. The tree-ring paleoclimate record demonstrates that the mid-century increase in exteme drought events in southern South America is unusual in the context of the last 600 years, suggesting an emerging influence of anthropogenic forcing (Morales et al., 2020).

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There has been a 20% decrease in winter (May to July) rainfall in south-western Australia since 1970, with the decline increasing to around 28% since 2000 (Delworth and Zeng, 2014; BoM and CSIRO, 2020). There has also been a significant increase in the average intensity of seasonal droughts in the region since 1911in response to both lower precipitation and increased atmospheric evaporative demand (Gallant et al., 2013). Several studies attribute the precipitation declines in south-western Australia to anthropogenic changes in GHG and ozone (Delworth and Zeng, 2014; Knutson and Zeng, 2018; Seager et al., 2019b). There is high confidence that the observed drying in south-western Australia can be attributed to anthropogenic forcing.

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In summary, it is very likely that anthropogenic factors have influenced global trends in aridity, mainly through competing changes in evapotranspiration and/or atmospheric evaporative demand due to anthropogenic emissions of GHG and aerosols. There is high confidence that the frequency and the severity of droughts has increased over the last decades in the Mediterranean, western North America, and south-western Australia and that this can be attributed to anthropogenic warming. There is medium confidence that recent drying and severe droughts in southern Africa and south-western South America can be attributed to human influence. In some regions of western North America and the Mediterranean, paleoclimate evidence suggests that recent warming has resulted in droughts that are of similar or greater intensity than those reconstructed over the last millennium (medium confidence).

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The AR5 assessed that Northern Hemisphere (NH) snow cover extent (SCE) has decreased since the late 1960s, especially in spring (very high confidence). This is confirmed by recent studies (Section 2.3.2.2; Kunkel et al., 2016). AR6 assesses that NH spring snow cover has been decreasing since 1978 (very high confidence) and that this trend extends back to 1950 (high confidence) (Section 9.5.3). Human-caused global warming is the dominant driver of this observed decline (Section 3.4.2; Estilow et al., 2015). Model simulations suggest that surface temperature responses at hemispheric/regional scales explain between 40% and 85% of the SCE trend variability (Mudryk et al., 2017). A decreasing trend in snowfall has also been detected in the NH (Figure 8.1; Rupp et al., 2013). Snowfall as a proportion of precipitation has decreased significantly in recent years (Berghuijs et al., 2014). However, a late-20th-century increase in snowfall in West Antarctica observed in ice cores has been linked to a combination of factors including the anthropogenically forced deepening of the Amundsen Sea Low (Thomas et al., 2015, 2017).

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Reconstructions in the Sahel (Carré et al., 2019) and Belize (Ridley et al., 2015) support the southward displacement of the tropical rain belt since 1850 and the narrowing trend of the tropical rainbelt detected in observations (Rotstayn et al., 2002; Hwang et al., 2013). Decreasing precipitation trends in the NH during the 1950s to 1980s have been attributed to anthropogenic aerosol emissions from North America and Europe, which peaked during the late1970s and declined thereafter following improved air quality regulations, causing dimming (brightening) through reduced (increased) surface solar radiation (Box 8.1 Figure 1), in agreement with model simulations (Chiang et al., 2013; Hwang et al., 2013). This is consistent with energetic constraints where tropical precipitation shifts are anti-correlated with cross-equatorial energy transport (Section6.3.3, Box 8.1). It also provides a physical mechanism for the severe drought in the Sahel that peaked in the mid-1980s (Sections 8.3.2.4.3 and 10.4.2.1) and the southward shift of the NH tropical edge from the 1950s to the 1980s (Allen et al., 2014; Brönnimann et al., 2015). However, CMIP5 and CMIP6 models still exhibit strong biases in representing the ITCZ, such as the simulation of a double ITCZ (Oueslati and Bellon, 2015; Adam et al., 2018; Tian and Dong, 2020). The impacts of aerosols and volcanic activity on the position of the ITCZ have been investigated but changes are difficult to characterize from observations (Section 6.3.3.2; Friedman et al. , 2013; J.M. Haywood et al. , 2013; Iles and Hegerl, 2014; Colose et al. , 2016; Chung and Soden, 2017). Such systematic shifts of the ITCZ can have important regional impacts like changes in precipitation (Figure 8.9).

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In summary, there is medium confidence that the tropical rain belts over the oceans have been narrowing and strengthening in recent decades, leading to increased precipitation in the ITCZ core region (Section 8.2.2.2). Decreasing precipitation trends in the NH during the 1950s1980s have been attributed to anthropogenic aerosol emissions from North America and Europe (high confidence).

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The poleward shift of the HC is closely related to migration of the location of tropical cyclone trajectories in bothhemispheres (Sharmila and Walsh, 2018; Studholme and Gulev, 2018), with avery likely poleward shift over the western North Pacific Oceans since the 1940s (Section 11.7.1.2). Moreover, the Western North Pacific Subtropical High has extended westward since the 1970s, resulting in a monsoon rain band shift over China, with excessive rainfall along the middle and lower reaches of the Yangtze River valley along about 30°N over eastern China. At the same time, the effect of anthropogenic aerosols dominated the response to GHG increases over East Asia, resulting in a weakening of the East Asian summer monsoon and causing a drying trend in north-eastern China (Hu, 2003; Yu and Zhou, 2007; T. Wang et al., 2013; Z. Li et al., 2016b; Lau and Kim, 2017) and northern parts of South Asia (Section 8.3.2.4.2; Preethi et al., 2017). During 19772007, the precipitation variability over the eastern USA increased due to changes in the intensity and position of the western ridge of the North Atlantic Subtropical High (Li et al., 2011; Diem, 2013).

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Since AR5, an improved understanding of the key drivers of the recent HC expansion has been achieved, identifying the role of both internal variability and anthropogenic climate change. Part of the recent expansion (19792005) of the HC has been driven by a swing from warm to cold phase of the Pacific Decadal Variability (PDV; Meehl et al., 2016; Grise et al., 2019). The presence of large multi-decadal variability in 20th-century reanalyses means there is limited evidence on the human influence on the recent HC strengthening, yet the southward shift of the southern edge and widening of the SH HC appeared as robust features in all reanalysis datasets, and their trends have accelerated during 19792010 (D’Agostino and Lionello, 2017). As assessed in Section 3.3.3.1, GHG increases and stratospheric ozone depletion have contributed to the expansion of the zonal mean HC in the SH since around 1980, and the expansion of the NH HC has not exceeded the range of internal variability (medium confidence). Moreover, Antarctic ozone depletion can cause a poleward shift in the SH mid-latitude jet and HC (Sections 3.3.3 and 6.3.3.2). Further assessment of the attribution of recently observed changes in the HC extent and intensity is found in Section 3.3.3.1.

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Evidence from several climate modelling studies indicates that the observed decrease in the regional monsoon precipitation during the second half of the 20th century is dominated by the radiative effects of NH anthropogenic aerosols, with smaller contributions due to volcanic aerosols from the Mount Pinatubo (1991) and El Chichón (1982) eruptions (Bollasina et al. , 2011; Polson et al. , 2014; Sanap et al. , 2015; Krishnan et al. , 2016; Liu et al. , 2016; Lau and Kim, 2017; Lin et al. , 2018; Takahashi et al. , 2018; Undorf et al. , 2018a, b; Patil et al. , 2019; M. Singh et al. , 2020; see Box 8.1, Figure 1 and Figure 8.11). Land-use changes over South and South East Asia and the rapid warming trend of the equatorial Indian Ocean during the recent few decades also appear to have contributed to the observed decrease in monsoon precipitation (Roxy et al., 2015; Krishnan et al., 2016; Singh, 2016). Overall, the magnitude of the precipitation response to anthropogenic forcing exhibits large spread across CMIP5 models pointing to the strong internal variability of the regional monsoon (Saha et al., 2014; Salzmann et al., 2014; Sinha et al., 2015), including variations linked to phase changes of the Pacific Decadal Variability (Section AVI.2.6; X. Huang et al., 2020a), uncertainties in representing aerosolcloud interactions (Takahashi et al., 2018), and the effects of local compared with remote aerosol forcing (Bollasina et al. , 2014; Polson et al. , 2014; Undorf et al. , 2018b). CMIP3 and CMIP5 models do not accurately reproduce the observed seasonal cycle of precipitation over the major river basins of South and South East Asia, limiting the attribution of observed regional hydroclimatic changes (Hasson et al., 2014, 2016; Biasutti, 2019). While warm rain processes and organized convection are known to dominate the heavy orographic monsoon rainfall over the Western Ghats mountains (Shige et al., 2017; Choudhury et al., 2018), in various parts of India (Konwar et al., 2012) and East Asia (Section 11.7.3.1), there are uncertainties in representing the regional physical processes of the monsoon environment, including cloudaerosol interactions (Sarangi et al., 2017), land atmosphere (e.g., Bartonet al., 2020) and oceanatmosphere coupling (Annamalai et al., 2017), in state-of-the-art climate models (see also Section 8.5.1).

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In summary, there is high confidence in observational evidence for a weakening of the SAsiaM in the second half of the 20th century. Results from climate models indicate that anthropogenic aerosol forcing has dominated the recent decrease in summer monsoon precipitation, as opposed to the expected intensification due to GHG forcing (high confidence). On paleoclimate time scales, the SAsiaM strengthened in response to enhanced summer warming in the NH during the early-to-mid Holocene, while it weakened during cold intervals (high confidence). These changes are tightly linked to orbital forcing and changes in high-latitude climate (medium confidence).

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Anthropogenic factors such as GHGs and aerosols had an influence on the EAsiaM changes (Figure 8.11; T. Wang et al. , 2013; Song et al. , 2014; Xie et al. , 2016; Chen and Sun, 2017; Ma et al. , 2017; L. Zhang et al. , 2017; Day et al. , 2018; Tian et al. , 2018). Increased precipitation in the southern region has been linked to increased moisture flux convergence driven by GHG forcing while changes in anthropogenic aerosols have weakened the EAsiaM and reduced precipitation in the northern regions (Tian et al., 2018). Aerosol-induced cooling, associated atmospheric circulation changes and sea surface temperature (SST) feedbacks weaken the EAsiaM and favour the observed dry-north and wet-south pattern of rainfall anomalies (T. Wang et al. , 2013; Song et al. , 2014; L. Zhang et al. , 2017; G. Chen et al. , 2018; X. Chen et al. , 2018; Undorf et al., 2018b).

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In summary, there is strong evidence of a stronger EAsiaM and northward migration of the rainbelt during warmer climates based on paleoclimate reconstructions. There is high confidence that anthropogenic forcing has been influencing historical EAsiaM changes with drying in the north and wetting in the south observed since the 1950s, but there is low confidence in the magnitude of the anthropogenic influence. The transition towards a positive PDV phase has been one of the main drivers of the EAsiaM weakening since the 1970s (high confidence).

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Since AR5, there has been improved understanding of the West African monsoon (WAfriM) response to natural and anthropogenic forcing. On paleoclimate time scales, enhanced summer insolation in the Northern Hemisphere (NH) intensified the WAfriM precipitation during the early-to-mid Holocene (high confidence), as seen in rainfall proxy records and climate model simulations (Masson-Delmotte et al. , 2013; Mohtadi et al. , 2016; Braconnot et al. , 2019). Despite improvements in model simulations of the present-day monsoons, CMIP5 and CMIP6 models underestimate mid-Holocene changes in the amount and spatial extent of the WAfriM precipitation (Section 3.3.3.2; Brierley et al., 2020).

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The Sahel drought from the 1970s until the early 1990s was related to anthropogenic emissions of sulphate aerosols in the Atlantic, which led to an inter-hemispheric pattern of SST anomalies and associated regional precipitation changes (Section 6.3.3.2 and Box 8.1). Also the combined effects of anthropogenic aerosols and GHG forcing appear to have contributed to the late twentieth century drying of the Sahel through their effect on SST, by cooling the North Atlantic and warming the tropical oceans (Giannini and Kaplan, 2019; Hirasawa et al., 2020). Subsequent aerosol removal led to SST warming of the North Atlantic, shifting the ITCZ further northward and strengthening the WAfriM (Giannini and Kaplan, 2019). The recent recovery has been ascribed to prevailing positive SST anomalies in the tropical North Atlantic potentially associated with a positive phase of the Atlantic Multi-decadal Oscillation (Diatta and Fink, 2014; Rodríguez-Fonseca et al., 2015). The Sahel rainfall recovery has also been attributed to higher levels of GHG in the atmosphere and increases in atmospheric temperature (Dong and Sutton, 2015).

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In summary, most regions of West Africa experienced a wet period in the mid-20th century followed by a very dry period in the 1970s and 1980s that is attributed to aerosol cooling of the NH (high confidence). Recent estimates provide evidence of a WAfriM recovery from the mid-to-late 1990s, with more intense extreme events partly due to the combined effects of increasing GHG and decreasing anthropogenic aerosols over Europe and North America (high confidence). On paleoclimate time scales, there is high confidence that the WAfriM strengthened during the early-to-mid Holocene in response to orbitally-forced enhancement of summer warming in the NH.

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Observations during 19792014 suggest that poleward shifts in the South Atlantic Convergence Zone (SACZ) noted in recent decades (Talento and Barreiro, 2018; Zilli et al., 2019), are associated with precipitation amount decrease along the equatorward margin and increase along the poleward margin of the convergenze zone (Zilli et al., 2019). Several observational studies identified delayed onsets of the SAmerM after 1978 related to longer dry seasons in the southern Amazon (Fu et al. , 2013; Yin et al. , 2014; Arias et al. , 2015; Debortoli et al. , 2015; Arvor et al. , 2017; Giráldez et al. , 2020; Haghtalab et al. , 2020; Correa et al. , 2021). In contrast, other studies indicate a trend toward earlier onsets of the SAmerM (Jones and Carvalho, 2013). These discrepancies are explained by the methodology used and the domain considered for the SAmerM, confirming the occurrence of delayed onsets of the SAmerM since 1978 (Correa et al., 2021). CMIP5 simulations show trends toward delayed onsets of the SAmerM in association with anthropogenic forcing, although the simulated trends underestimate the observed trends (Fu et al., 2013). Total rainfall reductions are observed in the southern Amazon during SeptemberOctoberNovember after 1978 (Fu et al., 2013; Bonini et al., 2014; Debortoli et al., 2015, 2016; Espinoza et al., 2019), consistent with reductions in river discharge in the region (Molina-Carpio et al. , 2017; Espinoza et al. , 2019; Heerspink et al., 2020).

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Significant increases in precipitation have been observed over south-eastern Brazil during 19022005 while non-significant decreases have been found over central Brazil (Vera andDíaz, 2015). In Bolivia, increases were observed during 19651984, while reductions have occurred since then (Seiler et al., 2013). However, the Peruvian Amazon does not reveal significant changes in mean rainfall during 1965–2007 (Lavado et al., 2013; Ronchail et al., 2018). Historical simulations from CMIP5 ensembles adequately capture the observed summer precipitation amount over central and south-eastern Brazil, thereby providing high confidence in interpreting the observed variability of SAmerM for the period 19601999 (Gulizia and Camilloni, 2015; Pascale et al., 2019). Also, CMIP5 simulations indicate that the anthropogenic forcing associated with increased GHG emissions is necessary to explain the positive trends in upper-troposphere zonal winds observed over the South American Altiplano (Vera et al., 2019). However, the detection of anthropogenically-induced signals for precipitation is still ambiguous in monsoon regions, like the SAmerM (Hoegh-Guldberg et al., 2018).

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In summary, there is high confidence that the SAmerM onset has been delayed since the late 1970s. This is reproduced by CMIP5 simulations that consider anthropogenic forcing. There is also high confidence that precipitation during the dry-to-wet transition season has been reduced over the southern Amazon. Paleoclimate reconstructions and simulations suggest a weaker SAmerM during warmer epochs such as the Mid-Holocene or the 900–1100 period, and stronger monsoon during colder epochs such as the LGM or the 1400–1600 period (high confidence).

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The AR5 assessed low confidence in centennial changes in tropical cyclone (TC) activity globally, and in the attribution of observed changes in TCs to anthropogenic forcing. Since AR5, there has been considerable progress in understanding the observed changes of TCs and an overall improved knowledge of the sensitivity of TCs to both GHG and aerosol forcing (Knutson et al., 2019; Sobel et al., 2019).

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Although observational data limitations (Lau and Zhou, 2012) tend to limit detection of anthropogenic forced increases in TC precipitation (Knutson et al., 2019), there is medium confidence that anthropogenic forcing has contributed to observed heavy rainfall events over the USA associated with TCs (Kunkel et al., 2012) and other regions with sufficient data coverage (Section 11.7.1.2; Bindoff et al., 2013). There has been increased frequency of TC heavy rainfall events over several areas in the USA since the late 19th century that is greater than what would be expected solely from changes in US landfall frequency, suggesting the increasing role of TCs have in causing heavy rainfall events (Kunkel et al., 2010). For example, there is evidence for an anthropogenic contribution to the extreme rainfall of Hurricane Harvey in 2017 (Emanuel, 2017; Risser and Wehner, 2017; van Oldenborgh et al., 2017; Trenberth et al., 2018; S.-Y.S. Wang et al., 2018).

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In addition to evidence that rain-rates have increased, there is evidence that TC translation speed has slowed globally (Kossin, 2018) thus amplifying thermodynamic intensification of rainfall and may be linked to anthropogenic forcing (Gutmann et al., 2018). This is limited evidence however, so there is medium confidence of a detectable change in TC translation speed over the US. Since the 1900s, and there is low confidence for a global signal because of limited agreement among models and due to data heterogeneity. However, the slowdown is consistent with theoretical and modelling studies that indicate a general weakening of the tropical circulation with warming that reduces the speed of the TC system (Chauvin et al., 2017), though there is limitedobservational evidence (Sections 8.2.3.5 and 11.7.1).

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In summary, there is low confidence in strengthened winter stationary wave activity over the North Atlantic, associated with increased poleward moisture fluxes east of North America There is medium confidence in a recent amplification of the NH stationary waves in summer, but no formal attribution to anthropogenic climate change.

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There is no evidence of a trend in the Indian Ocean Dipole (IOD; Section AIV.2.4) mode and associated anthropogenic forcing (Sections 2.4.3 and 3.7.4). The AR5 concluded that the IOD is likely to remain active, affecting climate extremes in Australia, Indonesia and East Africa. Since the AR5, IOD teleconnections have been identified extending further to the Middle East (Chandran et al., 2016), to the Yangtze river (Xiao et al., 2015), where in boreal summer and autumn positive IOD events tend to increase the precipitation in the south-eastern and central part of the basin, and to the southern Africa extreme wet seasons (Hoell and Cheng, 2018). During the last millenium, the combined effect of a positive IOD and El Niño conditions have caused severe droughts over Australia (Abram et al., 2020). In the satellited period, it is found more effective in inducing significant decrease of rainfall over Indonesia, with the opposite occurring for negative IOD events (As-syakur et al., 2014; Nur’utami and Hidayat, 2016; Pan et al., 2018). Similarly, over the Ganges and Brahmaputra river basins major droughts have been recorded during co-occurring El Niño and positive IOD, while floods occurred during La Niña and negative IOD conditions (Pervez and Henebry, 2015). Over equatorial East Africa the IOD affects the short rain season (medium confidence) exacerbating flooding and inundations independently of ENSO (Behera et al., 2005; Conway et al., 2005; Ummenhofer et al., 2009; Hirons and Turner, 2018). Extreme conditions, like the 2019 Australian bushfires and African flooding, have been associated with strong positive IOD conditions (Cai et al., 2021).

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In summary, multiple water cycle changes related to ENSO and IOD teleconnections have been observed across the 20th century (high confidence), mostly dominated by interannual to multi-decadal variations. The MJO amplitude has increased in the second half of the 20th century partly because of anthropogenic global warming (medium confidence) altering regional precipitation signals.

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CMIP6 climate models continue to project a steady increase in global mean column-integrated water vapour by around 613% by 20412060 and 532% by 20812100, depending on scenario (Table 8.1). This is consistent with projected atmospheric warming (Section 4.5.1.2) and the Clausius–Clapeyron relationship (Section 8.2.1) where every degree Celsius of warming is associated with an approximate 7% increase in atmospheric moisture in the lower atmospheric layers where most of the water vapour is concentrated. This increase sustains a positive feedback on anthropogenic global warming (Section 7.4.2.2). In contrast, the response of clouds is much more spatially heterogeneous, microphysically complex, and model-dependent so that the projected cloud feedbacks remain a key uncertainty for constraining climate sensitivity (Section 7.4.2.4).

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Geographical patterns of projected precipitation changes show substantial seasonal contrasts and regional differences, including over land (Figure 8.14 and Figure 4.27). Projections for 20812100under the SSP2-4.5scenariosuggest increased precipitation over the tropical oceans, north-eastern Africa, the Arabian Peninsula, India, south-eastern Asia and the Polar regions while decreased precipitation is projected mainly over the subtropical regions (Section 4.5.1.4). Precipitation changes contrast regionally in the tropics with wetter wet seasons over South Asia, central Sahel and eastern Africa, but less precipitation over Amazonia and coastal West Africa (Section 8.4.2.4). These large-scale responses are associated with stronger moisture transports in a warmer climate that are modulated by the greater warming over land than ocean, atmospheric circulation responses and land surface feedbacks (Section 8.2.2). There is agreement across CMIP5 and CMIP6 modelling studies that precipitation increases in wet parts of the atmospheric circulation and decreases in dry parts (Liu and Allan, 2013; Kumar et al., 2015; Deng et al., 2020; Schurer et al., 2020) although these regions shift with atmospheric circulation changes. The overall pattern is robust across different model scenarios and time horizons (Tebaldi and Knutti, 2018), but some deviations from the mean pattern cannot be excluded due to the multiple time scales and non-linear atmospheric or land surface processes involved (Section 8.5.3). Near-term regional changes in precipitation are more uncertain because of a stronger sensitivity to natural variability (Section 8.5.2) and non-GHG anthropogenic forcings (Section 4.4.1.3 and 8.4.3.1).

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The AR5 did not highlight observed changes in water cycle seasonality and SRCCL mostly emphasized changes in vegetation seasonality. Since AR5, a number of relevant studies have been published, but often with conflicting results. Based on three in situdatasets, reduced precipitation seasonality was identified over 62% of the terrestrial ecosystems analysed from 19502009 (Murray-Tortarolo et al. 2017). In contrast, both in situ and satellite data show a general increase in the annual range of precipitation from 1979 to 2010, which is dominated by wetter wet seasons (Chou et al., 2013). This paradox may be partly explained by a larger aerosol radiative forcing in the middle of the 20th century as well as by internal variability (Kumar et al., 2015; see also Box 8.1). For instance, the ‘long rains’ over East Africa experienced declining trends in the 1980s and 1990s (Nicholson, 2017), which was linked to anthropogenic aerosols and SST patterns (Rowell et al., 2015), followed by a recent recovery that was linked to internal variability (Wainwright et al., 2019). Two satellite datasets revealed decreased rainfall seasonality in the tropics but an increased seasonality in the subtropics and mid-latitudes since 1979, without clear attribution (Marvel et al., 2017).

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In northern high latitudes, a shorter snow season (X. Zeng et al., 2018) is mainly due to an earlier onset of spring snowmelt (Peng et al., 2013) which has been attributed to anthropogenic climate change (Najafi et al., 2016). Changes in snow seasonality affect streamflow at the regional scale, with an earlier peak in spring and a possible decrease of low-level flow in summer (Berghuijs et al., 2014; Kang et al., 2016; Dudley et al., 2017), while glacier shrinking can also alter the low-level flow in mountain catchments (Lutz et al., 2014; Milner et al., 2017; Huss and Hock, 2018). This can be partly ameliorated by water management in regulated catchments (Arheimer et al., 2017), but not in large river basins such as the Amazon which also shows an increased seasonality of discharge since 1979 (Liang et al., 2020).

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In summary, future projections indicate that anthropogenic forcings will drive an increase in global mean evaporation over most oceanic areas (high confidence) (Figure 8.17), an increase in global atmospheric demand (virtually certain) and an increase in evapotranspiration over most land areas, with the exception of moisture-limited regions (medium confidence). However, substantial uncertainties in projections of evapotranspiration, especially at seasonal and regional scales, remain (see also Section 8.2.3.3 and Cross-Chapter Box 5.1).

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Soil moisture in the top soil layer (10 cm) shows more widespread drying than total soil moisture, reflecting a greater sensitivity of the upper soil layer to increasing evaporative demand (Figure 8.19; Berg et al., 2017). Conversely, total column soil moisture represents the carry-over of moisture from previous seasons deeper in the soil column, and potentially higher sensitivity to vegetation processes (Berg et al., 2017; Kumar et al., 2019). Central America, the Amazonian basin, the Mediterranean region, southern Africa, and south-western Australia are projected to experience significant declines in total soil moisture, whereas declines in Europe (north of the Mediterranean), western Siberia, and north-eastern North America are limited to the surface (Figure 8.19). It should be noted that because models differ in their number of hydrologically active layers, there is less confidence in total soil moisture projections than surface soil moisture projections. Based on surface soil moisture projections, more than 40% of global land areas (excluding Antarctica and Greenland) are expected to experience robust year-round drying, even under lower emissions scenarios (Cook et al., 2020). The percentage of land area experiencing drying is slightly lower when runoff is used as an aridity metric instead (20–30%); taking this into consideration, it is estimated that about a third of global land areas will experience at least moderate drying in response to anthropogenic emissions, even under SSP1-2.6 (medium confidence) (Cook et al., 2020).

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Groundwater projections were not assessed in AR5. Groundwater processes are not explicitly included in most current CMIP6 models and so must be calculated separately with hydrologic models (e.g., R.G. Taylor et al. , 2013; Cuthbert et al. , 2019a). A range of factors are important in assessing groundwater projections, including the mean difference between precipitation and evaporation, the intensity of precipitation (R.G. Taylor et al., 2013a), and in changes in snow (Tague and Grant, 2009), glaciers (Gremaud et al., 2009), and permafrost (Okkonen and Kløve, 2011). Climate impacts on groundwater are occurring in the context of severe and growing human-caused groundwater depletion (WGII; Konikow and Kendy, 2005; Rodell et al., 2018; Bierkens and Wada, 2019), and water scarcity issues (Mekonnen and Hoekstra, 2016). Climate-related changes to the water cycle can influence water demand (for example, precipitation decreases in an irrigated area), and anthropogenic groundwater depletion can influence the water cycle through interactions with surface energy fluxes, surface water, and vegetation (Cuthbert et al., 2019a), although uncertainties in estimates of future groundwater depletion are large (Smerdon, 2017; Bierkens and Wada, 2019). Some aspects of groundwater change will be irreversible, including the increase of saltwater intrusion into coastal aquifers with sea level rise (Werner and Simmons, 2009), and depletion of fossil aquifers and aquifers with very long recharge times (Bierkens and Wada, 2019).

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A consistent poleward expansion of the edges of the Hadley cells is projected (Nguyen et al., 2015; Grise and Davis, 2020), particularly in the SH, consistent with observed trends (Figure 8.21 and Section 8.3.2.2; Nguyen et al. , 2015). The main driver of future expansion appears to be greenhouse gas forcing (Grise et al., 2019), with uncertainty in magnitude due to internal variability (Kang et al., 2013). Proposed mechanisms for poleward expansion include increased dry static stability (Frierson et al. , 2007; Lu et al. , 2007), increased tropopause height (Chen and Held, 2007; Chen et al. , 2008), stratospheric influences (Kidston et al., 2015) and radiative effects of clouds and water vapour (Shaw and Voigt, 2016; see also Section 4.5.1.5). Hadley cell expansion is thought to be associated with the precipitation declines projected in many subtropical regions (Shaw and Voigt, 2016), but more recent work suggests that these reductions are mainly due to the direct radiative effect of CO2 forcing (He and Soden, 2015), land sea contrasts in the response to forcing (Shaw and Voigt, 2016; Brogli et al. , 2019) and SST changes (Sniderman et al., 2019). In semi-arid, winter rainfall-dominated regions (such as the Mediterranean), thermodynamic processes associated with the land sea thermal contrast and lapse rate changes dominate the projected precipitation decline in summer, whereas circulation changes are of greater importance in winter (Brogli et al. , 2019). The hydroclimates in these regions are projected to evolve with time due to changing contributions from rapid atmospheric circulation changes and their associated SST responses, as well as slower SST responses to anthropogenic forcing (Zappa et al., 2020).

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CMIP5 projections indicated a possible intensification of the EAsiaM circulation during the 21st century, in addition to precipitation increase, although there is a lack of consensus on changes in the western North Pacific subtropical high, this is an important feature of the EAsiaM circulation (Kitoh, 2017). Furthermore, the EAsiaM precipitation enhancements in the CMIP5 projections are prominent over the southern part of the Baiu rainband by the late 21st century, with no significant changes in the Meiyu precipitation over central-eastern China (Horinouchi et al., 2019). It was also shown that the Baiu precipitation response in CMIP5 projections is accompanied by a southward retreat of the western North Pacific subtropical high and a southward shift of the East Asian subtropical jet (Horinouchi et al., 2019). According to the high-resolution MRI-AGCM global warming experiments, future summer precipitation could potentially increase on the southern side and decrease on the northern side of the present-day Baiu location in response to downward-motion tendencies which can offset the ‘wet-gets-wetter’ effect, but is subject to large model uncertainties (Ose, 2019). Future projections of land warming over the Eurasian continent (Endo et al., 2018) and intensified land sea thermal contrast (Z. Wang et al., 2016; Tian et al., 2019) can potentially intensify the EAsiaM circulation during the 21st century. However, there are large uncertainties in projected water cycle changes over the region (Endo et al., 2018), mostly in the near-term because of uncertainties in future aerosol emissions scenarios (Wilcox et al., 2020), as well as due to the interplay between internal variability and anthropogenic external forcing (Wang et al., 2021).

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The AR5 concluded that projections of West African monsoon (WAfriM) rainfall are highly uncertain in CMIP3 and CMIP5 models, but still suggest a small delay and intensification in late wet season rains. Studies published since AR5 are broadly consistent with this assessment. CMIP6 models agree on statistically significant projected increases in rainfall in eastern-central Sahel and a decrease in the west for the end of the 21st century (Roehrig et al., 2013; Biasutti, 2019; Monerie et al., 2020). However, the magnitude of WAfriM projected precipitation depends on the convective parametrization used (Hill et al., 2017), and large uncertainties remain in WAfriM projections because of large inter-model spread, particularly over the western Sahel (Roehrig et al., 2013; Biasutti, 2019; Monerie et al., 2020). CMIP6 models show a general increase of WAfriM precipitation across all future scenarios but with a substantial model spread for the SSP5-8.5 scenario (Figure 8.22). This sensitivity arises from the combined and contrasting influences of anthropogenic greenhouse gas and aerosol forcing that affect WAfriM precipitation (particularly over the Sahel) directly and also indirectly through subtropical North Atlantic SST changes (Giannini and Kaplan, 2019). The large model spread and associated uncertainties in projected precipitation changes is reflected also in runoff and P–E changes (Table 8.2). Regional climate models (RCMs) ensembles (e.g., Klutse et al., 2018) agree with CMIP5 projected rainfall trends but some individual models show rainfall declines (e.g., Sylla et al., 2015; Akinsanola et al., 2018), highlighting the existing large uncertainties in RCMs WAfriM rainfall projections.

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The role of anthropogenic aerosol forcing in future projections of the Australian monsoon has been investigated for CMIP5 models (Dey et al., 2019a); decreases in anthropogenic aerosol concentrations over the 21st century are expected to produce relatively greater warming in the NH than SH, favouring a northward shift of the tropical rain belt (e.g., Rotstayn et al., 2015).

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In AR5, several factors were identified as relevant to the uncertainties in projections of cyclone intensity, frequency, location of storm tracks and precipitation associated with ETCs. These include horizontal resolution, resolution of the stratosphere, and how changes in the Atlantic meridional overturning circulation (AMOC) were simulated. Since AR5, projections of extratropical cyclones and storm tracks have been examined further, largely confirming previous assessments. In particular, extratropical cyclone precipitation scales with the product of cyclone intensity (as measured by near-surface wind speed) and atmospheric moisture content (Pfahl and Sprenger, 2016). Booth et al. (2018) showed that the fraction of rainfall generated by the convection scheme in simulated extratropical cyclones is highly model- and resolution-dependent, which may be a source of uncertainty regarding their precipitation response to anthropogenic forcings. Also, increased moisture availability may increase the maximum intensity of individual storms while reducing the overall frequency as poleward energy transport becomes more efficient.

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A major challenge in representing convective clouds and related precipitation events in GCMs is a lack of sophisticated cloud microphysics in convective parametrization schemes (e.g., Fan et al., 2016). Most of these schemes only include simple microphysical treatments, such as direct partition between cloud condensation and precipitation, and do not include advanced treatment of conversion among different types of hydrometeors. As such these schemes are unable to simulate microphysical cloud and precipitation responses to aerosol-related perturbations in cloud droplet concentration and ice crystals (see Box 8.1), or perturbations in thermodynamical states from global warming. Efforts have been made to include more advanced cloud microphysical treatment in cumulus parametrizations (Song and Zhang, 2011; Grell and Freitas, 2014; Berg et al., 2015) or to use explicit cloud microphysics schemes in climate models with a ‘super parametrization’ (Wang et al., 2015), which have been shown to improve the performance in simulating cloud properties and precipitation. However, few of these improvements have been incorporated into CMIP6 climate models so the projected precipitation response to anthropogenic perturbation may still be hindered by the inadequate microphysical treatment in cumulus parametrization (Smith et al., 2020).

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Estimating internal variability is an important challenge in the assessment of human-induced changes in the water cycle since its magnitude and range of variability can exceed the anthropogenic signal, at least at the regional scale and for near-term projections or low-emissions scenarios (Sections 4.4.1.4 and 8.4.2.9; Deser et al. , 2012; Shepherd, 2014; Xie et al. , 2015; Sarojini et al. , 2016; Dai and Bloecker, 2019; Lehner et al. , 2020). Underestimating internal variability in models may result in the overestimation of anthropogenic climate change because the ‘noise’ in the signal-to-noise ratio is underestimated (Knutson and Zeng, 2018). There is medium confidence that this underestimation affects global water cycle projections, for instance, in terms of drought persistence and severity in the south-western USA, eastern Australia, southern Africa, the Mediterranean, the southern Amazonian basin and China (Ault et al., 2014; Cook et al., 2018; Gu et al., 2018). In CMIP6 models, the uncertainty in future projections of 20-year mean precipitation changes attributable to internal variability ranges from 41% in the near term (2021–2040) to 5% in the long term (2081–2100) (Figure 8.23). For decadal-mean precipitation changes, the relative contribution of internal variability is even larger when using large ensembles (Lehner et al., 2020).

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The mechanisms driving internal variability in the water cycle in climate model simulations varies. While models indicate that cool SSTs in the eastern tropical Pacific (La Niña or the cool phase of the PDO) are associated with drought in south-western North America, they also show that atmospheric internal variability may be a more prominent driver (Coats et al., 2015, 2016; Stevenson et al., 2015; Parsons et al., 2018). Simulations of the last millennium from CMIP5–PMIP3 reproduce the observed negative correlation between eastern Australian rainfall and the central equatorial Pacific SSTs with varying skill, and also display periods when the ENSO teleconnection weakens substantially for several decades (Brownet al., 2016a). Differences in simulated internal variability have been found to be responsible for the inter-model spread in predicted shifts in subtropical dry zones for a given shift in the Hadley cell (Seviour et al., 2018). CMIP5 models show that both internal variability and anthropogenic forcings are responsible for the drying over the South Atlantic Convergence Zone region, though with large uncertainties (Zilli and Carvalho, 2021). Moreover, the detection of the anthropogenic forcing on the South Atlantic Convergence Zone is strongly dependent on the characterization of model internal variability (Talento and Barreiro, 2012).

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Since AR5, SMILEs have helped quantify the time of emergence of climate change signals (see Sections 1.4.2.2 and 10.4.3). Results from SMILEs indicate that by 2000–2009 (compared to 1950–1999), simulated anthropogenic shifts in mean annual precipitation already emerged over 36–41% of the globe including high latitudes (Frankcombe et al., 2018; Kumar and Ganguly, 2018), the eastern subtropical oceans, and the tropics (Zhang andDelworth, 2018). By 2050 (2100), more than 60% (85%) of the globe is projected to show detectable anthropogenic shifts in mean annual precipitation (Zhang andDelworth, 2018). Other SMILE results for the 1950–2100 period (Kay et al., 2015; Sigmond and Fyfe, 2016) indicate that internal variability can obscure the detection of the anthropogenic hydroclimatic signal until the middle to late 21st century in many parts of the world for both mean and extreme precipitation (Martel et al., 2018; Dai and Bloecker, 2019). A common finding is that changes in the characteristics of wet extreme events will emerge earlier than changes in average conditions (Gaetani et al., 2020; Hawkins et al., 2020; Kusunoki et al., 2020). An assessment of the methods used to estimate time of emergence is presented in Chapter 10 (Section 10.3.4.3). For specific regional examples of climate change attribution and emergence of anthropogenic signal, see Section 10.4.2.

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Adapting water resource management in the face of climate change will greatly benefit from improved prediction of land surface hydrology at the decadal time scale. Climate predictions (Section 1.4.4) differ from climate projections by constraining the initial state of the slow components of the climate system (i.e., the ocean, the cryosphere and the terrestrial hydrology) as well as volcanic aerosols and ozone depleting substances with observations. Anthropogenic and natural radiative forcing and low-frequency modes of variability (e.g., AMV and PDV, Annex IV.2.7 and IV.2.6) suggest the possible predictability of climate in the first decade or so of the 21st century, in addition to the projected response to the anthropogenic forcing (Sections 4.2.3 and 4.4.1.3).

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Volcanic eruptions can affect climate projections in the near term (2021–2040; Section 4.4.4 and Cross-Chapter Box 4.1). In this chapter, they are of interest because they can trigger a transient departure from the water cycle response to anthropogenic radiative forcing. Major volcanic eruptions temporarily reduce total global and wet tropical region precipitation (high confidence) (Iles and Hegerl, 2014), can weaken or shift the ITCZ (Iles and Hegerl, 2014; Colose et al., 2016; Liu et al., 2016), and reduce summer monsoon rainfall (medium confidence) (Pausata et al. , 2015b; Zambri and Robock, 2016; Zambri et al. , 2017; Zuo et al. , 2019; M. Singh et al. , 2020). Monsoon precipitation in one hemisphere can be enhanced by the remote volcanic forcing occurring in the other hemisphere (medium confidence) (Pausata et al. , 2015a; Liu et al. , 2016; Zuo et al. , 2019). Over the Sahel, the sign of hydrological changes depend on the hemisphere where the volcanic eruptions occur (J.M. Haywood et al., 2013). Out of phase changes in the Sahel and the Amazonian basin are expected from the effect of volcanic aerosols on tropical Atlantic SST and the ITCZ (Hua et al., 2019). Over the last millennium, uncertainties remain in the symmetry/asymmetry of the monsoon response because it is difficult to estimate the exact latitude and season of past volcanic eruptions further back in time (Colose et al., 2016; Fasullo et al., 2019).

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The AR5 concluded that annual and seasonal mean precipitation changes can be estimated by linear pattern-scaling techniques (Santer and Wigley, 1990; Arnell and Gosling, 2016; Greve et al., 2018), which represent regional changes in precipitation as a linear function of global mean temperature change. However, there are a number of caveats when pattern-scaling is applied to low-emissions scenarios or to scenarios where localized forcing (e.g., anthropogenic aerosols) are significant and vary in time (Collins et al., 2013). Here the focus is in on non-linear water cycle responses to increasing global warming levels, as estimated for instance fromthe difference between the first 2°C of global warming, and the next 2°C of warming (Figure 8.25), andtheir possible underlying mechanisms.

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Even in a theoretical climate system governed by linear processes, pattern-scaling assumptions can fail because the different forcing time response of different parts of the Earth system cause evolving spatial warming patterns (Good et al., 2016a). This occurs primarily because different feedbacks occur at different time scales (Armour et al., 2013; Andrews et al., 2015), which in turn implies that the atmospheric circulation and water cycle is dependent both on the level of warming and the rate of change (Ceppi et al., 2018). The usual distinction between the fast adjustment to increased GHG concentrations and the slower response to SST warming (Section 8.2.2.2) may, however, not be sufficient to explain the time evolution of the hydroclimatic response at the regional scale, especially in subtropical land areas where this response critically depends on shifts in atmospheric circulation associated with distinct ‘fast’ (typically five to ten years, that is however much slower than the atmospheric adjustment assessed in Section 8.2.1) and slow SST warming patterns (Zappa et al., 2020). The changing balance between the water cycle response to anthropogenic GHG and aerosol forcings is another source of non-linearity across time and global warming levels (Ishizaki et al. , 2013; Rowell et al. , 2015; Y. Liu et al. , 2019b; Wilcox et al., 2020).

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Greening of the Sahara and Sahel regions in North Africa, in response to an increase in precipitation, has long been considered an amplifying mechanism that can lead to abrupt change. Although the high surface albedo of the desert stabilizes the energy balance of the system (Charney, 1975), greening can induce strong, positive feedbacks between the land surface and precipitation that can shift the region into a ‘Green Sahara’ state. The fact that the transition phase between a Desert Sahara and Green Sahara is not theoretically stable (Brovkin et al., 1998) creates a tipping point and allows for the possibility of an abrupt shift between dry and wet climate regimes. Paleoclimate reconstructions provide evidence of past Green Sahara states (DeMenocal and Tierney, 2012), under which rainfall rates increased by an order of magnitude (Tierney et al., 2017), leading to a vegetated landscape (Jolly et al., 1998) with large lake basins (Gasse, 2000; Drake and Bristow, 2006). The underlying driver of the Green Sahara is the periodic increase in summer insolation associated with the orbital precession cycle (Kutzbach, 1981). In this sense, Green Saharas are not direct analogues for a response to anthropogenic greenhouse gas emissions (GHGs), as these past states were forced by natural, seasonal changes in solar radiation. However, the climate dynamics of Green Sahara periods (which have global impacts, Pausata et al., 2020), and the speed of the transitions between Desert Saharas and Green Saharas, are relevant for future projections.

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Mineral dust aerosols in the climate system originate from both semi-permanent and transient sources (Prospero et al., 2002; Ginoux et al., 2012). The former are typically arid regions where significant alluvial sediments have accumulated over time, while the latter are often associated with natural (e.g., droughts, wildfires) and anthropogenic (e.g., land use change, desertification) disturbances. Modern-day dust emissions are dominated by natural sources (Ginoux et al., 2012), although human emissions may contribute 10–60% of the global atmospheric dust load (Webb and Pierre, 2018). Paleo-dust records suggest that human factors (land use change and landscape disturbance) may have doubled global dust emissions between 1750 and the last quarter of the 20th century (Section 2.2.6; Hooper and Marx, 2018).

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Zhang, L. et al., 2019: Indian Ocean Warming Trend Reduces Pacific Warming Response to Anthropogenic Greenhouse Gases: An Interbasin Thermostat Mechanism. Geophysical Research Letters, 46(19), 10882–10890, doi: 10.1029/2019gl084088.

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This Report assesses both observed changes, and the components of these changes that are attributable to anthropogenic influence (i.e., human-induced), distinguishing between anthropogenic and naturally forced changes (Chapter 3, Sections 1.2.1.1 and 1.4.1, and the Cross-Working Group Box on Attribution). The core assessment conclusions from previous IPCC reports are confirmed or strengthened in this report, indicating the robustness of our understanding of the primary causes and consequences of anthropogenic climate change.

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The chapter comprises seven sections (Figure 1.3). Section 1.2 describes the present state of Earth’s climate, in the context of reconstructed and observed long-term changes and variations caused by natural and anthropogenic factors. It also provides context for the present Assessment by describing recent changes in international climate change governance and fundamental scientific values. The evolution of knowledge about climate change and the development of earlier IPCC assessments are presented in Section 1.3. Approaches, methods and key concepts of this Assessment are introduced in Section 1.4. New developments in observing networks, reanalyses, modelling capabilities and techniques since AR5 are discussed in Section 1.5. The three main ‘dimensions of integration’ across Working Groups in AR6, that is, emissions scenarios, global warming levels and cumulative carbon emissions, are described in Section 1.6. The Chapter closes with a discussion of opportunities and gaps in knowledge integration in Section 1.7.

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The IPCC Sixth Assessment Cycle occurs in the context of increasingly apparent climatic changes observed across the physical climate system. Many of these changes can be attributed to anthropogenic influences, with impacts on natural and human systems. The AR6 also occurs in the context of efforts in international climate governance such as the Paris Agreement, which sets a long-term goal to hold the increase in global average temperature to ‘well below 2°C above pre-industrial levels, and to pursue efforts to limit the temperature increase to 1.5°C above pre-industrial levels, recognizing that this would significantly reduce the risks and impacts of climate change.’ This section summarizes key elements of the broader context surrounding the assessments made in the present report.

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Warming of the climate system is most commonly presented through the observed increase in global mean surface temperature (GMST). Taking a baseline of 1850–1900, GMST change until present (2011–2020) is 1.09°C [0.95 to 1.20] °C (Section 2.3 and Cross-Chapter Box 2.3). This evolving change has been documented in previous assessment reports, with each reporting a higher total global temperature change (Section 1.3 and Cross-Chapter Box 1.2). The total change in global surface air temperature (GSAT) (Section 1.4.1 and Cross-Chapter Box 2.3) attributable to anthropogenic activities is assessed to be consistent with the observed change in GSAT (Section 3.3). 1

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Similarly, atmospheric concentrations of a range of GHGs are increasing. Carbon dioxide (CO2, shown in Figure 1.4 and Figure 1.5a, found in AR5 and earlier reports to be the current strongest driver of anthropogenic climate change), has increased from 285.5 ± 2.1 ppm in 1850 to 409.9 ± 0.4 ppm in 2019; concentrations of methane (CH4), and nitrous oxide (N2O) have increased as well (Sections 2.2 and 5.2, and Annex V). These observed changes are assessed to be in line with known anthropogenic and natural emissions, when accounting for observed and inferred uptake by land, ocean and biosphere respectively (Section 5.2), and are a key source of anthropogenic changes to the global energy balance (or radiative forcing; Sections 2.2 and 7.3).

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An important time period in the assessment of anthropogenic climate change is the last 2 kyr. Since AR5, new global datasets have been produced that aggregate aggregating local and regional paleorecords (PAGES 2k Consortium, 2013, 2017, 2019; McGregor et al., 2015; Tierney et al., 2015; Abram et al., 2016; Hakim et al., 2016; Steiger et al., 2018; Brönnimann et al., 2019b). Before the global warming that began around the mid-19th century (Abram et al., 2016), a slow cooling in the Northern Hemisphere from roughly 1450–1850 CE is consistently recorded in paleoclimate archives (PAGES 2k Consortium, 2013; McGregor et al., 2015). While this cooling, primarily driven by an increased number of volcanic eruptions (Section 3.3.1; PAGES 2k Consortium, 2013; Owens et al., 2017; Brönnimann et al., 2019b), shows regional differences, the subsequent warming over the past 150 years exhibits a global coherence that is unprecedented in the last 2 kyr (Neukom et al., 2019).

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The rate, scale and magnitude of anthropogenic changes in the climate system since the mid-20th century suggested the definition of a new geological epoch: the Anthropocene (Crutzen and Stoermer, 2000; Steffen et al., 2007), referring to an era in which human activity is altering major components of the Earth system and leaving measurable imprints that will remain in the permanent geological record (Figure 1.5; IPCC, 2018). These alterations include not only climate change itself, but also chemical and biological changes in the Earth system such as rapid ocean acidification due to uptake of anthropogenic CO2, massive destruction of tropical forests, a worldwide loss of biodiversity and the sixth mass extinction of species (Hoegh-Guldberg and Bruno, 2010; Ceballos et al., 2017; IPBES, 2019). According to the key messages of the last global assessment of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES, 2019), climate change is a ‘direct driver that is increasingly exacerbating the impact of other drivers on nature and human well-being’, and ‘the adverse impacts of climate change on biodiversity are projected to increase with increasing warming.’

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The IPCC First Assessment Report (FAR, IPCC, 1990a) provided the scientific background for the establishment of the UNFCCC (UNFCCC, 1992), which committed parties to negotiate ways to ‘prevent dangerous anthropogenic interference with the climate system’ (the ultimate objective of the UNFCCC). The Second Assessment Report (SAR, IPCC, 1996) informed governments in negotiating the Kyoto Protocol (1997), the first major agreement focusing on mitigation under the UNFCCC. The Third Assessment report (TAR, IPCC, 2001a) highlighted the impacts of climate change and the need for adaptation, and introduced the treatment of new topics such as policy and governance in IPCC reports. The Fourth and Fifth Assessment Reports (AR4, IPCC, 2007a; AR5, IPCC, 2013a) provided the scientific background for the second major agreement under the UNFCCC: the Paris Agreement (2015), which entered into force in 2016.

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The PA further addresses mitigation (Article 4) and adaptation to climate change (Article 7), as well as loss and damage (Article 8), through the mechanisms of finance (Article 9), technology development and transfer (Article 10), capacity-building (Article 11) and education (Article 12). To reach its long-term temperature goal, the PA recommends ‘achieving a balance between anthropogenic emissions by sources and removals by sinks of greenhouse gases in the second half of this century’, a state commonly described as ‘net zero’ emissions (Article 4) (Section 1.6 and Box 1.4). Each Party to the PA is required to submit a Nationally Determined Contribution (NDC) and pursue, on a voluntary basis, domestic mitigation measures with the aim of achieving the objectives of its NDC (Article 4).

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Addressing climate change alongside other environmental problems, while simultaneously supporting sustainable socio-economic development, requires a holistic approach. Since AR5, there is increasing attention on the need for coordination among previously independent international agendas, and a recognition that climate change, disaster risk, economic development, biodiversity conservation and human well-being are tightly interconnected. The current COVID-19 pandemic provides an example of the need for such interconnection, with its widespread impacts on economy, society and environment (e.g., Shan et al., 2021). Cross-Chapter Box 6.1 assesses the consequences of the COVID-19 lockdowns for emissions of GHGs and SLCFs, and related implications for the climate. Another example of the interconnected nature of these issues is the close link between SLCF emissions, climate change and air quality concerns (Chapter 6). Emissions of halocarbons have previously been successfully regulated under the Montreal Protocol and its Kigali Amendment. This has been achieved in an effort to reduce ozone depletion that has also modulated other anthropogenic climate influence (Estrada et al., 2013; Wu et al., 2013). In the process, emissions of some SLCFs were jointly regulated to reduce environmental and health impacts from air pollution (e.g., Gothenburg Protocol; Reis et al., 2012). Considering the recognized importance of SLCFs in climate change processes, the IPCC decided in May 2019 to approve that the IPCC Task Force on National Greenhouse Gas Inventories produces an IPCC Methodology Report on SLCFs to develop guidance for national SLCF inventories.

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The AR5 WGI highlighted ‘the other CO2 problem’ (Doney et al., 2009), that is, ocean acidification caused by the absorption of some 20–30% of anthropogenic CO2 from the atmosphere and its conversion to carbonic acid in seawater. The AR5 WGI assessed that the pH of ocean surface water has decreased by 0.1 since the beginning of the industrial era (high confidence), indicating approximately a 30% increase in acidity (IPCC, 2013b).

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With the gradual acceptance of evidence for geological ‘deep time’ in the 19th century came investigation of fossils, geological strata, and other evidence pointing to large shifts in the Earth’s climate, from ice ages to much warmer periods, across thousands to billions of years. This awareness set off a search for the causes of climatic changes. The long-term perspective provided by paleoclimate studies is essential to understanding the causes and consequences of natural variations in climate, as well as crucial context for recent anthropogenic climatic change. The reconstruction of climate variability and change over recent millennia began in the 1800s (Brückner, 1890; Stehr and von Storch, 2000; Coen, 2018, 2020). In brief, paleoclimatology reveals the key role of CO2 and other greenhouse gases in past climatic variability and change, the magnitude of recent climate change in comparison to past glacial–interglacial cycles, and the unusualness of recent climate change (Section 1.2.1.2 and Cross-Chapter Box 2.1; Tierney et al., 2020a). FAQ 1.3 provides a plain-language summary of its importance.

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The natural and anthropogenic factors responsible for climate change are known today as radiative ‘drivers’ or ‘forcers’. The net change in the energy budget at the top of the atmosphere, resulting from a change in one or more such drivers, is termed ‘radiative forcing’ (RF; Glossary) and measured in watts per square metre (W m–2). The total radiative forcing over a given time interval (often since 1750) represents the sum of positive drivers (inducing warming) and negative ones (inducing cooling). Past IPCC reports have assessed scientific knowledge of these drivers, quantified their range for the period since 1750, and presented the current understanding of how they interact in the climate system. Like all previous IPCC reports, AR5 assessed that total radiative forcing has been positive at least since 1850–1900, leading to an uptake of energy by the climate system, and that the largest single contribution to total radiative forcing is the rising atmospheric concentration of CO2 since 1750 (Chapter 7, and Cross-Chapter Box 1.2; IPCC, 2013a).

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Anthropogenic drivers of climatic change were hypothesized as early as the 17th century, with a primary focus on forest clearing and agriculture (Grove, 1995; Fleming, 1998). In the 1890s, Arrhenius was first to calculate the effects of increased or decreased CO2 concentrations on planetary temperature, and Högbom estimated that worldwide coal combustion of about 500 Mt yr–1had already completely offset the natural absorption of CO2 silicate rock weathering (Högbom, 1894; Arrhenius, 1896; Berner, 1995; Crawford, 1997). As coal consumption reached 900 Mt yr–1only a decade later, Arrhenius wrote that anthropogenic CO2 from fossil fuel combustion might eventually warm the planet (Arrhenius, 1908). In 1938, analysing records from 147 stations around the globe, Callendar calculated atmospheric warming over land at 0.3°C–0.4°C from 1880–1935 and attributed about half of this warming to anthropogenic CO2 (Figure 1.8; Callendar, 1938; Fleming, 2007; Hawkins and Jones, 2013).

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Studiesof radiocarbon (14C) in the 1950s established that increasing atmospheric CO2 concentrations were due to fossil fuel combustion. Since all the14C once contained in fossil fuels long ago decayed into non-radioactive12C, the CO2 produced by their combustion reduces the overall concentration of atmospheric14C (Suess, 1955). Related work demonstrated that while the ocean was absorbing around 30% of anthropogenic CO2, these emissions were also accumulating in the atmosphere and biosphere (Section 1.3.1 and Chapter 5, Section 5.2.1.5). Further work later established that atmospheric oxygen levels were decreasing in inverse relation to the anthropogenic CO2 increase, because combustion of carbon consumes oxygen to produce CO2 (Chapters 2 and 6; Keeling and Shertz, 1992; IPCC, 2013a). Revelle and Suess (1957) famously described fossil fuel emissions as a ‘large scale geophysical experiment’, in which ‘within a few centuries we are returning to the atmosphere and ocean the concentrated organic carbon stored in sedimentary rocks over hundreds of millions of years.’ The 1960s saw increasing attention to other radiatively active gases, especially ozone (O3; Manabe and Möller, 1961; Plass, 1961). Methane and nitrous oxide (N2O) were not considered systematically until the 1970s, when anthropogenic increases in those gases were first noted (Wang et al., 1976). In the 1970s and 1980s, scientists established that synthetic halocarbons (see Glossary), including widely used refrigerants and propellants, were extremely potent greenhouse gases (Sections 2.2.4.3 and 6.2.2.9; Ramanathan, 1975). When these chemicals were also found to be depleting the stratospheric ozone layer, they were stringently and successfully regulated on a global basis by the 1987 Montreal Protocol on the Ozone Layer and successor agreements (Parson, 2003).

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Radioactive fallout from atmospheric nuclear weapons testing (1940s–1950s) and urban smog (1950s–1960s) first provoked widespread attention to anthropogenic aerosols and ozone in the troposphere (Edwards, 2012). Theory, measurement and modelling of these substances developed steadily from the 1950s (Hidy, 2019). However, the radiative effects of anthropogenic aerosols did not receive sustained study until around 1970 (Bryson and Wendland, 1970; Rasool and Schneider, 1971), when their potential as cooling agents was recognized (Peterson et al., 2008). The US Climatic Impact Assessment Program (CIAP) found that proposed fleets of supersonic aircraft, flying in the stratosphere, might cause substantial aerosol cooling and depletion of the ozone layer, stimulating efforts to understand and model stratospheric circulation, atmospheric chemistry, and aerosol radiative effects (Mormino et al., 1975; Toon and Pollack, 1976). Since the 1980s, aerosols have increasingly been integrated into comprehensive modelling studies of transient climate evolution and anthropogenic influences, through treatment of volcanic forcing, links to global dimming and cloud brightening, and their influence on cloud nucleation and other properties (e.g., thickness, lifetime and extent), and precipitation (e.g., Hansen et al., 1981; Charlson et al., 1987, 1992; Albrecht, 1989; Twomey, 1991).

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The FAR (1990) focused attention on human emissions of CO2, CH4, tropospheric O3, chlorofluorocarbons (CFCs), and N2O. Of these, at that time only the emissions of CO2 and CFCs were well measured, with methane sources known only ‘semi-quantitatively’ (IPCC, 1990a). The FAR assessed that some other trace gases, especially CFCs, have global warming potentials hundreds to thousands of times greater than CO2 and CH4, but are emitted in much smaller amounts. As a result, CO2 remains by far the most important positive anthropogenic driver, with CH4 next most significant (Section 1.6.3); anthropogenic methane stems from such sources as fossil fuel extraction, natural gas pipeline leakage, agriculture and landfills. In 2001, increased greenhouse forcing attributable to CO2, CH4, O3, CFC-11 and CFC-12 was detected by comparing satellite measurements of outgoing longwave radiation measurements taken in 1970 and in 1997 (Harries et al., 2001). AR5 assessed that the 40% increase in atmospheric CO2 contributed most to positive RF since 1750. Together, changes in atmospheric concentrations of CO2, CH4, N2O and halocarbons from 1750–2011 were assessed to contribute a positive RF of 2.83 [2.26 to 3.40] W m–2 (IPCC, 2013b).

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All IPCC reports have assessed the total RF as positive when considering all sources. However, due to the considerable variability of both natural and anthropogenic aerosol loads, FAR characterized total aerosol RF as ‘highly uncertain’ and was unable even to determine its sign (positive or negative). Major advances in quantification of aerosol loads and their effects have taken place since then, and IPCC reports since 1992 have consistently assessed total forcing by anthropogenic aerosols as negative (IPCC, 1992, 1995a, 1996). However, due to their complexity and the difficulty of obtaining precise measurements, aerosol effects have been consistently assessed as the largest single source of uncertainty in estimating total RF (Stevens and Feingold, 2009; IPCC, 2013a). Overall, AR5 assessed that total aerosol effects, including cloud adjustments, resulted in a negative RF of –0.9 [–1.9 to −0.1] W m−2 (medium confidence), offsetting a substantial portion of the positive RF resulting from the increase in GHGs (high confidence) (IPCC, 2013b). Chapter 7 provides an updated assessment of the total and per-component RF for the WGI contribution to AR6.

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Understanding the global climate system requires both theoretical understanding and empirical measurement of the major forces and factors that govern the transport of energy and mass (air, water and water vapour) around the globe; the chemical and physical properties of the atmosphere, ocean, cryosphere and land surfaces; and the biological and physical dynamics of natural ecosystems, as well as the numerous feedbacks (both positive and negative) among these processes. Attributing climatic changes or extreme weather events to human activity (Cross-Working Group Box: Attribution) also requires an understanding of the many ways that human activities may affect the climate, along with statistical and other techniques for separating the ‘signal’ of anthropogenic climate change from the ‘noise’ of natural climate variability (Section 1.4.2). This inter- and trans-disciplinary effort requires contributions from many sciences.

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Since climate models vary along many dimensions, such as grid type, resolution, and parameterizations, comparing their results requires special techniques. To address this problem, the climate modelling community developed increasingly sophisticated model intercomparison projects (MIPs; Gates et al., 1999; Covey et al., 2003). MIPs prescribe standardized experiment designs, time periods, output variables or observational reference data to facilitate direct comparison of model results. This aids in diagnosing the reasons for biases and other differences among models, and furthers process understanding (Section 1.5). Both the CMIP3 and CMIP5 model intercomparison projects included experiments testing the ability of models to reproduce 20th-century global surface temperature trends both with and without anthropogenic forcings. Although some individual model runs failed to achieve this (Hourdin et al., 2017), the mean trends of multi-model ensembles did so successfully (Meehl et al., 2007a; Taylor et al., 2012). When only natural forcings were included (creating the equivalent of a ‘control Earth’ without human influence), similar multi-model ensembles could not reproduce the observed post-1970 warming at either global or regional scales (Edwards, 2010; Jones et al., 2013). The GCMs and ESMs compared in CMIP6 (used in this Report) offer more explicit documentation and evaluation of tuning procedures (Section 1.5; Schmidt et al., 2017; Burrows et al., 2018; Mauritsen and Roeckner, 2020).

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The FAR (IPCC, 1990a) concluded that while both theory and models suggested that anthropogenic warming was already well underway, its signal could not yet be detected in observational data against the ‘noise’ of natural variability (see also Section 1.4.2; and Barnett and Schlesinger, 1987). Since then, increased warming and progressively more conclusive attribution studies have identified human activities as the ‘dominant cause of the observed warming since the mid-20th century’ (IPCC, 2013b). ‘Fingerprint’ studies seek to detect specific observed changes – expected from theoretical understanding and model results – that could not be explained by natural drivers alone, and to attribute statistically the proportion of such changes that is due to human influence. These include global-scale surface warming, nights warming faster than days, tropospheric warming and stratospheric cooling, a rising tropopause, increasing ocean heat content, changed global patterns of precipitation and sea level air pressure, increasing downward longwave radiation, and decreasing upward longwave radiation (Hasselmann, 1979; Karoly et al., 1994; Schneider, 1994; Santer et al., 1995, 2013; Hegerl et al., 1996, 1997; Gillett et al., 2003; Santer, 2003; Zhang et al., 2007; Stott et al., 2010; Davy et al., 2017; Mann et al., 2017). The Cross-Working Group Box on Attribution outlines attribution methods and uses from across AR6, now including event attribution (specifying the influence of climate change on individual extreme events such as floods, or on the frequency of classes of events such as tropical cyclones). Overall, the evidence for human influence has grown substantially over time and from each IPCC report to the next.

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A key indicator of climate understanding is whether theoretical climate system budgets or ‘inventories’, such as the balance of incoming and outgoing energy at the surface and at the top of the atmosphere, can be quantified and balanced observationally. The global energy budget, for example, includes energy retained in the atmosphere, upper ocean, deep ocean, ice, and land surface. Church et al. (2013) assessed in AR5 with high confidence that independent estimates of effective radiative forcing (ERF), observed heat storage, and surface warming combined to give an energy budget for the Earth that is consistent with the AR5 WGI assessed likely range of equilibrium climate sensitivity (ECS) [1.5°C to 4.5°C] to within estimated uncertainties (on ECS, see (Section 1.3.5; IPCC, 2013a). Similarly, over the period 1993–2010, when observations of all sea level components were available, AR5 WGI assessed the observed global mean sea level rise to be consistent with the sum of the observed contributions from ocean thermal expansion (due to warming) combined with changes in glaciers, the Antarctic and Greenland ice sheets, and land-water storage (high confidence). Verification that the terms of these budgets balance over recent decades provides strong evidence for our understanding of anthropogenic climate change (Cross-Chapter Box 9.1).

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The Appendix to (Chapter 1 (Appendix 1A) lists the key detection and attribution statements in the Summaries for Policymakers of WGI reports since 1990. The evolution of these statements over time reflects the improvement of scientific understanding and the corresponding decrease in uncertainties regarding human influence. The Second Assessment Report (SAR) stated that ‘the balance of evidence suggests a discernible human influence on global climate’ (IPCC, 1995b). Five years later, the Third Assessment Report (TAR) concluded that ‘there is new and stronger evidence that most of the warming observed over the last 50 years is attributable to human activities’ (IPCC, 2001b). The AR4 further strengthened previous statements, concluding that ‘most of the observed increase in global average temperatures since the mid-20th century is very likely due to the observed increase in anthropogenic greenhouse gas concentrations’ (IPCC, 2007b). The AR5 assessed that a human contribution had been detected in: changes in warming of the atmosphere and ocean; changes in the global water cycle; reductions in snow and ice; global mean sea level rise; and changes in some climate extremes. The AR5 concluded that ‘it is extremely likely that human influence has been the dominant cause of the observed warming since the mid-20th century’ (IPCC, 2013b).

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From the close link between cumulative emissions and warming it follows that any given level of global warming is associated with a total budget of GHG emissions, especially CO2 as it is the largest long-lived contributor to radiative forcing (Allen et al., 2009; Collins et al., 2013; Rogelj et al., 2019). Higher emissions in earlier decades imply lower emissions later on to stay within the Earth’s carbon budget. Stabilizing the anthropogenic influence on global surface temperature thus requires that CO2 emissions and removals reach net zero once the remaining carbon budget is exhausted (Cross-Chapter Box 1.4).

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Although this approach has limitations when the modelled forcings differ greatly from the forcings subsequently experienced, they were generally able to project actual future global warming when the mismatches between forecast and observed radiative forcings are accounted for. For example, Scenario B presented in Hansen et al. (1988) projected around 50% more warming than has been observed during the 1988–2017 period, but this is largely because it overestimated subsequent radiative forcings. Similarly, while FAR (IPCC, 1990a) projected a higher rate of global surface temperature warming than has been observed, this is largely because it overestimated future GHG concentrations: FAR’s projected increase in total anthropogenic forcing between 1990 and 2017 was 1.6 W m–2, while the observational estimate of actual forcing during that period is 1.1 W m–2 (Dessler and Forster, 2018). Under these actual forcings, the change in temperature in FAR aligns with observations (Hausfather et al., 2020).

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The SR1.5 estimated with high confidence that human activities caused a global warming of approximately 1°C between the 1850–1900 period and 2017. For the period 2006–2015, observed global mean surface temperature (GMST7) was 0.87°C ± 0.12°C higher than the average over the 1850–1900 period (very high confidence). Anthropogenic global warming was estimated to be increasing at 0.2 ± 0.1°C per decade (high confidence) and likely matches the level of observed warming to within ±20%. The SRCCL found with high confidence that over land, mean surface air temperature increased by 1.53°C ± 0.15°C between 1850–1900 and 2006–2015, or nearly twice as much as the global average. This observed warming has already led to increases in the frequency and intensity of climate and weather extremes in many regions and seasons, including heat waves in most land regions (high confidence), increased droughts in some regions (medium confidence), and increases in the intensity of heavy precipitation events at the global scale (medium confidence). These climate changes have contributed to desertification and land degradation in many regions (high confidence). Increased urbanization can enhance warming in cities and their surroundings (heat island effect), especially during heat waves (high confidence), and intensify extreme rainfall (medium confidence).

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The SROCC assessed that anthropogenic climate change has increased observed precipitation (medium confidence), winds (low confidence), and extreme sea level events (high confidence) associated with some tropical cyclones. It also found evidence for an increase in the annual global proportion of Category 4 or 5 tropical cyclones in recent decades (low confidence).

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The SRCCL stated that the land is simultaneously a source and sink of CO2, due to both anthropogenic and natural drivers. It estimates with medium confidence that agriculture, forestry and other land use (AFOLU) activities accounted for around 13% of CO2, 44% of CH4, and 82% of N2O emissions from human activities during 2007–2016, representing 23% (12.0 ± 3.0 GtCO2 equivalent yr–1) of the total net anthropogenic emissions of GHGs. The natural response of land to human-induced environmental change – such as increasing atmospheric CO2 concentration, nitrogen deposition and climate change – caused a net CO2 sink equivalent of around 29% of total CO2 emissions (medium confidence); however, the persistence of the sink is uncertain due to climate change (high confidence).

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The SR1.5 concluded that global warming is likely to reach 1.5°C between 2030 and 2052 if it continues to increase at the current rate (high confidence). However, even though warming from anthropogenic emissions will persist for centuries to millennia and will cause ongoing long-term changes, past emissions alone are unlikely to raise global surface temperature to 1.5°C above 1850–1900 levels.

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The SR1.5 also found that reaching and sustaining net zero anthropogenic CO2 emissions and reducing net non-CO2 radiative forcing would halt anthropogenic global warming on multi-decadal time scales (high confidence). The maximum temperature reached is then determined by (i) cumulative net global anthropogenic CO2 emissions up to the time of net zero CO2 emissions (high confidence) and (ii) the level of non-CO2 radiative forcing in the decades prior to the time that maximum temperatures are reached (medium confidence).

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The SR1.5 focused on emissions pathways and system transitions consistent with 1.5°C global warming over the 21st century. Building upon the understanding from AR5 WGI of the quasi-linear relationship between cumulative net anthropogenic CO2 emissions since 1850–1900 and maximum global mean temperature, the Report assessed the remaining carbon budgets compatible with the 1.5°C or 2°C warming goals of the Paris Agreement. Starting from year 2018, the remaining carbon budget for a one-in-two (50%) chance of limiting global warming to 1.5°C is about 580 GtCO2, and about 420 GtCO2 for a two-in-three (66%) chance (medium confidence).

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It is concluded that all emissions pathways with no or limited overshoot of 1.5°C imply that global net anthropogenic CO2 emissions would need to decline by about 45% from 2010 levels by 2030, reaching net zero around 2050, together with deep reductions in other anthropogenic emissions, such as methane and black carbon. To limit global warming to below 2°C, CO2 emissions would have to decline by about 25% by 2030 and reach net zero around 2070.

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The Paris Agreement aims to limit global temperatures to specific thresholds ‘above pre-industrial levels’. In AR6 WGI, as in previous IPCC reports, observations and projections of changes in global temperature are generally expressed relative to 1850–1900 as an approximate pre-industrial state (SR1.5, IPCC, 2018). This is a pragmatic choice based upon data availability considerations, though both anthropogenic and natural changes to the climate occurred before 1850. The remaining carbon budgets, the chance of crossing global temperature thresholds, and projections of extremes and sea level rise at a particular level of global warming can all be sensitive to the chosen definition of the approximate pre-industrial baseline (Millar et al., 2017b; Schurer et al., 2017; Pfleiderer et al., 2018; Rogelj et al., 2019; Tokarska et al., 2019). This Cross-Chapter Box assesses the evidence on change in radiative forcing and global temperature from the period around 1750 to 1850–1900; variations in the climate before 1750 are discussed in Chapter 2.

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Here weassess improvements in our understanding of climatic changes in the period 1750–1850. Anthropogenic influences on climate between 1750 and 1900 were primarily increased anthropogenic GHG and aerosol emissions, and changes in land use. Between 1750 and 1850 atmospheric CO2 levels increased from about 278 ppm to about 285 ppm (equivalent to around 3 years of current rates of increase; Chapter 2, Section 2.2.3), corresponding to about 55 GtCO2 in the atmosphere. Estimates of emissions from fossil fuel burning (about 4 GtCO2, Boden et al., 2017) cannot explain the pre-1850 increase, so CO2 emissions from land-use changes are implicated as the dominant source. The atmospheric concentration of other GHGs also increased over the same period, and there was a cooling influence from other anthropogenic radiative forcings (such as aerosols and land-use changes), but with a larger uncertainty than for GHGs (Sections 2.2.6 and 7.3.5.2, and Cross-Chapter Box 1.2, Figure 1; e.g., Carslaw et al., 2017;Owens et al., 2017; Hamilton et al., 2018). It is likely that there was a net anthropogenic forcing of 0.00.3 Wm–2 in 18501900 relative to 1750 (medium confidence). The net radiative forcing from changes in solar activity and volcanic activity in 18501900, compared to the period around 1750, is estimated to be smaller than ±0.1 W m–2, but note there were several large volcanic eruptions between 1750 and 1850 (Cross-Chapter Box 1.2, Figure 1).

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Combining these different sources of evidence, we assess that from the period around 1750 to 1850–1900 there was a change in global temperature of around 0.1 [–0.1 to +0.3] °C (medium confidence), with an anthropogenic component in a likely range of 0.0°C–0.2°C (medium confidence).

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Cross-Chapter Box 1.2, Figure 1 | Changes in radiative forcing from 1750–2019. The radiative forcing estimates from the AR6 emulator (Cross-Chapter Box 7.1) are split into GHG, other anthropogenic (mainly aerosols and land use) and natural forcings, with the average over the 1850–1900 baseline shown for each. Further details on data sources and processing are available in the chapter data table (Table 1.SM.1).

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Climatic changes since the pre-industrial era are a combination of long-term anthropogenic changes and natural variations on time scales from days to decades. The relative importance of these two factors depends on the climate variable or region of interest. Natural variations consist of both natural radiatively forced trends (e.g., due to volcanic eruptions or solar variations) and ‘internal’ fluctuations of the climate system which occur even in the absence of any radiative forcings. The internal ‘modes of variability’, such as the El Niño–Southern Oscillation (ENSO) and the North Atlantic Oscillation (NAO), are discussed further in Annex IV.

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Natural variations in both weather and longer time scale phenomena can temporarily mask or enhance any anthropogenic trends (e.g., Deser et al., 2012; Kay et al., 2015). These effects are more important on small spatial and temporal scales but can also occur on the global scale (Cross-Chapter Box 3.1).

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Since AR5, many studies have examined the role of internal variability through the use of ‘large ensembles’. Each such ensemble consists of many different simulations by a single climate model for the same time period and using the same radiative forcings. These simulations differ only in their phasing of the internal climate variations (also see Section 1.5.4.2). A set of illustrative examples using one such large ensemble (Maher et al., 2019) demonstrates how variability can influence trends on decadal time scales (Figure 1.13). The long-term anthropogenic trends in this set of climate indicators are clearly apparent when considering the ensemble as a whole (grey shading), and all the individual ensemble members have very similar trends for ocean heat content (OHC), which is a robust estimate of the total energy stored in the climate system (e.g., Palmer and McNeall, 2014). However, the individual ensemble members can exhibit very different decadal trends in global surface air temperature (GSAT), UK summer temperatures, and Arctic sea ice variations. More specifically, for a representative 11-year period, both positive and negative trends can be found in all these surface indicators, even though the long-term trend is for increasing temperatures and decreasing sea ice. Periods in which the long-term trend is substantially masked or enhanced for more than 20 years are also visible in these regional examples. This highlights the fact that observations are expected to exhibit short-term trends which are larger or smaller than the long-term trend or that differ from the average projected trend from climate models, especially on continental spatial scales or smaller (Cross-Chapter Box 3.1). The actual observed trajectory can be considered as one realization of many possible alternative worlds that experienced different weather; this is also demonstrated by the construction of ‘observation-based large ensembles’, which are alternate possible realizations of historical observations that retain the statistical properties of observed regional weather (e.g., McKinnon and Deser, 2018).

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Future radiative forcing is uncertain due to as-yet-unknown societal choices that will determine future anthropogenic emissions; this is considered ‘scenario uncertainty’. The RCP and SSP scenarios, which form the basis for climate projections assessed in this Report, are designed to span a plausible range of future pathways (Section 1.6) and can be used to estimate the magnitude of scenario uncertainty, but the real world may also differ from any one of these example pathways.

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Uncertainties also exist regarding past emissions and radiative forcings. These are especially important for simulations of paleoclimate time periods, such as the Pliocene, Last Glacial Maximum or the last millennium, but are also relevant for the CMIP historical simulations of the instrumental period since 1850. In particular, historical radiative forcings due to anthropogenic and natural aerosols are less well constrained by observations than the GHG radiative forcings. There is also uncertainty in the size of large volcanic eruptions (and in the location for some that occurred before around 1850), and the amplitude of changes in solar activity, before satellite observations. The role of historical radiative forcing uncertainty was considered previously (Knutti et al., 2002; Forster et al., 2013) but, since AR5, specific simulations have been performed to examine this issue, particularly for the effects of uncertainty in anthropogenic aerosol radiative forcing (e.g., Jiménez-de-la-Cuesta and Mauritsen, 2019; Dittus et al., 2020).

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Even without any anthropogenic radiative forcing, there would still be uncertainty in projecting future climate because of unpredictable natural factors such as variations in solar activity and volcanic eruptions. For projections of future climate, such as those presented in Chapter 4, the uncertainty in these factors is not normally considered. However, the potential effects on the climate of large volcanic eruptions (Cross-Chapter Box 4.1; Zanchettin et al., 2016; Bethke et al., 2017) and large solar variations (Feulner and Rahmstorf, 2010; Maycock et al., 2015) are studied. On longer time scales, orbital effects and plate tectonics also play a role.

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Further, even in the absence of any anthropogenic or natural changes in radiative forcing, Earth’s climate fluctuates on time scales from days to decades or longer. These ‘internal’ variations, such as those associated with modes of variability (e.g., ENSO, Pacific Decadal Variability (PDV), or Atlantic Multi-decadal Variability (AMV); Annex IV) are unpredictable on time scales longer than a few years ahead and are a source of uncertainty for understanding how the climate might become in a particular decade, especially regionally. The increased use of ‘large ensembles’ of complex climate model simulations to sample this component of uncertainty is discussed above in Section 1.4.2.1 and further in Chapter 4.

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It is plausible that there are interactions between radiative forcings and climate variations, such as influences on the phasing or amplitude of internal or natural climate variability (Zanchettin, 2017). For example, the timing of volcanic eruptions may influence Atlantic Multi-decadal Variability (e.g., Otterå et al., 2010; Birkel et al., 2018) or ENSO (e.g., Maher et al., 2015; Khodri et al., 2017; Zuo et al., 2018), and anthropogenic aerosols may influence decadal modes of variability in the Pacific (e.g., Smith et al., 2016). In addition, melting of glaciers and ice caps due to anthropogenic influences has been speculated to increase volcanic activity (e.g., a specific example for Iceland is discussed in Swindles et al., 2018).

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Other sources of uncertainty, such as model response uncertainty, can in principle be reduced, but are not amenable to a frequency-based interpretation of probability, and Bayesian methods to quantify the uncertainty have been considered instead (e.g., Tebaldi, 2004; Rougier, 2007; Sexton et al., 2012). The scenario uncertainty component is distinct from other uncertainties, given that future anthropogenic emissions can be considered as the outcome of a set of societal choices (Section 1.6.1).

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There is evidence of abrupt changes in Earth’s history, and some of these events have been interpreted as tipping points (Dakos et al., 2008). Some of these are associated with significant changes in the global climate, such as deglaciations in the Quaternary (past 2.5 million years) and rapid warming at the Palaeocene–Eocene Thermal Maximum (around 55.5 million years ago; Bowen et al., 2015; Hollis et al., 2019). Such events changed the planetary climate for tens to hundreds of thousands of years, but at a rate that is actually much slower than projected anthropogenic climate change over this century, even in the absence of tipping points.

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Such paleoclimate evidence has even fuelled concerns that anthropogenic GHGs could tip the global climate into a permanent hot state (Steffen et al., 2018). However, there is no evidence of such non-linear responses at the global scale in climate projections for the next century, which indicates a near-linear dependence of global temperature on cumulative GHG emissions (Sections 1.3.5, 5.5 and 7.4.3.1). At the regional scale, abrupt changes and tipping points, such as Amazon rainforest dieback and permafrost collapse, have occurred in projections with Earth System Models (Section 4.7.3; Drijfhout et al., 2015; Bathiany et al., 2020). In such simulations, tipping points occur in narrow regions of parameter space (e.g., CO2 concentration or temperature increase), and for specific climate background states. This makes them difficult to predict using Earth system models (ESMs) relying on parmeterizations of known processes. In some cases, it is possible to detect forthcoming tipping points through time-series analysis that identifies increased sensitivity to perturbations as the tipping point is approached (e.g., ‘critical slowing-down’, Scheffer et al., 2012).

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The tipping point concept is most commonly framed for systems in which the forcing changes relatively slowly. However, this is not the case for most scenarios of anthropogenic forcing projected for the 21st century. Systems with inertia lag behind rapidly increasing forcing, which can lead to the failure of early warning signals or even the possibility of temporarily overshooting a bifurcation point without provoking tipping (Ritchie et al., 2019).

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Major paleoreconstruction efforts completed since AR5 include a variety of large-scale, multi-proxy temperature datasets and associated reconstructions spanning the last 2000 years (PAGES 2k Consortium, 2017, 2019; Neukom et al., 2019), the Holocene (Kaufman et al., 2020), the Last Glacial Maximum (Cleator et al., 2020; Tierney et al., 2020b), the mid-Pliocene Warm Period (McClymont et al., 2020), and the Early Eocene Climatic Optimum (Hollis et al., 2019). Newly compiled borehole data (Cuesta-Valero et al., 2019), as well as advances in statistical applications to tree ring data, result in more robust reconstructions of key indices such as Northern Hemisphere temperature over the last millennium (e.g., Wilson et al., 2016; Anchukaitis et al., 2017). Such reconstructions provide a new context for recent warming trends (Chapter 2) and serve to constrain the response of the climate system to natural and anthropogenic forcing (Chapters 3 and 7).

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An emergent constraint is the relationship between an uncertain aspect of future climate change and an observable feature of the Earth System, evident across an ensemble of models (Allen and Ingram, 2002; Mystakidis et al., 2016; Wenzel et al., 2016; Hall et al., 2019; Winkler et al., 2019). Complex Earth system models (ESMs) simulate variations on time scales from hours to centuries, telling us how aspects of the current climate relate to its sensitivity to anthropogenic forcing. Where an ensemble of different ESMs displays a relationship between a short-term observable variation and a longer-term sensitivity, an observation of the short-term variation in the real world can be converted, via the model-based relationship, into an ‘emergent constraint’ on the sensitivity. This is shown schematically in Figure 1.23 (see Glossary; Eyring et al., 2019).

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Emergent constraints use the spread in model projections to estimate the sensitivities of the climate system to anthropogenic forcing, providing another type of ensemble-wide information that is not readily available from simulations with one ESM alone. As emergent constraints depend on identifying those observable aspects of the climate system that are most related to climate projections, they also help to focus model evaluation on the most relevant observations (Hall et al., 2019). However, there is a chance that indiscriminate data-mining of the multi-dimensional outputs from ESMs could lead to spurious correlations (Caldwell et al., 2014; Wagman and Jackson, 2018) and less-than-robust emergent constraints on future changes (Bracegirdle and Stephenson, 2013). To avoid this, emergent constraints need to be tested ‘out of sample’ on parts of the dataset that were not included in its construction (Caldwell et al., 2018) and should also always be based on sound physical understanding and mathematical theory (Hall et al., 2019). Their conclusions should also be reassessed when a new generation of MMEs becomes available, such as CMIP6. As an example, Chapter 7 (Section 7.5.4) discusses and assesses recent studies where equilibrium climate sensitivities (ECS) diagnosed in a multi-model ensemble are compared with the same models’ estimates of an observable quantity, such as post-1970s global warming or tropical sea surface temperatures of past climates like the Last Glacial Maximum or the Pliocene. Assessments of other emergent constraints appear throughout later chapters, such as Chapter 4 (Section 4.2.5), Chapter 5 (Section 5.4.6) and Chapter 7 (Section 7.5.4).

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The scenario generation process involves research communities linked to all three IPCC Working Groups (Figure 1.27). It generally starts in the scientific communities associated with WGII and WGIII with the definition of new socio-economic scenario storylines (IPCC, 2000; O’Neill et al., 2014) that are quantified in terms of their drivers – i.e., GDP, population, technology, energy and land use – and their resulting emissions (Riahi et al., 2017). Then, numerous complementation and harmonization steps are necessary for datasets within the WGI and WGIII science communities, including gridding emissions of anthropogenic short-lived forcers, providing open biomass-burning emissions estimates, preparing land-use patterns, aerosol fields, stratospheric and tropospheric ozone, nitrogen deposition datasets, solar irradiance and aerosol optical property estimates, and observed and projected GHG concentration time series (documented for CMIP6 through input4mips; Cross-Chapter Box 1.4, Table 2; Durack et al., 2018).

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Article 4 of the Paris Agreement sets an objective to ‘achieve a balance between anthropogenic emissions by sources and removals by sinks of greenhouse gases’ (Section 1.2). This box addresses the relationship between such a balance and the corresponding evolution of global surface temperature, with or without the deployment of large-scale carbon dioxide removal (CDR), using the definitions of ‘net zero CO2 emissions’ and ‘net zero greenhouse gas (GHG) emissions’ of the AR6 Glossary (Annex VII).

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‘Net zero CO2 emissions’ is defined in AR6 as the condition in which anthropogenic CO2 emissions are balanced by anthropogenic CO2 removals over a specified period. Similarly, ‘net zero GHG emissions’ is the condition in which metric-weighted anthropogenic GHG emissions are balanced by metric-weighted anthropogenic GHG removals over a specified period. The quantification of net zero GHG emissions thus depends on the GHG emissions metric chosen to compare emissions of different gases, as well as the time horizon chosen for that metric. (For a broader discussion of metrics, see Box 1.3 and Section 7.6, and WGIII Cross-Chapter Box 2.)

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A global net zero level of CO2, or GHG, emissions will be achieved when the sum of anthropogenic emissions and removals across all countries, sectors, sources and sinks reaches zero. Achieving net zero CO2 or GHG emissions globally, at a given time, does not imply that individual entities (i.e., countries, sectors) have to reach net zero emissions at that same point in time, or even at all (see WGIII, TS Box 4 and Chapter 3).

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Net zero CO2 and net zero GHG emissions differ in their implications for the subsequent evolution of global surface temperature. Net zero CO2 emissions result in approximately stable CO2 -induced warming, but overall warming will depend on any further warming contribution of non-CO2 GHGs. The effect of net zero GHG emissions on global surface temperature depends on the GHG emissions metric chosen to aggregate emissions and removals of different gases. For GWP100 (the metric in which Parties to the Paris Agreement have decided to report their aggregated emissions and removals), net zero GHG emissions would generally imply a peak in global surface temperature, followed by a gradual decline (Section 7.6.2; see also Section 4.7.1 regarding the zero emissions commitment). However, other anthropogenic factors, such as aerosol emissions or land use-induced changes in albedo, may still affect the climate.

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For virtually all scenarios assessed by the IPCC, CDR is necessary to reach both global net zero CO2 and net zero GHG emissions, to compensate for residual anthropogenic emissions. This is in part because for some sources of CO2 and non-CO2 emissions, abatement options to eliminate them have not yet been identified. For a given scenario, the choice of GHG metric determines how much net CDR is necessary to compensate for residual non-CO2 emissions, in order to reach net zero GHG emissions (Section 7.6.2).

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If CDR is further used to go beyond net zero, to a situation with net-negative CO2 emissions (i.e., where anthropogenic removals exceed anthropogenic emissions), anthropogenic CO2 -induced warming will decline. A further increase of CDR, until a situation with net zero or even net-negative GHG emissions is reached, would increase the pace at which historical human-induced warming is reversed after its peak (SR1.5, IPCC, 2018). Net negative anthropogenic GHG emissions may become necessary to stabilize the global surface temperature in the long term, should climate feedbacks further affect natural GHG sinks and sources (Chapter 5).

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CDR can be achieved through a number of measures (Section 5.6; SRCCL, IPCC, 2019a). These include additional afforestation, reforestation, soil carbon management, biochar, direct air capture and carbon capture and storage (DACCS), and bioenergy with carbon capture and storage (BECCS; de Coninck et al., 2018, SR1.5 Ch4; Minx et al., 2018; see also WGIII Chapters 7 and 12). Differences between land use, land-use change and forestry (LULUCF) accounting rules, and scientific bookkeeping approaches for CO2 emissions and removals from the terrestrial biosphere, can result in significant differences between the amount of CDR that is reported in different studies (Grassi et al., 2017). Different measures to achieve CDR come with different risks, negative side effects and potential co-benefits – also in conjunction with sustainable development goals – that can inform choices around their implementation (Section 5.6; Fuss et al., 2018; Roe et al., 2019). Technologies to achieve direct large-scale anthropogenic removals of non-CO2 GHGs are speculative at present (Yoon et al. , 2009; Ming et al. , 2016; Kroeger et al. , 2017; Jackson et al., 2019).

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Spatial and temporal gaps in both historical and current observing networks, and the limited extent of paleoclimatic archives, have always posed a challenge for IPCC assessments. A relative paucity of long-term observations is particularly evident in Antarctica and in the depths of the ocean. Knowledge of previous cryospheric and oceanic processes is therefore incomplete. Sparse instrumental temperature observations prior to the industrial revolution make it difficult to uniquely characterize a ‘pre-industrial’ baseline, although this Report extends the assessment of anthropogenic temperature change further back in time than previous assessment cycles (Chapter 7 and Cross-Chapter Box 1.2).

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Undorf, S. et al., 2018: Detectable Impact of Local and Remote Anthropogenic Aerosols on the 20th century Changes of West African and South Asian Monsoon Precipitation. Journal of Geophysical Research: Atmospheres, 123(10), 4871–4889, doi: 10.1029/2017jd027711.

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Zeebe, R.E., A. Ridgwell, and J.C. Zachos, 2016: Anthropogenic carbon release rate unprecedented during the past 66 million years. Nature Geoscience, 9(4), 325–329, doi: 10.1038/ngeo2681.

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Short-lived climate forcers (SLCFs) are a set of chemically and physically reactive compounds with atmospheric lifetimes typically shorter than two decades but differing in terms of physiochemical properties and environmental effects. SLCFs can be classified as direct or indirect, with direct SLCFs exerting climate effects through their radiative forcing and indirect SLCFs being precursors of direct climate forcers. Direct SLCFs include methane (CH4), ozone (O3), short-lived halogenated compounds, such as hydrofluorocarbons (HFCs), hydrochlorofluorocarbons (HCFCs), and aerosols. Indirect SLCFs include nitrogen oxides (NOx), carbon monoxide (CO), non-methane volatile organic compounds (NMVOCs), sulphur dioxide (SO2), and ammonia (NH3). Aerosols consist of sulphate (SO24), nitrate (NO3), ammonium (NH+4), carbonaceous aerosols (e.g., black carbon (BC), organic aerosols (OA)), mineral dust, and sea spray (see Table 6.1) and can be present as internal or external mixtures and at sizes from nano-meters to tens of micro-meters. SLCFs can be emitted directly from natural systems and anthropogenic sources (primary) or can be formed by reactions in the atmosphere (secondary; Figure 6.1).

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As depicted in Figure 6.1, emissions of SLCFs are governed by anthropogenic activities and sources from natural systems (see Section 6.2 for details). Atmospheric chemistry in this context is both a source and a sink of SLCFs. For instance, ozone and secondary aerosols are exclusively formed through atmospheric mechanisms (Sections 6.3.2 and 6.3.5 respectively). The hydroxyl (OH) radical, the most important oxidizing agent in the troposphere, acts as a sink for SLCFs by reacting with them and thereby influencing their lifetime (Section 6.3.6). Through SLCF radiative forcing and feedbacks (Section 6.4), key climate parameters, such as temperature, hydrological cycle and weather patterns are perturbed. Climate change also influences air quality (Section 6.5). As depicted in Figure 6.1, SLCFs affect both climate and air quality, hence SLCF mitigation has linkages to both issues (Section 6.6). Socio-economic narratives including air-quality policies determine future projections of SLCFs in the five core Shared Socio-economic Pathways (SSPs): SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 (described in Chapter 1), and in addition, a subset of SSP3 scenarios make it possible to isolate the effect of various SLCF mitigation trajectories on climate and air quality (Section 6.7).

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SLCF emissions originate from a variety of sources driven by anthropogenic activities and natural processes. The natural sources include vegetation, soil, fire, lightning, volcanoes and oceans. Changes in SLCF emissions from natural systems occur either due to human activities, such as land-use change, or due to global changes. Their sensitivity to climate change thus induces climate feedbacks (see Section 6.4.5 for a quantification of these feedbacks). This section reviews the current understanding of historical emissions for anthropogenic, natural, and open biomass burning sources. A detailed discussion of methane sources, sinks, trends are provided in Chapter 5, Section 5.2.2.

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Estimates of global anthropogenic (human-caused) SLCF emissions and their historical evolution that were used in AR5 (CMIP5; Lamarque et al., 2010) have been revised for use in CMIP6 (Hoesly et al., 2018). The update considered new data and assessment of the impact of environmental policies, primarily regarding air pollution control (R. Wang et al. , 2014; S.X. Wang et al. , 2014; Montzka et al. , 2015; Crippa et al. , 2016; Turnock et al. , 2016; Klimont et al. , 2017a; Zanatta et al. , 2017; Prinn et al. , 2018). Additionally, Hoesly et al. (2018) have extended estimates of anthropogenic emissions back to 1750 and developed an updated and new set of spatial proxies allowing for more differentiated (source sector-wise) gridding of emissions (Feng et al., 2020). The CMIP6 emissions inventory has been developed with the Community Emissions Data System (CEDS) that improves upon existing inventories with a more consistent and reproducible methodology, similar to approaches used in, for example, the EDGAR database (Crippa et al., 2016) and the GAINS model (Amann et al., 2011; Klimont et al., 2017a; Höglund-Isaksson et al., 2020) where emissions of all compounds are consistently estimated using the same emissions drivers and propagating individual components (activity data and emissions factors) separately to capture fuel and technology trends affecting emissions trajectories over time. This contrasts with the approach used to establish historical emissions for CMIP5 where different datasets available at the time were combined. The CMIP6 exercise is based on the first release of the CEDS emissions dataset (version 2017-05-18, sometimes referred to hereafter as CMIP6 emissions) whose main features regarding SLCFs are described hereafter. The CEDS has been and will be regularly updated and extended; the recent update of the CEDS (Hoesly et al., 2019) and consequences for this Assessment is discussed when necessary. Some details on how SLCF emissions have been represented in scenarios used by IPCC assessments can be found in Chapter 1 (Section 1.6.1 and Cross-Chapter Box 1.4 and in Section 6.7.1.1).

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For most of the SLCF species, the global and regional anthropogenic emissions trends developed for CMIP6 for the period 1850–2000 are not substantially different from those used in CMIP5 (Figures 6.18 and 6.19) despite the different method used to derive them. Hoesly et al. (2018, CEDS) developed independent time series capturing trends in fuel use, technology and level of control, whereas CMIP5 combined different emissions datasets. However, for the period after 1990, the CMIP6 dataset shows for all species, except for SO2, CO, and (since 2011) for NOx, a different trend than CMIP5 (i.e., continued strong growth of emissions driven primarily by developments in Asia (Figure 6.19)). The unprecedented growth of emissions from Eastern and Southern Asia since 2000 changed the global landscape of emissions, making Asia the dominant SLCF source region (Figures 6.3 and 6.19). The Representative Concentration Pathways (RCP) scenarios used in AR5 started from the year 2000 (van Vuuren et al., 2011) and did not capture the SLCF emissions which actually occurred until 2015. The CEDS inventory (Hoesly et al., 2018) includes improved representation of these trends and the estimate for 2014. These findings have been largely supported by several independent emissions inventory studies and remote-sensing data analysis. However, for the last decade the decline of Asian emissions of SO2 and NOx appears underestimated while growth of BC and OC emissions in Asia and Africa seems overestimated in CMIP6, compared to most recent regional evaluations (Klimont et al., 2017a; Zheng et al., 2018b; Elguindi et al., 2020; Kanaya et al., 2020; McDuffie et al., 2020), which are largely considered in the updated release of the CEDS (Hoesly et al., 2019). Consequently, global CMIP6 anthropogenic emissions for 2014 are likely overestimated by about 10% for SO2 and NOx and by about 15% for BC and OC.

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For SO2, independent emissions inventories and observational evidence show that on a global scale strong growth of Asian emissions has been countered by reduction in North America and Europe (Reis et al. , 2012; Amann et al. , 2013; Crippa et al. , 2016; Aas et al. , 2019). However, Chinese emissions declined by nearly 70% between about 2006 and 2017(high confidence) (Silver et al. , 2018; Zheng et al. , 2018b; Mortier et al. , 2020; Tong et al. , 2020). The estimated reduction in China contrasts with continuing strong growth of SO2 emissions in Southern Asia (Figure 6.19). In 2014, over 80% of anthropogenic SO2 emissions originated from power plants and industry, with Asian sources contributing more than 50% of the total (Figure 6.3).

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Global emissions of NOx have been growing in spite of the successful reduction of emissions in North America, Europe, Japan and Korea (Crippa et al., 2016; Turnock et al., 2016; Miyazaki et al., 2017; Jiang et al., 2018), partly driven by continuous efforts to strengthen the emissions standards for road vehicles in most countries (Figures 6.18 and 6.19). In many regions, an increase in vehicle fleet as well as non-compliance with emissions standards (Anenberg et al. , 2017, 2019; Jonson et al. , 2017; Jiang et al. , 2018), growing aviation (Grewe et al., 2019; Lee et al., 2021) and demand for energy, and consequently a large number of new fossil fuel power plants, have more than compensated for these reductions. Since about 2011, global NOx emissions appear to have stabilized or slightly declined (medium confidence) but the global rate of decline has been underestimated in the CEDS, as recent data suggest that emissions reductions in China were larger than included in the CEDS (Figure 6.19 and Hoesly et al., 2018). Recent bottom-up emissions estimates (Zheng et al., 2018b) largely confirm what has been shown in satellite data (F. Liu et al., 2016; Miyazaki et al., 2017; Silver et al., 2018): a strong decline of NO2 column over eastern China (high confidence) (Section 6.3.3.1). At a global level, the estimated CEDS CO emissions trends are comparable to NOx, which has been confirmed by several inverse modelling studies (Section 6.3.3.2). The transport sector (including international shipping and aviation) was the largest anthropogenic source of NOx (about 50% of the total) and also contributed over 25% of CO emissions in 2014; Asia represented 50% and North America and Europe about 20% of global total NOx and CO emissions (Figure 6.3).

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Oil production-distribution and transport sectors have dominated anthropogenic NMVOC emissions for most of the 20th century (Hoesly et al., 2018) and still represent a large share (Figure 6.3). Efforts to control transport emissions (i.e., increasing stringency of vehicle emissions limits) were largely offset by the fast growth of emissions from chemical industries and solvent use, as well as from fossil fuel production and distribution, resulting in continued growth of global anthropogenic NMVOC emissions since 1900 (high confidence) (Figure 6.18). Since AR5, there is high confidence that motor vehicle NMVOC emissions have sharply declined in North America and Europe in the last decades (Rossabi and Helmig, 2018), for example, by about an order of magnitude in major US cities since 1990 (Bishop and Haugen, 2018; McDonald et al., 2018). Increasing (since 2008) oil- and gas-extraction activities in North America lead to a strong growth of NMVOC emissions (high confidence) as shown by analysis of ethane column data (Franco et al., 2016), but absolute emission amounts remain uncertain (Pétron et al., 2014; Tzompa-Sosa et al., 2019). In Eastern Asia, there is medium confidence in a decreasing trend of motor vehicle emissions, suggested by ambient measurements in Beijing since 2002 (Wang et al., 2015) and by bottom-up estimates (Zheng et al., 2018b), and a decrease in residential heating emissions due to declining coal and biofuel use since 2005 (Zheng et al., 2018b; M Li et al., 2019). However, total anthropogenic NMVOC emissions have increased steadily in China since the mid-20th century, largely due to the growing importance of the solvent-use and industrial sectors (medium evidence, high agreement) (Sun et al. , 2018; Zheng et al. , 2018b; M. Li et al. , 2019). Resulting changes in the NMVOC speciated emissions might be underestimated in the current regional and global inventories. For example, in the USA, a recent study suggested an emergent shift in urban NMVOC sources from transportation to chemical products (i.e., household chemicals, personal care products, solvents, etc.), which is not in accordance with emissions inventories currently used (McDonald et al., 2018). In many European regions and cities, wood burning has been increasingly used for residential heating, partly for economic reasons and because it is considered CO2-neutral (Athanasopoulou et al., 2017); in situ measurements in several cities, including Paris, suggest that wood burning explains up to half of the NMVOC emissions during winter (Kaltsonoudis et al., 2016; Languille et al., 2020). Due to the vast heterogeneity of sources and components of NMVOCs, uncertainty in regional emissions and trends is higher than for most other components.

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To summarize, there are significant differences in spatial and temporal patterns of SLCF emissions across global regions (Figure 6.18). Until the 1950s, the majority of SLCF emissions were associated with fossil fuel use (SO2, NOx, NMVOCs, CO) and about half of BC and OC originated from North America and Europe (Lamarque et al., 2010; Hoesly et al., 2018). Since the 1990s a large redistribution of emissions was associated with strong economic growth in Asia and declining emissions in North America and Europe due to air-quality legislation and the declining capacity of energy-intensive industry; currently more than 50% of anthropogenic emissions of each SLCF species (including methane and NH3) originates from Asia (Figure 6.3; Amann et al. , 2013; Bond et al. , 2013; Fiore et al. , 2015; Crippa et al. , 2016, 2018; Klimont et al. , 2017a; Hoesly et al. , 2018). The dominance of Asia for SLCF emissions is corroborated by growing remote-sensing capacity that has been providing an independent evaluation of estimated pollution trends in the last decade (Duncan et al. , 2013; Lamsal et al. , 2015; Luo et al. , 2015; Fioletov et al. , 2016; Geddes et al. , 2016; Irie et al. , 2016; Krotkov et al. , 2016; Wen et al. , 2018).

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Global BVOC emissions are highly sensitive to environmental changes including changes in climate, atmospheric CO2, and vegetation composition and cover changes in natural and managed lands. Recent global modelling studies agree that global isoprene emissions have declined since the pre-industrial period, driven predominantly by anthropogenic land-use and land-cover change (LULCC) with results converging on a 10–25% loss of isoprene emissions between 1850 and the present day (Lathière et al., 2010; Unger, 2013, 2014; Acosta Navarro et al., 2014; Heald and Geddes, 2016; Hantson et al., 2017; Hollaway et al., 2017; Scott et al., 2017). The historical evolution of monoterpene and sesquiterpene emissions is less well studied and there is no robust consensus on even the sign of the change (Acosta Navarro et al., 2014; Hantson et al., 2017). Future global isoprene and monoterpene emissions depend strongly on the climate and land-use scenarios considered (Hantson et al., 2017; Szogs et al., 2017). BVOC emissions will be sensitive to future land-based climate change mitigation strategies including afforestation and bioenergy, with impacts of bioenergy depending on the choice of crops (Szogs et al., 2017).

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The emission of dust particles into the atmosphere results from a natural process, namely saltation bombardment of the soil by large wind-blown particles, such as sand grains, and from disintegration of saltating particle clusters (Kok et al., 2012). The occurrence and intensity of dust emissions are controlled by soil properties, vegetation and near-surface wind, making dust emissions sensitive to climate change and LULCC (Jia et al., 2019). In addition, dust can be directly emitted through human activities, such as agriculture, off-road vehicles, building construction and mining, and indirectly emitted through hydrological changes due to human actions such as water diversion for irrigation (e.g., Ginoux et al., 2012). Estimates of the anthropogenic fraction of global dust vary from less than 10% to over 60% suggesting that the human contribution to the global dust budget is quite uncertain (Ginoux et al., 2012; Stanelle et al., 2014; Xi and Sokolik, 2016). Reconstruction of global dust (deposition) from paleo records indicate factor of two to four changes between the different climate regimes in the glacial and interglacial periods (Section 2.2.6).

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Climate warming, especially through change in temperature and precipitation, will generally increase the risk of fire (Jia et al., 2019, see also Chapter 12) and can also affect the fire injection and plume height (Veira et al., 2016), but occurrence of fires and their emissions in the future strongly depends on anthropogenic factors, such as population density, land use and fire management (Veira et al., 2016). Consequently, future emissions vary widely with increases and decreases amongst the SSP scenarios due to different land-use change scenarios.

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model parametrizations (e.g., chemical mechanisms, photolysis schemes, parametrizations for mixing and convective transport, and deposition), model input parameters (e.g., reaction rate constants, emissions) and an incomplete understanding of the physical and chemical processes that determine SLCF distributions (Brasseur and Jacob, 2017; Young et al., 2018). CCMs can therefore not capture every aspect of atmospheric chemical composition, but are expected to represent, as faithfully as possible, the sensitivity of chemical compounds to their drivers (e.g., anthropogenic emissions). Models are evaluated in multiple ways to identify their strengths and weaknesses in explaining the evolution of SLCF abundances. For example, CCM simulations are performed in the nudged or offline meteorology mode, that is, driven by observed or reanalysed meteorology rather than in the free-running mode, for consistent comparison of modelled chemical composition with observations for a specific time period (Dameris and Jöckel, 2013). However, caution is exercised as nudging can alter the model climate resulting in unintentional impacts on the simulated atmospheric physics and/or chemistry (Orbe et al., 2018; Chrysanthou et al., 2019). Chemical mechanisms implemented in CCMs are evaluated and intercompared to assess their skill in capturing relevant chemistry features (e.g., Brown-Steiner et al., 2018). The multi-model ensemble approach, employed for evaluating climate models, has been particularly useful for characterizing errors in CCM simulations of SLCFs related to structural uncertainty and internal variability (Naik et al. , 2013; Shindell et al. , 2013; Young et al. , 2013; Turnock et al. , 2020). However, as discussed in Box 4.1, this approach is unable to capture the full uncertainty range.

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The global mean surface mixing ratio of methane has increased by 156% since 1750 (Section 2.2.3.4 and Annex III). Since AR5, the methane mixing ratio has increased by about 3.5% from 1803 ± 2 ppb in 2011 to 1866 ± 3 ppb in 2019 (Section 2.2.3.3.2) largely driven by anthropogenic activities as assessed in Chapter 5 (Section 5.2.2 and Cross-Chapter Box 5.2).

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Because of the heterogeneous distribution of ozone, limited observations or proxies do not provide accurate information about the global pre-industrial abundance, posing a challenge to the estimation of the historical evolution of tropospheric ozone. Therefore, global CCMs complemented by observations are relied upon for estimating the long-term changes in tropospheric ozone. The AR5 concluded that anthropogenic changes in ozone precursor emissions are unequivocally responsible for the increase in tropospheric ozone between 1850 and the present (Myhre et al., 2013). Based on limited isotopic evidence, Chapter 2 assesses that the global tropospheric ozone increased by less than 40% between 1850 and 2005 (low confidence) (Section 2.2.5.3). The CMIP6 models are in line with this increase of tropospheric ozone with an ensemble-mean value of 109 ± 25 Tg (model range) from 1850–1859 to 2005–2014 (Figure 6.4). This increase is higher than the AR5 value of 100 ± 25 Tg from 1850–2010 due to higher ozone precursor emissions in CMIP6. However, the AR5 and CMIP6 values are close when considering the reported uncertainties. The uncertainties are equivalent in CMIP6 and AR5 despite enhanced inclusion of coupled processes in the CMIP6 ESMs (e.g., biogenic NMVOC emissions or interactive stratospheric ozone chemistry).

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Evidence from successive multi-model intercomparisons and the limited isotopic evidence agree on the magnitude of the increase of the tropospheric ozone burden from 1850 to the present day in response to anthropogenic changes in ozone precursor emissions corroborating AR5 findings. This increase is assessed to be 109 ± 25 Tg (medium confidence). The CMIP6 model ensemble shows a constant global increase since the mid-20th century whose rate is consistent with that derived from observations since the mid-1990s.

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Observational constraints derived from the isotopic composition of atmospheric nitrate inferred from ice cores provide evidence of increasing anthropogenic NOx sources since pre-industrial times (Hastings et al., 2009; Geng et al., 2014). Global NOx emissions trends in bottom-up inventories (Section 6.2.1) as well as model simulations of nitrogen deposition (Lamarque et al., 2013a) are in qualitative agreement with these observational constraints. CMIP6 ESMs exhibit stable NOx4 burden over the first half of the 20th century and then a sharp increase driven by a factor of three increase in emissions, however, the magnitude of this increase remains uncertain due to poor observational constraints on pre-industrial concentrations of NOx (Griffiths et al., 2021).

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The AR5 reported a global CO decline of about 1% yr–1based on satellite data from 2002–2010, but biases in instruments rendered low confidence in this trend. The AR5 also indicated a small CO decrease from in situ networks but did not provide quantitative estimates. New analysis of CO trends performed since AR5 and based on different observational platforms and assimilation products show a decline globally and over most regions during the last one to two decades with varying amplitudes partly depending on the period of analysis (Table 6.4). Inversion-based analysis attributes the global CO decline during the past two decades to decreases in anthropogenic and biomass-burning CO emissions despite probable increase in atmospheric CO chemical production (Gaubert et al. , 2017; Jiang et al. , 2017; Zheng et al. , 2019). Furthermore, Buchholz et al. (2021) report a slowdown in global CO decline in 2010–2018 compared to 2002–2010, although the magnitude and sign of this change in the trend varies regionally. Global models prescribed with emissions inventories developed prior to the CMIP6 inventory capture the declining observed CO trends over North America and Europe but not over Eastern Asia (Strode et al., 2016). CMIP6 models driven by CMIP6 emissions simulate a negative trend in global CO burden over the 1990–2020 period (Griffiths et al., 2021), however the simulated trends have not yet been evaluated against observations.

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In summary, our understanding of present-day global CO distribution has increased since AR5 with newer and improved observations and reanalysis. There is high confidence that global CO burden is declining since 2000. Evidence from observational CO reanalysis suggests this decline is driven by reductions in anthropogenic CO emissions, however this is yet to be corroborated by global ESM studies with the most recent emissions inventories.

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NMVOCs encompass thousands of compounds with lifetimes from hours to days to months, and abundances and chemical composition highly variable with respect to space and time. Although the biogenic source (Section 6.2.2) dominates the global NMVOC budget, anthropogenic activities are the main driver of long-term trends in the abundance of many compounds.

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Information on the global distribution of individual NMVOCs is scarce, except for the less reactive compounds having lifetimes of several days to months. Based on measurements from polar firn air samples and ground-based networks, AR5 reported that the abundances of the predominantly anthropogenic light alkanes (C2-C5) increased until 1980 and declined afterwards. The decline was attributed to air-quality emissions controls and to fugitive emissions decreases following the collapse of the Soviet Union (Simpson et al., 2012). Since AR5, scarce ground-based measurements have shown that the decline in C2-C3 alkanes ended around 2008 and their abundances are since growing again, which is primarily attributed to increasing North American emissions (Section 6.2.1). Furthermore, since AR5 the evolution of ethane levels during the past millennium was made accessible by analysis of ice-core samples (Nicewonger et al., 2016). The large observed interpolar ratio of ethane in pre-industrial times (3.9) corroborates a large geologic source of ethane previously put forward by (Etiope and Ciccioli, 2009), and narrows down its likely global magnitude (Nicewonger et al., 2018) (low to medium confidence). The incorporation of geologic emissions in CCMs is not yet systematic though a one-model study has shown improved agreement of the results with observations (Dalsøren et al., 2018).

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Formaldehyde (HCHO) is a short-lived, high-yield product of NMVOC oxidation, and formaldehyde column data from satellite instruments can therefore inform on trends in anthropogenic NMVOC abundances over very industrialized regions. The AR5 reported significant positive trends in formaldehyde between 1997 and 2009 over northeastern China (4% yr–1) and negative trends over northeastern US cities. Since AR5, there is robust evidence and high agreement of an upward trend of HCHO over eastern China, though large regional disparities exist in the trends (De Smedt et al., 2015; Shen et al., 2019) with a possible negligible or decreasing trend over Beijing and the Pearl River Delta. In other world regions, in particular North America, there is limited to medium evidence for significant changes in the HCHO columns, except in regions where the trend is particularly strong (e.g., the Houston area: –2.2% yr–1 over 2005–2014) and the Alberta oil sands (+3.8% yr–1;Zhu et al., 2017). Over the northeastern USA, even the sign of the trend differs between studies (De Smedt et al. , 2015; Zhu et al. , 2017) for reasons that are unclear.

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In summary, after a decline between 1980 and 2008, abundances of light NMVOCs have increased again over the Northern Hemisphere due to the extraction of oil and gas in North America (high confidence). Trends in satellite HCHO observations, used as a proxy of anthropogenic NMVOC over industrialized areas, show a significant positive trend over eastern China (high confidence) but also indicate large regional disparities in the magnitude of the trends over China and even in their signs over North America.

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The AR5 did not assess trends in SO2 concentrations. Trends in SO2 abundances are consistent with the overall anthropogenic emissions changes as presented in Section 6.2 and Figure 6.18. Long-term surface-based in situ observations in North America and Europe show reductions of more than 80% since the measurements began around 1980 (Table 6.5). Europe had the largest reductions in the first part of the period while the highest reduction came later in North America. Observed trends are qualitatively reproduced by global and regional models over North America and Europe during the period 1990–2015 for which emissions changes are well quantified (Table 6.5; Aas et al., 2019).

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Of the atmospheric VSLSs, brominated and iodinated species are predominantly of oceanic origin, while chlorinated species have significant additional anthropogenic sources (Carpenter et al., 2014; Hossaini et al., 2015). Global mean chlorine from the VSLSs has increased in the troposphere from about 91 ppt in 2012 to about 110 ppt in 2016 (Engel et al., 2018). This increase is mostly due to dichloromethane (CH2 cl2), a species that has predominantly anthropogenic sources reflected by three-times higher concentrations in the Northern Hemisphere than in the Southern Hemisphere (Hossaini et al., 2017). The upward dichloromethane trend is corroborated by upper-tropospheric aircraft data over the period 1998–2014 (Elvidge et al., 2015b; Oram et al., 2017). The observations from the surface networks show that the abundance of dichloromethane continued to increase until 2019 (Annex III), although the accuracy of global abundance of VSLSs is limited by the scarce coverage by networks. No long-term changes of the bromine-containing VSLSs have been observed (Engel et al., 2018).

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All CMIP6 models simulate a positive trend in global mean AOD from 1850, with a strong increase after the 1950s coinciding with the massive increase in anthropogenic SO2 emissions (Figure 6.8). Global mean AOD increases have slowed since 1980, or even reversed in some models, as a result of a compensation between SO2 emissions decreases over the USA and Europe in response to air-quality controls since the mid-1980s, and increases over Asia. From about 2000, global mean AOD stabilized in the models, driven by soaring emissions in Southern Asia and declining emissions in Eastern Asia (Section 6.2.1). Trends after around 2010 are difficult to assess from CMIP6 models because the historical simulations end at 2014. Nevertheless, the strong decline in anthropogenic SO2 emissions over Eastern Asia since 2011 is underestimated in the CMIP6 emissions database (Hoesly et al., 2018), indicating that the observed AOD change over Eastern Asia may not be captured accurately by CMIP6 models (Wang et al., 2021). While all CMIP6 models simulate the increase of AOD between 1850 and 2014 there is strong inter-model diversity in the simulated AOD change since 1850 ranging from 0.01 (15%) to 0.08 (53%) in 2014. Some models therefore lie outside the 68% confidence interval of 0.02 (15%) to 0.04 (or 30%) for global AOD change in 2005–2015 compared to 1850, estimated by Bellouin et al. (2020) based on observational and model (excluding CMIP6) lines of evidence. In addition to the horizontal distribution of aerosols documented by AOD, their number size distribution, vertical distribution, optical properties, hygroscopicity, ability to act as CCN, chemical composition, mixing state and morphology are key elements to assess their climate effect (Section 6.4).

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Sulphate aerosols (or sulphate-containing aerosols) are emitted directly or formed in the atmosphere by gas- and aqueous-phase oxidation of precursor sulphur gases, including SO2, DMS and carbonyl sulphide (OCS), emitted from anthropogenic and natural sources (Section 6.2). Sulphate aerosols influence climate forcing directly by either scattering solar radiation or absorbing longwave radiation, and indirectly by influencing cloud micro- and macrophysical properties and precipitation (Boucher et al., 2013; Myhre et al., 2013). Additionally, sulphate aerosols and sulphate deposition have a large impact on air quality and ecosystems (Reis et al., 2012). The majority of sulphate particles are formed in the troposphere, however, SO2 and other longer-lived natural precursors, such as OCS, transported into the stratosphere, contribute to the background stratospheric aerosol layer (Kremser et al., 2016). SO2 emissions from volcanic eruptions are a significant source of stratospheric sulphate loading (see Chapter 2 for reconstruction of stratospheric aerosol optical depth and Chapter 7 for radiative forcing of volcanic aerosols). Furthermore, studies suggest sulphate contributions from anthropogenic SO2 emissions transported into the stratosphere could have a consequent impact on radiative forcing (Myhre et al., 2004; Yu et al., 2016). However, there is significant uncertainty in the relative importance of this stratospheric sulphate source (Kremser et al., 2016).

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Global and regional models qualitatively reproduce observed trends over North America and Europe for the period 1990–2015 for which emissions changes are generally well quantified (Aas et al., 2019; Mortier et al., 2020), building confidence in the relationship between emissions, concentration, deposition and radiative forcing derived from these models. However, the models seem to systematically underestimate sulphate (Bian et al., 2017; Lund et al., 2018a) and AOD (Lund et al., 2018a; Gliß et al., 2021), and there are quite large differences in the models’ distribution of the concentration fields of sulphate driven by differences in the representation of photochemical production and sinks of aerosols. One global model study also highlighted biases in simulated sulphate trends over the 2001–2015 period over eastern China due to uncertainties in the CEDS anthropogenic SO2 emissions trends (Paulot et al. , 2018a).

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Since AR5, there is much closer agreement in the estimates of interannual variations in global mean OH derived from atmospheric inversions, empirical reconstruction, and global CCMs and ESMs, with an estimate of 2–3% over the 1980–2015 period (Table 6.7). While the different methodologies agree on the occurrence of small interannual variations, there is much debate over the longer-term global OH trend. Two studies using multi-box model inversions of MCF and methane observations suggest large positive and negative trends since the 1990s in global mean OH (Rigby et al., 2017;Turner et al., 2017), however, both find that observational constraints are weak, such that a wide range of multi-annual OH variations are possible. Indeed, Naus et al. (2019) find an overall positive global OH trend over the past two decades (Table 6.7) after accounting for uncertainties and biases in atmospheric MCF and methane inversions, confirming the weakness in observational constraints for deriving OH trends. Global ESMs, CCMs and CTMs exhibit increasing global OH after 1980 contrary to the lack of trend derived from some atmospheric inversions and empirical reconstructions (Table 6.7). In particular, a three-member ensemble of ESMs participating in the AerChemMIP/CMIP6 agrees that global OH has increased since 1980 by around 9% (Figure 6.9) with an associated reduction in methane lifetime (Stevenson et al., 2020). This positive OH trend is in agreement with the OH increase of about 7% derived by assimilating global-scale satellite observations of CO over the 2002–2013 period (with CO declining trends) into a CCM (Section 6.3.4; Gaubert et al., 2017). Multi-model sensitivity analysis suggests that increasing OH since 1980 is predominantly driven by changes in anthropogenic SLCF emissions with the complementary influence of increasing NOx and decreasing CO emissions (Stevenson et al., 2020).

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In summary, global mean tropospheric OH does not show a significant trend from 1850 up to around 1980 (low confidence). There is conflicting information from global models constrained by emissions versus observationally constrained inversion methods over the 1980–2014 period. A positive trend since 1980 (about 9% increase over 1980–2014) is a robust feature among ESMs and CCMs and there is medium confidence that this trend is mainly driven by increases in global anthropogenic NOx emissions and decreases in CO emissions. There is limited evidence and medium agreement for positive trends or absence of trends inferred from observation-constrained methods. Overall, there is medium confidence that global mean OH has remained stable or exhibited a positive trend since the 1980s.

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In AR5, the confidence in the spatial patterns of aerosol and ozone forcing was lower than that for the global mean because of the large spread in the regional distribution simulated by global models, and was assessed as medium. The AR5 assessment was based on aerosol and ozone RFs, and aerosol ERFs (with fixed SSTs) from ACCMIP and a small sample of CMIP5 models (Myhre et al., 2013; Shindell et al., 2013). For this assessement, the spatial distribution of aerosol ERF due to human-induced changes in aerosol concentrations over 1850–2014 is quantified based on results from a seven-member ensemble of CMIP6 ESMs including interactive gas and aerosol chemistry analysed in AerChemMIP. There is insufficient information to estimate the spatial patterns of ozone ERF from CMIP6, however, the spatial patterns in SLCF ERF are dominated by that from aerosol ERF over most regions (e.g., Shindell et al., 2015). The aerosol ERF includes contributions from both direct aerosol–radiation (ERFari) and indirect aerosol–cloud interactions (ERFaci; Section 7.3.3), and is computed as the difference between radiative fluxes from simulations with time-evolving aerosol and their precuror emissions, and identical simulations but with these emissions held at their 1850 levels (Collins et al., 2017). Both simulations are driven by time-evolving sea surface temperatures (SSTs) and sea ice from the respective coupled model historical simulation, and therefore, differ from ERFs computed using fixed pre-industrial SST and sea ice fields (Section 7.3.1), but the effect of this difference is generally small (Forster et al., 2016). A correction for land surface temperature change (Section 7.3.1) is not available from these data to explicitly quantify the contribution from adjustments. The ESMs included here used the CMIP6 anthropogenic and biomass-burning emissions for ozone and aerosol precursors but varied in their representation of the natural emissions, chemistry and climate characteristics contributing to spread in the simulated concentrations (Section 6.3) and resulting forcings, partly reflecting uncertainties in the successive processes (Thornhill et al., 2021b).

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The geographical distribution of the ensemble-mean aerosol ERF over the 1850–2014 period is highly heterogeneous (Figure 6.10a) in agreement with AR5. Negative ERF is greatest over and downwind of most industrialized regions in the Northern Hemisphere and to some extent over tropical biomass-burning regions, with robust signals. The largest negative forcing occurs over Eastern Asia and Southern Asia, followed by Europe and North America, reflecting the changes in anthropogenic aerosol emissions in recent decades (Section 6.2). Positive ERF over high albedo areas, including cryosphere, deserts and clouds, also found in AR5 and attributed to absorbing aerosols, are not robust across the small CMIP6 ensemble applied here. Regionally aggregated shortwave (SW) and longwave (LW) components of the aerosol ERF exhibit similar large variability across regions (Figure 6.10b). The SW flux changes come from aerosol–radiation and aerosol–cloud interactions while the small positive LW flux changes come from aerosol–cloud interactions (related to liquid-water path changes (Section 7.3.2.2). These spatial patterns in aerosol ERF are similar to the patterns reported in AR5.

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The ERFs attributable to emissions versus concentrations for several SLCFs including ozone and methane are different. A concentration change, used to assess the abundance-based ERF, results fromThe changes in emissions of multiple species and subsequent chemical reactions. The corollary is that the perturbation of a single emitted compound can induce subsequent chemical reactions and affect the concentrations of several climate forcers (chemical adjustments); this is what is accounted for in emissions-based ERF. Due to non-linear chemistry (Section 6.3) and non-linear aerosol–cloud interactions (Section 7.3.3.2), the ERF attributed to the individual species cannot be precisely defined and can only be estimated through model simulations. For example, the ERF attributed to methane emissions, which includes indirect effects through ozone formation and oxidation capacity with feedbacks on the methane lifetime, depend non-linearly on the concentrations of NOx, CO and NMVOCs. This means that the results from the model simulations depend to some extent on the chosen methodology. In AR5 (based on Shindell et al., 2009; Stevenson et al., 2013) the attribution was done by removing the anthropogenic emissions of individual species one by one from a control simulation for present-day conditions. Further, only the radiative forcings, and not the ERF (mainly including the effect of aerosol–cloud interactions) were attributed to the emitted compounds.

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Since AR5, the emissions estimates have been revised and extended for CMIP6 (Hoesly et al., 2018), the models have been further developed, the period has been extended (1750–2019, versus 1750–2011 in AR5) and the experimental setup for the model simulations has changed (Collins et al., 2017), making a direct comparison of results difficult. Figure 6.12a shows the global and annual mean ERF attributed to emitted compounds over the period 1750–2019 based on AerChemMIP simulations (Thornhill et al., 2021b) where anthropogenic emissions or concentrations of individual species were perturbed from 1850 to 2014 levels (methodology described in Supplementary Material 6.SM.1).

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NOx causes a positive ERF through enhanced tropospheric ozone production and a negative ERF through enhanced OH concentrations that reduce the methane lifetime. There is also a small negative ERF contribution through the formation of nitrate aerosols, although only three of the AerChemMIP models include nitrate aerosols. The best estimate of the net ERF from changes in anthropogenic NOx emissions is –0.27 [–0.55 to 0.01] W m–2. The magnitude is somewhat greater than the AR5 estimate (–0.15 [–0.34 to +0.02] W m–2) but with a similar level of uncertainty. The difference between AR6 and AR5 estimates is possibly due to the different modeling protocols (see Supplementary Material: 6.SM.1).

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Anthropogenic emissions of SO2 lead to the formation of sulphate aerosols and a negative ERF through aerosol–radiation and aerosol–cloud interactions. The emissions-based ERFaci, which was not previously considered in AR5, is now included. The estimated ERF is thus considerably more negative than the AR5 estimate with a radiative forcing of –0.4 W m–2, despite the decline of ERF due to aerosols since 2011 (Section 7.3.3.1.3, Figure 6.12a). SO2 emissions are estimated to contribute to a negative ERF of –0.94 [–1.63 to –0.25] W m–2, with –0.23 W m–2 from aerosol–radiation interactions and –0.70 W m–2 from aerosol–cloud interactions. Emissions of NH3 lead to formation of ammonium-nitrate aerosols with an estimated ERF of –0.03 W m–2.

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On the global scale, as assessed in Chapter 3, anthropogenic aerosols have likely cooled GSAT since 1850–1900 driven by the negative aerosol forcing, while it is extremely likely that human-induced stratospheric ozone depletion has primarily driven stratospheric cooling between 1979 and the mid-1990s. Multiple modelling studies support the understanding that present-day emissions of SO2, a precursor for sulphate aerosols, are the dominant driver of near- surface air temperature responses in comparison to BC or OC even though, for some regions, BC forcing plays a key role (Baker et al. , 2015; Samset et al. , 2016; Stjern et al. , 2017; Zanis et al. , 2020) . Further, there is high confidence that the aerosol-driven cooling has led to detectable large-scale water-cycle changes since at least the mid-20thcentury as assessed in Chapter 8. The overall effect of surface cooling from anthropogenic aerosols is to reduce global precipitation and alter large-scale atmospheric circulation patterns (high confidence), primarily driven by the cooling effects of sulphate aerosols (Section 8.2.1). In addition, there is high confidence that darkening of snow through the deposition of black carbon and other light-absorbing particles enhances snowmelt (Section 7.3.4.3; SROCC Chapter 3).

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The asymmetric aerosol and greenhouse gas forcing on regional-scale climate responses have also been assessed to lead to contrasting effects on precipitation in Chapter 8. The asymmetric historical radiative forcing due to aerosols led to a southward shift in the tropical rain belt (high confidence) and contributed to the Sahel drought from the 1970s to the 1980s (high confidence). Furthermore, the asymmetry of the forcing led to contrasting effects in monsoon precipitation changes over West Africa, Southern Asia and Eastern Asia over much of the mid-20thcentury due to GHG-induced precipitation increases counteracted by anthropogenic aerosol-induced decreases (high confidence) (see Section 8.3 and Box 8.1).

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Considering anthropogenic emissions of precursors globally higher than the current emissions (SSP3-7.0 in 2050; Figure 6.20), the CMIP6 ensemble confirms thesurface ozone penalty due to climate changeover regions close to anthropogenic pollution sources or close to natural emissions sources of ozone precursors (e.g., biomass-burning areas), with a penalty of a few ppb for the annual mean, proportional to warming levels (Figure 6.14). This rate ranges regionally from 0.2–2 ppb °C–1 (Supplementary Material Figure 6.SM.1). The CMIP6 ESMs show this consistently for South East Asia (in line with Hong et al. (2019) and Schnell et al. (2016)) and for India (in line with Pommier et al., 2018) as well as in parts of Africa and South America, close to enhanced BVOC emissions (at least three out of four ESMs agree on the sign of change). The results are mixed in polluted regions of Europe and US because of lower anthropogenic precursor emissions which leads to a very low sensitivity of surface ozone to climate change (–0.5 ppb °C–1 to 0.5 ppb °C–1; Supplementary Material Figure 6.SM.1) and thus the ESMs can disagree on sign of changes for a given warming level. This heterogeneity in the results is also found in regional studies over North America (Gonzalez-Abraham et al. , 2015; Val Martin et al. , 2015; Schnell et al. , 2016; He et al. , 2018; Nolte et al. , 2018; Rieder et al. , 2018) or over Europe (Colette et al. , 2015; Lacressonnière et al. , 2016; Schnell et al. , 2016; Fortems-Cheiney et al. , 2017).

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Overall, warmer climate is expected to reduce surface ozone in unpolluted regions as a result of greater water vapour abundance accelerating ozone chemical loss (high confidence). Over regions with high anthropogenic and/or natural ozone precursor emissions, there is prevailing evidence that climate change will introduce a surface O3 penalty increasing with increasing warming levels (with a magnitude ranging regionally from 0.2–2 ppb °C–1) (medium to high confidence). Yet, there are uncertainties in processes affected in a warmer climate which can impact and modify future baseline and regional/local surface ozone levels. The response of surface ozone to future climate change through stratosphere–troposphere exchange, soil NOx emissions and wildfires is positive (medium confidence). In addition, there is low confidence in the magnitude of the effect of climate change on surface ozone through biosphere interactions (natural methane, non-methane BVOC emissions and ozone deposition) and lightning NOx emissions.

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Meteorological conditions, such as heatwaves, temperature inversions and atmospheric stagnation episodes favour air quality extremes and are influenced by changing climate (Fiore et al., 2015). The body of literature on the connection between climate change and extreme anthropogenic pollution episodes is essentially based on correlation and regression applied to observation reanalysis but the metrics and methodologies differ making quantitative comparisons difficult. Many emission processes in the natural systems are sensitive to temperature, and bursts of emissions as a reponse to extreme weather, as in the case of wildfires in dry conditions (Bondur et al., 2020; Xie et al., 2020) can occur, which would then add to the risk of extreme air pollution but are not sufficiently constrained to be quantitatively assessed.

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Policies addressing the reduction of either SLCFs or LLGHGs, often prioritize mitigation of emissions from specific anthropogenic sources, such as energy production, industry, transportation, agriculture, waste management and residential fuel use. The choice of the targetted sector and chosen measures will determine the ratios of emitted SLCFs and LLGHGs. These changes in emissions of co-emitted species will result in diverse responses driven by complex chemical and physical processes, and resulting climate perturbations. The understanding of the co-benefits through sectoral mitigation efforts (as well as potential negative impacts) is essential to inform policymaking.

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As a consequence, in idealized ESM studies that assume an instantaneous removal of all anthropogenic or fossil fuel-related emissions, a rapid change in aerosol levels occurs leading to large increases in GSAT with the rate of warming lasting for several years. Similarly, the thermal inertia causes the pulse emissions (Figure 6.15) of SLCFs to have a significant effect on surface temperature even after 10 years.

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There is consensus that on‐road transportation sector emissions, including gasoline and diesel, are important anthropogenic contributors to elevated surface ozone and PM2.5 concentrations (Chambliss et al. , 2014; Lelieveld et al., 2015b ; Silva et al. , 2016; Anenberg et al. , 2019). At a global scale, land transportation has been estimated to be the dominant contributor to surface ozone concentrations in populated areas (Silva et al., 2016) and ozone-induced vegetation damages (Section 6.4.4; Unger et al., 2020). Furthermore, it is now well established that real-world diesel NOx emissions rates are substantially higher, the so-called ‘excess NOx’, in all regional markets than in laboratory tests, worsening air quality (Anenberg et al., 2017; Jonson et al., 2017; Chossière et al., 2018) and contributing to slightly larger warming on the scale of years and smaller warming at the decadal scale (Tanaka et al., 2018). Excess NOx emissions from key global diesel markets are estimated at 4.6 Tg yr–1 in 2015, with annual mean ozone and PM2.5 increases of 1 ppb and 1µg m–3across large regions of Europe, India and China (Anenberg et al., 2017).

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The attribution of present-day surface PM2.5 and ozone concentrations to sectors and regions (Figure 6.17) is based on 2014 CMIP6 emissions used in the TM5-FASST model (Van Dingenen et al., 2018) that has been widely applied to analyse air quality in regional and global scenarios (e.g., Van Dingenen et al. , 2009; Rao et al. , 2016, 2017; Vandyck et al. , 2018; Harmsen et al. , 2020b). Regions with the largest year-2014 population-weighted annual average surface PM2.5 concentrations are Southern Asia, Eastern Asia and the Middle East. The dominant anthropogenic source of ambient PM2.5 in Southern Asia are the residential and commercial sectors (biomass and coal fuel-based cooking and heating) with secondary contributions from energy and industry. In Eastern Asia, the main anthropogenic sources of ambient PM2.5 are energy, industry and residential sources. Natural sources, predominantly dust, are the most important PM2.5 source in the Middle East, Africa and Eurasia, contributing about 40–70% of ambient annual average concentrations (Figure 6.17). Agriculture is an important contributor to ambient PM2.5 in Europe and North America, while open biomass burning is a major contributor in South East Asia and Developing Pacific, North America as well as Latin America. These results are consistent with several global and regional studies, where contribution of emissions sources to ambient PM2.5 or premature mortality was estimated at different scales (e.g., Guttikunda et al. , 2014; Lelieveld et al. 2015b ,; Amann et al. , 2017; Qiao et al. , 2018; Venkataraman et al. , 2018; Wu et al. , 2018).

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Natural sources contribute more than 50% to surface ozone in all regions except Southern Asia and South East Asia. Southern Asia, Eastern Asia and the Middle East experience the highest surface ozone levels of all regions. For ozone, the anthropogenic sectoral attribution is more uniform across regions than for PM2.5, except for Southern and South East Asia, where land transportation plays a larger role, and Eastern Asia, where the most significant contribution is from energy and industry. Land transportation and energy are the most important contributors to ozone across many of the regions, with smaller contributions from agriculture, biomass burning, waste management and industry. Open biomass burning is not a major contributor to surface ozone, except for in Africa, Latin America and South East Asia where its contribution is estimated at about 5–10% of anthropogenic sources. The relative importance of natural and anthropogenic emissions sources on surface ozone has been assessed in several studies (Uherek et al., 2010; Zare et al., 2014; Mertens et al., 2020; Unger et al., 2020) and the results are comparable with the estimates of the TM5-FASST used here.

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Residential and commercial cooking and heating are among the most important anthropogenic sources of ambient PM2.5, except in the Middle East and Asia-Pacific Developed (high confidence) and agriculture is the dominant source in Europe and North America (medium confidence). Energy and industry are important PM2.5 contributors in most regions, except Africa (high confidence). Energy and land transportation are the major anthropogenic sources of ozone across many world regions (medium to high confidence).

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Local response to global reduction can be higher than the global temperature response, particularly for regions subjected to rapid changes. Hence, mitigation of rapid warming in the Arctic has been subject to an increasing number of studies (Sand et al. , 2013b, 2016; Jiao et al. , 2014; AMAP, 2015a, b; Mahmood et al. , 2016; Christensen et al. , 2019). Considering maximum technically feasible reductions (MTFR) for methane globally and an idealized strategy reducing key global anthropogenic sources of BC (about 80% reduction by 2030 and sustained thereafter) and precursors of ozone was estimated to jointly bring a reduction of Arctic warming, averaged over the 2041–2050 period, between 0.2°C and 0.6°C (AMAP, 2015a; Sand et al., 2016). Stohl et al. (2015) have estimated that a global SLCF mitigation strategy (excluding further reduction of SO2) would lead to about twice as high a temperature reduction (–0.44 (–0.39 to –0.49) °C) in the Arctic than the global response to such mitigation.

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There is robust evidence that reducing atmospheric methane will benefit climate and improve air quality through near-surface ozone reduction (Fiore et al., 2015; Shindell et al., 2017a) and wide agreement that strategies reducing methane offer larger (and less uncertain) climate benefits than policies addressing BC (e.g., Smith andMizrahi, 2013; Rogelj et al., 2014b, 2018b; Stohl et al., 2015; Christensen et al., 2019; Shindell and Smith, 2019). SR1.5 (Rogelj et al., 2018b) highlighted the importance of methane mitigation in limiting warming to 1.5ºC in addition to net zero CO2 emissions by 2050. Implementation of the identified maximum technically feasible reductions (MTFR) potential for methane globally, estimated at nearly 50% reduction (or 205 Tg CH4 in 2050) of anthropogenic emissions from the baseline, would lead to a reduction in warming, calculated as the differences between the baseline and MTFR scenario, for the 2036–2050 period of about 0.20°C ± 0.02°C globally (AMAP, 2015b). Plausible levels of methane mitigation, achieved with proven technologies, can increase the feasibility of achieving the Paris Agreement goal through slightly slowing down the pace of CO2 reductions (but not changing the final CO2 reduction goal) while this benefit is enhanced by the indirect effects of methane mitigation on ozone levels (Collins et al., 2018). Adressing methane mitigation appears even more important in view of recently observed growth in atmospheric concentrations that is linked to increasing anthropogenic emissions (Section 5.2.2).

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However, the design of suitable policies addressing these SDGs can be difficult because of the complexity of linking emissions to impacts on human health, ecosystems, equity, infrastructure and costs. Beyond the fact that several species are co-emitted, interlinkage between species, such as through atmospheric chemistry, can weaken the benefit of emissions reduction efforts. An illustration lies in the recent (2013–2017) reduction of aerosols over China (Silver et al., 2018; Zheng et al., 2018b) resulting from the strategy to improve air quality (‘Clean Air Action’); this has successfully reduced the level of PM2.54 but has led to a concurrent increase in surface ozone, partly due to declining heterogeneous interactions of ozone precursors with aerosols (K. Li et al., 2019; Yu et al., 2019). This side effect on ozone has been addressed since then by amending the legislation to target NMVOC sources, especially solvent use. Complex interactions between anthropogenic and biogenic volatile compounds are also at play and reduction of certain SLCFs could possibly promote new particle formation from organic vapours (e.g., Lehtipalo et al., 2018). Finally, a recent example of this complexity is the mixed effects on ozone pollution induced by NOx decrease during the COVID-19 pandemic (Cross-Chapter Box 6.1). Thus, the climate and air pollution effects of policies depend strongly on the choice of regulated compounds and the degree of reduction. Such policies have to be informed by strong science support, including for example multi-model analyses such as HTAP (UNECE, 2010) and AMAP (AMAP, 2015a, b), based on global and regional CCMs. This is essential to capture the complexity and inform the policy development process.

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Using similar methodologies, Forster et al. (2020) assembled activity data and emissions estimates for other greenhouse gases and aerosols and their precursors. Anthropogenic NOx emissions, which are largely from the transport sector, are estimated to have decreased by a maximum of 35% in April (medium confidence). Species whose emissions are dominated by other sectors, such as methane and NH3 from agriculture, saw smaller reductions.

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COVID-19-related emissions changes primarily exerted effective radiative forcing (ERF) through reduced emissions rates of CO2 and methane, altered abundance of SLCFs, notably ozone, NO2 and aerosols, and through other changes in anthropogenic activities, notably a reduction in the formation of aviation-induced cirrus clouds.

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This section assesses the 21st-century projections of SLCF emissions, abundances and responses in terms of climate and air quality following the SSPs (Chapter 1, Section 1.6.1.3 and Cross-Chapter Box 1.5; Riahi et al., 2017; Gidden et al., 2019). The future evolution of atmospheric abundances and the resulting climate and AQ responses is driven mainly by anthropogenic emissions and by natural emissions modulated by chemical, physical and biological processes as discussed in Sections 6.2 and 6.3. Like the RCP scenarios used in AR5, the SSP emissions scenarios consider only direct anthropogenic (including biomass burning) emissions and do not project natural emissions changes due to climate or land-use changes; ESMs intrinsically consider these biogeochemical feedbacks to varying degrees (Section 6.4.5). We rely on future projections based on CMIP6 ESMs with comprehensive representation of chemistry, aerosol microphysics and biospheric processes that participated in the ScenarioMIP (O’Neill et al., 2016) and AerChemMIP (Collins et al., 2017). However, due to the high computational costs of running coupled ESMs, they cannot be used for quantifying the contributions from individual species, regions and sectors, and across the scenarios. Therefore, reduced complexity models (Box 1.3 and Cross-Chapter Box 7.1), which represent chemistry and complex ESM interactions in parametrized forms updated since the AR5, are also applied here.

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Until the mid-21st century, SSP3-7.0 and SSP5-8.5 scenarios project no reduction in NOx emissions at the global level with decline in most OECD countries and Eastern Asia, driven by existing legislation in power, industry and transportation (e.g., Tong et al., 2020), and continued increase in the rest of the world (Figures 6.18 and 6.19). Towards the end of the century, similar trends continue in SSP3-7.0 while emissions in SSP5 decline strongly owing to faster technological progress and stronger air-quality action (Rao et al., 2017; Riahi et al., 2017). By 2100, the ‘Regional Rivalry’ (SSP3) scenario emissions of NOx (and most other SLCFs, except ammonia) are typically twice as high as the next highest SSP projection, both at the global (Figure 6.18) and regional levels (Figure 6.19). In emissions pathways consistent with Paris Agreement goals (SSP1-1.9 or ­SSP1-2.6; Section 1.6.1), NOx drops, compared to 2015, by 50% in SSP1-2.6 and by 65% in SSP1-1.9 by 2050, is reduced by about 70% by 2100, resulting in global emissions levels comparable to the 1950s and below the RCP range. In these pathways considering strong climate change mitigation, similar reductions are projected at the regional level, except in Africa (less than 50% decline) due to its high share of biomass emissions as well as strong growth in population and fossil fuel use. The trends in anthropogenic and biomass-burning emissions for other ozone precursors (NMVOC, CO) are similar to that of NOx.

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Global emissions of carbonaceous aerosols are projected to decline in all SSP scenarios (Figure 6.18) except SSP3-7.0. In that scenario, which also has much higher emissions than any of the RCPs, about half of the anthropogenic BC originates from cooking and heating on solid fuels, mostly in Asia and Africa (Figure 6.19), where only limited progress in access to clean energy is achieved. Slow progress in improving waste management, high coal use in energy and industry, and no further progress in controlling diesel engines in Asia, Africa and Latin America contributes most of the remaining emissions, resulting in about 90% of anthropogenic BC emitted in the non-OECD world by 2100 in SSP3-7.0. A similar picture emerges for OC but with greater contribution of the waste management sector and biomass burning, and lower impact of transportation and industry developments. Since scenarios compliant with Paris Agreement goals (SSP1-1.9 or SSP1-2.6; Section 1.6.1) include widespread access to clean energy already by 2050, the global and regional emissions of BC decline by 70–75% by 2050 and 80% by 2100 relayive to 2015. The decline in the residential sector (about 90% by 2050 and over 95% by 2100) is accompanied by a strong reduction in transport (over 98%) and the decarbonization of the industry and energy sector. About 50% of remaining BC emissions in SSP1-1.9 or SSP1-2.6 are projected to originate from waste and open biomass burning of which open burning of waste represent a significant part. Some studies suggest this might be pessimistic as, for example, efficient waste management (consistent with SDG goals) could potentially eliminate the open burning of solid residues (Gómez-Sanabria et al., 2018), which accounts for over 30% of BC emissions in SSP1-1.9 in 2050 or 2100.

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While the emulators used for GSAT projections shown in Figure 6.22 do not take the regional perspective into account, the set of simulations performed within the Hemispheric Transport of Air Pollutants Phase 2 (HTAP2) project (Galmarini et al., 2017) allows for this perspective. The results from the chemistry–transport model OsloCTM3 taking part in the HTAP2 have been used by Lund et al. (2020) to derive region-specific absolute global warming potentials (AGTPs; cf. Aamaas et al., 2016) for each emitted SLCF and each HTAP2 region. With this set of AGTPs, Lund et al. (2020) estimate the transient response in GSAT to the regional anthropogenic emissions. There are important differences in the contributions to GSAT in 2040 and 2100 (relative to 2020) between the regions and scenarios, mainly due to the differences in the mixture of emitted SLCFs (Figure 6.23). There is overall good agreement between the total net contribution from all regions to GSAT and the estimate based on global ERF and the two-layer emulator (Figure 6.22).

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In summary, the warming induced by SLCF changes is stable after 2040 in the WGI core set of SSP scenarios associated with lower global air pollution as long as methane emissions are also mitigated, but the overall warming induced by SLCF changes is higher in scenarios in which air quality continues to deteriorate (caused by growing fossil fuel use and limited air pollution control) (high confidence). In the SSP3-7.0 context, applying an additional strong air pollution control resulting in reductions in anthropogenic aerosols and non-methane ozone precursors would lead to an additional near-term global warming of 0.08°C with a very likely range of [–0.05 to 0.25] °C (compared with SSP3-7.0 for the same period). A simultaneous methane mitigation consistent with SSP1’s stringent climate change mitigation policy implemented in the SSP3 world, could entirely alleviate this warming and even lead to a cooling of 0.07°C with a very likely range of [–0.08 to +0.18] °C (compared withSSP3-7.0 for the same period). Across the SSPs, the reduction of methane, ozone precursors and HFCs can make a 0.2 [0.1 to 0.4] °C difference on GSAT in 2040 and a 0.8 [0.5 to 1.3] °C difference at the end of the 21st century (Figure 6.24), which is substantial in the context of the Paris Agreement. Sustained methane mitigation, wherever it occurs, stands out as an option that combines near- and long-term gains on surface temperature (high confidence) and leads to air pollution benefits by reducing surface ozone level globally (high confidence).

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Li, K. et al., 2019: Anthropogenic drivers of 2013–2017 trends in summer surface ozone in China. Proceedings of the National Academy of Sciences, 116(2), 422–427, doi: 10.1073/pnas.1812168116.

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Li, K. et al., 2020: Increases in surface ozone pollution in China from 2013 to 2019: anthropogenic and meteorological influences. Atmospheric Chemistry and Physics, 20(19), 11423–11433, doi: 10.5194/acp-20-11423-2020.

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Li, M. et al., 2019: Persistent growth of anthropogenic non-methane volatile organic compound (NMVOC) emissions in China during 1990–2017: Drivers, speciation and ozone formation potential. Atmospheric Chemistry and Physics, 19(13), 8897–8913, doi: 10.5194/acp-19-8897-2019.

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Earth’s energy budget encompasses the major energy flows of relevance for the climate system (Figure 7.2). Virtually all the energy that enters or leaves the climate system does so in the form of radiation at the TOA. The TOA energy budget is determined by the amount of incoming solar (shortwave) radiation and the outgoing radiation that is composed of reflected solar radiation and outgoing thermal (longwave) radiation emitted by the climate system. In a steady-state climate, the outgoing and incoming radiative components are essentially in balance in the long-term global mean, although there are still fluctuations around this balanced state that arise through internal climate variability (Brown et al., 2014; Palmer and McNeall, 2014). However, anthropogenic forcing has given rise to a persistent imbalance in the global mean TOA radiation budget that is often referred to as Earth’s energy imbalance (e.g., Trenberth et al., 2014; von Schuckmann et al., 2016), which is a key element of the energy budget framework (N; Box 7.1, Equation 7.1) and an important metric of the rate of global climate change (Hansen et al., 2005a; von Schuckmann et al., 2020). In addition to the TOA energy fluxes, Earth’s energy budget al.o includes the internal flows of energy within the climate system, which characterize the climate state. The surface energy budget consists of the net solar and thermal radiation as well as the non-radiative components such as sensible, latent and ground heat fluxes (Figure 7.2, upper panel). It is a key driver of the global water cycle, atmosphere and ocean dynamics, as well as a variety of surface processes.

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Since AR5, there is additional evidence that strong decadal changes in surface solar radiation have occurred under cloud-free conditions, as shown for long-term observational records in Europe, USA, China, India and Japan (Xu et al., 2011; Gan et al., 2014; Manara et al., 2016; Soni et al., 2016; Tanaka et al., 2016; Kazadzis et al., 2018; J. Li et al., 2018; Yang et al., 2019; Wild et al., 2021). This suggests that changes in the composition of the cloud-free atmosphere, primarily in aerosols, contributed to these variations, particularly since the second half of the 20th century (Wild, 2016). Water vapour and other radiatively active gases seem to have played a minor role (Wild, 2009; Mateos et al., 2013; Posselt et al., 2014; Yang et al., 2019). For Europe and East Asia, modelling studies also point to aerosols as an important factor for dimming and brightening by comparing simulations that include or exclude variations in anthropogenic aerosol and aerosol-precursor emissions (Golaz et al., 2013; Nabat et al., 2014; Persad et al., 2014; Folini and Wild, 2015; Turnock et al., 2015; Moseid et al., 2020). Moreover, decadal changes in surface solar radiation have often occurred in line with changes in anthropogenic aerosol emissions and associated aerosol optical depth (Streets et al., 2006; Wang and Yang, 2014; Storelvmo et al., 2016; Wild, 2016; Kinne, 2019). However, further evidence for the influence of changes in cloudiness on dimming and brightening is emphasized in some studies (Augustine and Dutton, 2013; Parding et al., 2014; Stanhill et al., 2014; Pfeifroth et al., 2018; Antuña-Marrero et al., 2019). Thus, the contribution of aerosol and clouds to dimming and brightening is still debated. The relative influence of cloud-mediated aerosol effects versus direct aerosol radiative effects on dimming and brightening in a specific region may depend on the prevailing pollution levels (Section 7.3.3; Wild, 2016).

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In summary, since AR5, multi-decadal decreasing and increasing trends in surface solar radiation of up to several percent per decade have been detected at many more locations, even in remote areas. There is high confidence that these trends are widespread, and not localized phenomena or measurement artefacts. The origin of these trends is not fully understood, although there is evidence that anthropogenic aerosols have made a substantial contribution (medium confidence). There is medium confidence that downward and upward thermal radiation has increased since the 1970s, while there remains low confidence in the trends in surface sensible and latent heat.

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The net effective radiative forcing (ERF) of the Earth system since 1971 has been positive (Section 7.3 and Box 7.2, Figure 1b,e), mainly as a result of increases in atmospheric greenhouse gas concentrations (Sections 2.2.8 and 7.3.2). The ERF of these positive forcing agents have been partly offset by that of negative forcing agents, primarily due to anthropogenic aerosols (Section 7.3.3), which dominate the overall uncertainty. The net energy inflow to the Earth system from ERF for the period 1971–2018 is estimated to be 937 ZJ (1 ZJ = 1021J) with a likely range of 644 to 1259 ZJ (Box 7.2, Figure 1b).

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Effective radiative forcing (ERF) quantifies the energy gained or lost by the Earth system following an imposed perturbation (for instance in GHGs, aerosols or solar irradiance). As such it is a fundamental driver of changes in the Earth’s TOA energy budget. ERF is determined by the change in the net downward radiative flux at the TOA (Box 7.1) after the system has adjusted to the perturbation but excluding the radiative response to changes in surface temperature. This section outlines the methodology for ERF calculations (Section 7.3.1) and then assesses the ERF due to greenhouse gases (Section 7.3.2), aerosols (Section 7.3.3) and other natural and anthropogenic forcing agents (Section 7.3.4). These are brought together in (Section 7.3.5 for an overall assessment of the present-day ERF and its evolution over the historical time period from 1750 to 2019. The same section also evaluates the surface temperature response to individual ERFs.

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This section considers direct anthropogenic effects on stratospheric water vapour by oxidation of methane. Since AR5 the SARF from methane-induced stratospheric water vapour changes has been calculated in Winterstein et al., 2019, corresponding to 0.09 W m–2 when scaling to 1850 to 2014 methane changes. This is marginally larger than the AR5 assessed value of 0.07 ± 0.05 W m–2(Myhre et al., 2013b). Wang and Huang (2020) quantified the adjustment terms to a stratospheric water vapour change equivalent to a forcing from a 2×CO2 warming (which has a different vertical profile). They found that the ERF was less than 50% of the SARF due to high-cloud decrease and upper tropospheric warming. The assessed ERF is therefore 0.05 ± 0.05 W m–2 with a lower limit reduced to zero and the central value and upper limit reduced to allow for adjustment terms. This still encompasses the two recent SARF studies. There is medium confidence in the SARF from agreement with the recent studies and AR5. There is low confidence in the adjustment terms.

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Anthropogenic activity, and particularly burning of biomass and fossil fuels, has led to a substantial increase in emissions of aerosols and their precursors, and thus to increased atmospheric aerosol concentrations since the pre-industrial era (Sections 2.2.6 and 6.3.5, and Figure 2.9). This is particularly true for sulphate and carbonaceous aerosols (Section 6.3.5). This has in turn led to changes in the scattering and absorption of incoming solar radiation, and also affected cloud micro- and macro-physics and thus cloud radiative properties. Aerosol changes are heterogeneous in both space and time and have impacted not just Earth’s radiative energy budget but also air quality (Sections 6.1.1 and 6.6.2). Here, the assessment is focused exclusively on the global mean effects of aerosols on Earth’s energy budget, while regional changes and changes associated with individual aerosol compounds are assessed in (Chapter 6 (Sections 6.4.1 and 6.4.2).

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Consistent with the terminology introduced in Box 7.1, the ERF due to changes from direct aerosol–radiation interactions (ERFari) is equal to the sum of the instantaneous top-of-atmosphere (TOA) radiation change (IRFari) and the subsequent adjustments. Likewise, the ERF following interactions between anthropogenic aerosols and clouds (ERFaci, referred to as ‘indirect aerosol effects’ in previous assessment reports) can be divided into an instantaneous forcing component (IRFaci) due to changes in cloud droplet (and indirectly also ice crystal) number concentrations and sizes, and the subsequent adjustments of cloud water content or extent. While these changes are thought to be induced primarily by changes in the abundance of cloud condensation nuclei (CCN), a change in the number of ice nucleating particles (INPs) in the atmosphere may also have occurred, and thereby contributed to ERFaci by affecting properties of mixed-phase and cirrus (ice) clouds. In the following, an assessment of IRFari and ERFari (Section 7.3.3.1) focusing on observation-based (Section 7.3.3.1.1) as well as model-based (Section 7.3.3.1.2) evidence is presented. The same lines of evidence are presented for IRFaci and ERFaci in Section 7.3.3.2. These lines of evidence are then compared with TOA energy budget constraints on the total aerosol ERf (Section 7.3.3.3) before an overall assessment of the total aerosol ERF is given in Section 7.3.3.4. For the model-based evidence, all estimates are generally valid for 2014 relative to 1750 (the time period spanned by CMIP6 historical simulations), while for observation-based evidence the assessed studies use slightly different end points, but they all generally fall within a decade (2010–2020).

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Estimating IRFari requires an estimate of industrial-era changes in aerosol optical depth (AOD) and absorption AOD, which are often taken from global aerosol model simulations. Since AR5, updates to methods of estimating IRFari based on aerosol remote sensing or data-assimilated reanalyses of atmospheric composition have been published. Ma et al. (2014) applied the method of Quaas et al. (2008) to updated broadband radiative flux measurements from CERES, MODIS-retrieved AODs, and modelled anthropogenic aerosol fractions to find a clear-sky IRFari of −0.6 W m−2. This would translate into an all-sky estimate of about −0.3 W m−2 based on the clear-sky to all-sky ratio implied by Kinne (2019). Rémy et al. (2018) applied the methods of Bellouin et al. (2013a) to the reanalysis by the Copernicus Atmosphere Monitoring Service, which assimilates MODIS total AOD. Their estimate of IRFari varies between −0.5 W m–2 and −0.6 W m−2 over the period 2003–2018, and they attribute those relatively small variations to variability in biomass-burning activity. Kinne (2019) provided updated monthly total AOD and absorption AOD climatologies, obtained by blending multi-model averages with ground-based sun-photometer retrievals, to find a best estimate of IRFari of −0.4 W m−2. The updated IRFari estimates above are all scattered around the midpoint of the IRFari range of −0.35 ± 0.5 W m−2 assessed by AR5 (Boucher et al., 2013).

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The more negative estimate of Rémy et al. (2018) is due to neglecting a small positive contribution from absorbing aerosols above clouds and obtaining a larger anthropogenic fraction than Kinne (2019). Rémy et al. (2018) also did not update their assumptions on black carbon anthropogenic fraction and its contribution to absorption to reflect recent downward revisions (Section 7.3.3.1.2). Kinne (2019) made those revisions, so more weight is given to that study to assess the central estimate of satellite-based IRFari to be only slightly stronger than reported in AR5 at –0.4 W m–2. While uncertainties in the anthropogenic fraction of total AOD remain, improved knowledge of anthropogenic absorption results in a slightly narrowervery likely range here than in AR5. The assessed best estimate and very likely IRFari range from observation-based evidence is therefore –0.4 ± 0.4 W m–2, but with medium confidence due to the limited number of studies available .

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The observation-based assessment of IRFari of –0.4 ± 0.4 W m–2 and the corresponding model-based assessment of –0.2 ± 0.2 W m–2 can be compared to the range of –0.45 to –0.05 W m–2 that emerged from a comprehensive review in which an observation-based estimate of anthropogenic AOD was combined with model-derived ranges for all relevant aerosol radiative properties (Bellouin et al., 2020). Based on the above, IRFari is assessed to be –0.25 ± 0.2 W m–2(medium confidence).

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Anthropogenic aerosol particles primarily affect water clouds by serving as additional cloud condensation nuclei (CCN) and thus increasing cloud drop number concentration (Nd; Twomey, 1959). Increasing Nd while holding liquid water content constant reduces cloud drop effective radius (re), increases the cloud albedo, and induces an instantaneous negative radiative forcing (IRFaci). The clouds are thought to subsequently adjust by a slowing of the drop coalescence rate, thereby delaying or suppressing rainfall. Rain generally reduces cloud lifetime and thereby liquid water path (LWP, i.e., the vertically integrated cloud water) and/or cloud fractional coverage (Cf; Albrecht, 1989), thus any aerosol-induced rain delay or suppression would be expected to increase LWP and/or Cf. Such adjustments could potentially lead to an ERFaci considerably larger in magnitude than the IRFaci alone. However, adding aerosols to non-precipitating clouds has been observed to have the opposite effect (i.e., a reduction in LWP and/or Cf) (Lebsock et al., 2008; Christensen and Stephens, 2011). These findings have been explained by enhanced evaporation of the smaller droplets in the aerosol-enriched environments, and resultant enhanced mixing with ambient air, leading to cloud dispersal.

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A small subset of aerosols can also serve as ice nucleating particles (INPs) that initiate the ice phase in supercooled water clouds, and thereby alter cloud radiative properties and/or lifetimes. However, the ability of anthropogenic aerosols (specifically BC) to serve as INPs in mixed-phase clouds has been found to be negligible in recent laboratory studies (e.g., Vergara-Temprado et al., 2018). No assessment of the contribution to ERFaci from cloud phase changes induced by anthropogenic INPs will therefore be presented.

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In ice (cirrus) clouds (cloud temperatures less than –40°C), INPs can initiate ice crystal formation at relative humidity much lower than that required for droplets to freeze spontaneously. Anthropogenic INPs can thereby influence ice crystal numbers and thus cirrus cloud radiative properties. At cirrus temperatures, certain types of BC have in fact been demonstrated to act as INPs in laboratory studies (Ullrich et al., 2017; Mahrt et al., 2018), suggesting a non-negligible anthropogenic contribution to INPs in cirrus clouds. Furthermore, anthropogenic changes to drop number also alter the number of droplets available for spontaneous freezing, thus representing a second pathway through which anthropogenic emissions could affect cirrus clouds.

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In AR5 the statistical relationship between cloud microphysical properties and aerosol index (AI; AOD multiplied by Ångström exponent) was used to make inferences about IRFaci were assessed alongside other studies which related cloud quantities to AOD. However, it is now well-documented that the latter approach leads to low estimates of IRFaci since AOD is a poor proxy for cloud-base CCN (Penner et al., 2011; Stier, 2016). Gryspeerdt et al. (2017) demonstrated that the statistical relationship between droplet concentration and AOD leads to an inferred IRFaci that is underestimated by at least 30%, while the use of AI leads to estimates of IRFaci to within ±20%, if the anthropogenic perturbation of AI is known.

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Since AR5, several studies assessed the global IRFaci from satellite observations using different methods (Table 7.7). All studies relied on statistical relationships between aerosol and cloud quantities to infer sensitivities. Four studies inferred IRFaci by estimating the anthropogenic perturbation of Nd(cloud drop number concentration). For this, Bellouin et al. (2013b) and Rémy et al. (2018) made use of regional-seasonal regressions between satellite-derived Nd and AOD following Quaas et al. (2008), while Gryspeerdt et al. (2017) used AI instead of AOD in the regression to infer IRFaci. McCoy et al. (2017b) instead used the sulphate-specific mass derived in the MERRA aerosol reanalysis that assimilated MODIS AOD (Rienecker et al., 2011). All approaches have in common the need to identify the anthropogenic perturbation of the aerosol to assess IRFaci. Gryspeerdt et al. (2017) and Rémy et al. (2018) used the same approach as Bellouin et al. (2013b), while McCoy et al. (2017b) used an anthropogenic fraction from the AEROCOM multi-model ensemble (Schulz et al., 2006). Chen et al. (2014), Christensen et al. (2016a) and Christensen et al. (2017) derived the combination of IRFaci and the LWP adjustment to IRFaci (‘intrinsic forcing’ in their terminology). They relate AI and cloud albedo statistically and use the anthropogenic aerosol fraction from Bellouin et al. (2013b). This was further refined by Hasekamp et al. (2019) who used additional polarimetric satellite information over ocean to obtain a better proxy for CCN. They derived an IRFaci of –1.14 [–1.72 to –0.84] W m–2. The variant by Christensen et al. (2017) is an update compared to the Chen et al. (2014) and Christensen et al. (2016a) studies in that it better accounts for ancillary influences on the aerosol retrievals such as aerosol swelling and three-dimensional radiative effects. McCoy et al. (2020) used the satellite-observed hemispheric difference in Nd as an emergent constraint on IRFaci as simulated by GCMs to obtain a range of –1.2 to –0.6 W m–2(95% confidence interval). Diamond et al. (2020) analysed the difference in clouds affected by ship emissions with unperturbed clouds and based on this inferred a global IRFaci of –0.69 [–0.99 to –0.44] W m–2.

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Summarizing the above findings related to statistical relationships and causal aerosol effects on cloud properties, there is high confidence that anthropogenic aerosols lead to an increase in cloud droplet concentrations. Taking the average across the studies providing IRFaci estimates discussed above and considering the general agreement among estimates (Table 7.7), IRFaci is assessed to be –0.7 ± 0.5 W m–2(medium confidence).

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The relationship between LWP and cloud droplet number is debated. Most recent studies (primarily based on MODIS data) find negative statistical relationships (Michibata et al., 2016; Toll et al., 2017; Sato et al., 2018; Gryspeerdt et al., 2019), while Rosenfeld et al. (2019) obtained a modest positive relationship. To increase confidence that observed relationships between aerosol emissions and cloud adjustments are causal, known emissions of aerosols and aerosol precursor gases into otherwise pristine conditions have been exploited. Ship exhaust is one such source. Goren and Rosenfeld (2014) suggested that both LWP and Cf increase in response to ship emissions, contributing approximately 75% to the total ERFaci in mid-latitude stratocumulus. Christensen and Stephens (2011) found that such strong adjustments occur for open-cell stratocumulus regimes, while adjustments are comparatively small in closed-cell regimes. Volcanic emissions have been identified as another important source of information (Gassó, 2008). From satellite observations, Yuan et al. (2011) documented substantially larger Cf, higher cloud tops, reduced precipitation likelihood, and increased albedo in cumulus clouds in the plume of the Kīlauea volcano in Hawaii. Ebmeier et al. (2014) confirmed the increased LWP and albedo for other volcanoes. In contrast, for the large Holuhraun eruption in Iceland, Malavelle et al. (2017) did not find any large-scale change in LWP in satellite observations. However, when accounting for meteorological conditions, McCoy et al. (2018) concluded that for cyclonic conditions, the extra Holuhraun aerosol did enhance LWP. Toll et al. (2017) examined a large sample of volcanoes and found a distinct albedo effect, but only modest LWP changes, on average. Gryspeerdt et al. (2019) demonstrated that the negative LWP–Nd relationship becomes very small when conditioned on a volcanic eruption, and therefore concluded that LWP adjustments are small in most regions. Similarly, Toll et al. (2019) studied clouds downwind of various anthropogenic aerosol sources using satellite observations and inferred an IRFaci of –0.52 W m–2 that was partly offset by 29% due to aerosol-induced LWP decreases.

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Identifying relationships between INP concentrations and cloud properties from satellites is intractable because the INPs generally represent a very small subset of the overall aerosol population at any given time or location. For ice clouds, only a few satellite studies have so far investigated responses to aerosol perturbations. Gryspeerdt et al. (2018) find a positive relationship between aerosol and ice crystal number for cold cirrus under strong dynamical forcing, which could be explained by an overall larger number of solution droplets available for homogeneous freezing in polluted regions. Zhao et al. (2018) conclude that the sign of the relationship between ice crystal size and aerosol depends on humidity. While these studies support modelling results finding that ice clouds do respond to anthropogenic aerosols (Section 7.3.3.2.2), no quantitative conclusions about IRFaci or ERFaci for ice clouds can be drawn based on satellite observations.

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Contributions to ERFaci from anthropogenic aerosols acting as INPs are generally not included in CMIP6 models. Two global modelling studies incorporating parametrizations based on recent laboratory studies both found a negative contribution to ERFaci (Penner et al., 2018; McGraw et al., 2020), with central estimates of –0.3 and –0.13 W m–2, respectively. However, previous studies have produced model estimates of opposing signs (Storelvmo, 2017). There is thus limited evidence and medium agreement for a small negative contribution to ERFaci from anthropogenic INP-induced cirrus modifications (low confidence).

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When the first top-down estimates emerged (e.g., Knutti et al., 2002), it became clear that some of the early (‘bottom-up’) ESM estimates of total aerosol ERF were inconsistent with the plausible top-down range. However, as more inverse estimates have been published, it has increasingly become clear that they too are model-dependent and span a wide range of ERF estimates, with confidence intervals that in some cases do not overlap (Forest, 2018). It has also become evident that these methods are sensitive to revised estimates of other forcings and/or updates to observational datasets. A recent review of 19 such estimates reported a mean of –0.77 W m–2 for the total aerosol ERF, and a 95% confidence interval of [–1.15 to –0.31] W m–2(Forest, 2018). Adding to that review, a more recent study using the same approach reported an estimate of total aerosol ERF of –0.89 [–1.82 to –0.01] W m–2(Skeie et al., 2018). However, in the same study, an alternative way of incorporating ocean heat content in the analysis produced a total aerosol ERF estimate of –1.34 [–2.20 to –0.46] W m–2, illustrating the sensitivity to the manner in which observations are included. A new approach to inverse estimates took advantage of independent climate radiative response estimates from eight prescribed SST and sea ice-concentration simulations over the historical period to estimate the total anthropogenic ERF. From this a total aerosol ERF of –0.8 [–1.6 to +0.1] W m–2 was derived (valid for near-present relative to the late 19th century). This range was found to be more invariant to parameter choices than earlier inverse approaches (Andrews and Forster, 2020).

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Here, the best estimate and range is revised relative to AR5 (Boucher et al., 2013), partly based on updates to the above lines of argument. Firstly, the studies that included aerosol effects on mixed-phase clouds in AR5 relied on the assumption that anthropogenic black carbon (BC) could act as INPs in these clouds, which has since been challenged by laboratory experiments (Kanji et al., 2017; Vergara-Temprado et al., 2018). There is no observational evidence of appreciable ERFs associated with aerosol effects on mixed-phase and ice clouds (Section 7.3.3.2.1), and modelling studies disagree when it comes to both their magnitude and sign (Section 7.3.3.2.2). Likewise, very few ESMs incorporate aerosol effects on deep convective clouds, and cloud-resolving modelling studies report different effects on cloud radiative properties depending on environmental conditions (Tao et al., 2012). Thus, it is not clear whether omitting such effects from ESMs would lead to any appreciable ERF biases, or if so, what the sign of such biases would be. As a result, all ESMs are given equal weight in this assessment. Furthermore, there is now a considerably expanded body of literature which suggests that early modelling studies that incorporated satellite observations may have resulted in overly conservative estimates of the magnitude of ERFaci (Section 7.3.3.2.1). Finally, based on an assessment of the longwave ERFaci in the CMIP5 models, the offset of +0.2 W m–2 applied in AR5 appears to be too large (Heyn et al., 2017). As in AR5, there is still reason to question the ability of ESMs to simulate adjustments in LWP and cloud cover in response to aerosol perturbation, but it is not clear that this will result in biases that exclusively increase the magnitude of the total aerosol ERf (Section 7.3.3.2.2).

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In addition to the large anthropogenic ERFs associated with WMGHGs and atmospheric aerosols assessed in Sections 7.3.2 and 7.3.3, land-use change, contrails and aviation-induced cirrus, and light-absorbing particles deposited on snow and ice have also contributed to the overall anthropogenic ERF and are assessed in Sections 7.3.4.1, 7.3.4.2 and 7.3.4.3. Changes in solar irradiance, galactic cosmic rays, and volcanic eruptions since pre-industrial times combined represent the natural contribution to the total (anthropogenic + natural) ERF and are discussed in Sections 7.3.4.4, 7.3.4.5 and 7.3.4.6.

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The total anthropogenic ERF over the industrial era (1750–2019) is estimated as 2.72 [1.96 to 3.48] W m–2(high confidence) (Table 7.8 and Annex III). This represents a 0.43 W m–2 increase over the assessment made in AR5 (Myhre et al., 2013b) for the period 1750–2011. This increase is a result of compensating effects. Atmospheric concentration increases of GHGs since 2011 and upwards revisions of their forcing estimates have led to a 0.59 W m–2 increase in their ERF. However, the total aerosol ERF is assessed to be more negative compared to AR5, due to revised estimates rather than trends (high confidence).

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The total (anthropogenic + natural) emulated GSAT between 1850–1900 and 2010–2019 is 1.14 [0.89 to 1.45] °C, compared to the assessed GSAT of 1.06 [0.88 to 1.21] °c (Section 2.3.1 and Cross Chapter Box 2.3). The emulated response is slightly warmer than the observations and has a larger uncertainty range. As the emulated response attempts to constrain to multiple lines of evidence (Supplementary Material 7.SM.2), only one of which is GSAT, they should not necessarily be expected to exactly agree. The larger uncertainty range in the emulated GSAT compared to the observations is reflective of the uncertainties in ECS, TCR and ERF (particularly the aerosol ERF) that drive the emulator response.

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The emulator gives a range of GSAT response for the period 1750 to 1850–1900 of 0.09 [0.04 to 0.14] °C from anthropogenic ERFs. These results are used as a line of evidence for the assessment of this change in (Chapter 1 (Cross-Chapter Box 1.2), which gives an overall assessment of 0.1°C [likely range –0.1 to +0.3] °C.

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Biogeophysical feedbacks are associated with changes in the spatial distribution and/or biophysical properties of vegetation, induced by surface temperature change and attendant hydrological cycle change. These vegetation changes can alter radiative fluxes directly via albedo changes, or via surface momentum or moisture flux changes and hence changes in cloud properties. However, the direct physiological response of vegetation to changes in CO2, including changes in stomatal conductance, is considered part of the CO2 effective radiative forcing rather than a feedback (Section 7.3.2.1). The time scale on which vegetation responds to climate change is relatively uncertain but can be from decades to hundreds of years (Willeit et al., 2014), and could occur abruptly or as a tipping point (Sections 5.4.9.1.1, 8.6.2.1 and 8.6.2.2); equilibrium only occurs when the soil system and associated nutrient and carbon pools equilibrate, which can take millennia (Brantley, 2008; Sitch et al., 2008). The overall effects of climate-induced vegetation changes may be comparable in magnitude to those from anthropogenic land-use and land-cover change (Davies-Barnard et al., 2015). Climate models that include a dynamical representation of vegetation (e.g., Reick et al., 2013; Harper et al., 2018) are used to explore the importance of biogeophysical feedbacks (Notaro et al., 2007; Brovkin et al., 2009; O’ishi et al., 2009; Port et al., 2012; Willeit et al., 2014; Alo and Anagnostou, 2017; W. Zhang et al., 2018; Armstrong et al., 2019). In AR5, it was discussed that such model experiments predicted that expansion of vegetation in the high latitudes of the Northern Hemisphere would enhance warming due to the associated surface-albedo change, and that reduction of tropical forests in response to climate change would lead to regional surface warming, due to reduced evapotranspiration (M. Collins et al., 2013), but there was no assessment of the associated feedback parameter. The SRCCL stated that regional climate change can be dampened or enhanced by changes in local land cover, but that this depends on the location and the season; however, in general the focus was on anthropogenic land-cover change, and no assessment of the biogeophysical feedback parameter was carried out. There are also indications of a marine biogeophysical feedback associated with surface-albedo change due to changes in phytoplankton (Frouin and Iacobellis, 2002; Park et al., 2015), but there is not currently enough evidence to quantitatively assess this feedback.

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Since 1870, observed SSTs in the tropical western Pacific Ocean have increased while those in the tropical eastern Pacific Ocean have changed less (Figure 7.14a and (Section 9.2.1). Much of the resultant strengthening of the equatorial Pacific temperature gradient has occurred since about 1980 due to strong warming in the west and cooling in the east (Figure 2.11b) concurrent with an intensification of the surface equatorial easterly trade winds and Walker circulation (Sections 3.3.3.1, 3.7.6, 8.3.2.3 and 9.2, and Figures 3.16f and 3.39f; England et al., 2014). This temperature pattern is also reflected in regional ocean heat content trends and sea level changes observed from satellite altimetry since 1993 (Bilbao et al., 2015; Richter et al., 2020). The observed changes may have been influenced by one or a combination of temporary factors including sulphate aerosol forcing (Smith et al., 2016; Takahashi and Watanabe, 2016; Hua et al., 2018), internal variability within the Indo-Pacific Ocean (Luo et al., 2012; Chung et al., 2019), teleconnections from multi-decadal tropical Atlantic SST trends (Kucharski et al., 2011, 2014, 2015; McGregor et al., 2014; Chafik et al., 2016; X. Li et al., 2016; Kajtar et al., 2017; Sun et al., 2017), teleconnections from multi-decadal Southern Ocean SST trends (Hwang et al., 2017), and coupled ocean–atmosphere dynamics which slow warming in the equatorial eastern Pacific (Clement et al., 1996; Cane et al., 1997; Seager et al., 2019). CMIP3 and CMIP5 ESMs have difficulties replicating the observed trends in the Walker circulation and Pacific Ocean SSTs over the historical record (Sohn et al., 2013; Zhou et al., 2016; Coats and Karnauskas, 2017), possibly due to model deficiencies including insufficient multi-decadal Pacific Ocean SST variability (Laepple and Huybers, 2014; Bilbao et al., 2015; Chung et al., 2019), mean state biases affecting the forced response or the connection between Atlantic and Pacific basins (Kucharski et al., 2014; Kajtar et al., 2018; Luo et al., 2018; McGregor et al., 2018; Seager et al., 2019), and/or a misrepresentation of radiative forcing (Sections 9.2.1 and 3.7.6). However, the observed trends in the Pacific Ocean SSTs are still within the range of internal variability as simulated by large initial condition ensembles of CMIP5 and CMIP6 models (Olonscheck et al., 2020; Watanabe et al., 2021). Because the causes of observed equatorial Pacific temperature gradient and Walker circulation trends are not well understood (Section 3.3.3.1), there is low confidence in their attribution to anthropogenic influences (Section 8.3.2.3), while there is medium confidence that the observed changes have resulted from internal variability (Sections 3.7.6 and 8.2.2.2).

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A key advance over the AR5 assessment is the broad agreement across multiple lines of evidence. These support a central estimate of ECS close to, or at least not inconsistent with, 3°C. This advance is foremost following improvements in the understanding and quantification of Earth’s energy imbalance, the instrumental record of global temperature change, and the strength of anthropogenic radiative forcing. Further advances include increased understanding of how the pattern effect influences ECS inferred from historical global warming (Sections 7.4.4 and 7.5.3), improved quantification of paleo climatechange from proxy evidence and a deepened understanding of how feedback mechanisms increase ECS in warmer climate states (Sections 7.4.3, 7.4.4 and 7.5.4), and also an improved quantification of individual cloud feedbacks (Sections 7.4.2 and 7.5.4.2). The assessment findings for ECS and TCR are summarized in Table 7.13 and Table 7.14, respectively, and also visualized in Figure 7.18.

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One prominent use of emissions metrics is for comparison of efforts measured against climate change goals or targets. One of the most commonly discussed goals is in Article 2 of the Paris Agreement which aims to limit the risks and impacts of climate change by setting temperature goals. In addition, the Paris Agreement has important provisions which relate to how the goals are to be achieved, including making emissions reductions in a manner that does not threaten food production (Article 2), an early emissions peaking target, and the aim to ‘achieve a balance between anthropogenic emissions by sources and removals by sinks of greenhouse gases in the second half of this century’ (Article 4). Article 4 also contains important context regarding international equity, sustainable development, and poverty reduction. Furthermore, the United Nations Framework Convention on Climate Change (UNFCCC) sets out as its ultimate objective, the ‘stabilization of greenhouse gas concentrations in the atmosphere at a level that would prevent dangerous anthropogenic interference with the climate system.’

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How the interpretation of the Paris Agreement and the meaning of ‘net zero’ emissions, reflects on the appropriate choice of metric is an active area of research (Schleussner et al., 2016, 2019; Fuglestvedt et al., 2018; Collins et al., 2020). Several possible scientific interpretations of the Article 2 and 4 goals can be devised, and these, along with emissions metric choice, have implications both for when a balance in GHG emissions, net zero CO2 emissions or net zero GHG emissions are achieved, and for their meaning in terms of temperature outcome (Fuglestvedt et al., 2018; Rogelj et al., 2018; Wigley, 2018). In AR6 net zero GHG emissions is defined as the condition in which metric-weighted anthropogenic GHG emissions are balanced by metric-weighted anthropogenic GHG removals over a specified period (see Box 1.4 and Appendix VII: Glossary). The quantification of net zero GHG emissions depends on the GHG emissions metric chosen to compare emissions and removals of different gases, as well as the time horizon chosen for that metric. As the choice of emissions metric affects the quantification of net zero GHG emissions, it therefore affects the resulting temperature outcome after net zero emissions are achieved (Lauder et al., 2013; Rogelj et al., 2015; Fuglestvedt et al., 2018; Schleussner et al., 2019). Schleussner et al. (2019) note that declining temperatures may be a desirable outcome of net zero. Rogelj and Schleussner (2019) also point out that the use of physical metrics raises questions of equity and fairness between developed and developing countries.

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Chung, E.-S., B. Soden, B.J. Sohn, and L. Shi, 2014: Upper-tropospheric moistening in response to anthropogenic warming. Proceedings of the National Academy of Sciences, 111(32), 11636–11641, doi: 10.1073/pnas.1409659111.

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Collins, W.D., D.R. Feldman, C. Kuo, and N.H. Nguyen, 2018: Large regional shortwave forcing by anthropogenic methane informed by Jovian observations. Science Advances, 4(9), eaas9593, doi: 10.1126/sciadv.aas9593.

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Fiedler, S., B. Stevens, and T. Mauritsen, 2017: On the sensitivity of anthropogenic aerosol forcing to model-internal variability and parameterizing a Twomey effect. Journal of Advances in Modeling Earth Systems, 9(2), 1325–1341, doi: 10.1002/2017ms000932.

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Fiedler, S. et al., 2019: Anthropogenic aerosol forcing-insights from multiple estimates from aerosol–climate models with reduced complexity. Atmospheric Chemistry and Physics, 19(10), 6821–6841, doi: 10.5194/acp-19-6821-2019.

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Ghan, S. et al., 2016: Challenges in constraining anthropogenic aerosol effects on cloud radiative forcing using present-day spatiotemporal variability. Proceedings of the National Academy of Sciences, 113(21), 5804–5811, doi: 10.1073/pnas.1514036113.

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Ghimire, B. et al., 2014: Global albedo change and radiative cooling from anthropogenic land cover change, 1700 to 2005 based on MODIS, land use harmonization, radiative kernels, and reanalysis. Geophysical Research Letters, 41(24), 9087–9096, doi: 10.1002/2014gl061671.

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Gordon, H. et al., 2016: Reduced anthropogenic aerosol radiative forcing caused by biogenic new particle formation. Proceedings of the National Academy of Sciences, 113(43), 12053–12058, doi: 10.1073/pnas.1602360113.

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Hoesly, R.M. et al., 2018: Historical (1750–2014) anthropogenic emissions of reactive gases and aerosols from the Community Emissions Data System (CEDS). Geoscientific Model Development, 11(1), 369–408, doi: 10.5194/gmd-11-369-2018.

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Lade, S.J. et al., 2018: Analytically tractable climate–carbon cycle feedbacks under 21st century anthropogenic forcing. Earth System Dynamics, 9(2), 507–523, doi: 10.5194/esd-9-507-2018.

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Lee, D.S.S. et al., 2020: The contribution of global aviation to anthropogenic climate forcing for 2000 to 2018. Atmospheric Environment, 244, 117834, doi: 10.1016/j.atmosenv.2020.117834.

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Lund, M.T. et al., 2018a: Concentrations and radiative forcing of anthropogenic aerosols from 1750 to 2014 simulated with the Oslo CTM3 and CEDS emission inventory. Geoscientific Model Development, 11(12), 4909–4931, doi: 10.5194/gmd-11-4909-2018.

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Myhre, G. et al., 2013b: Anthropogenic and Natural Radiative Forcing. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change[Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 659–740, doi: 10.1017/ cbo9781107415324.018.

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Myhre, G. et al., 2017: Multi-model simulations of aerosol and ozone radiative forcing due to anthropogenic emission changes during the period 1990–2015. Atmospheric Chemistry and Physics, 17(4), 2709–2720, doi: 10.5194/acp-17-2709-2017.

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Nabat, P., S. Somot, M. Mallet, A. Sanchez-Lorenzo, and M. Wild, 2014: Contribution of anthropogenic sulfate aerosols to the changing Euro-Mediterranean climate since 1980. Geophysical Research Letters, 41(15), 5605–5611, doi: 10.1002/2014gl060798.

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O’Connor, F.M. et al., 2021: Assessment of pre-industrial to present-day anthropogenic climate forcing in UKESM1. Atmospheric Chemistry and Physics, 21(2), 1211–1243, doi: 10.5194/acp-21-1211-2021.

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Penner, J.E., C. Zhou, A. Garnier, and D.L. Mitchell, 2018: Anthropogenic Aerosol Indirect Effects in Cirrus Clouds. Journal of Geophysical Research: Atmospheres, 123(20), 11652–11677, doi: 10.1029/2018jd029204.

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Port, U., V. Brovkin, and M. Claussen, 2012: The influence of vegetation dynamics on anthropogenic climate change. Earth System Dynamics, 3(2), 233–243, doi: 10.5194/esd-3-233-2012.

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Reick, C.H., T. Raddatz, V. Brovkin, and V. Gayler, 2013: Representation of natural and anthropogenic land cover change in MPI-ESM. Journal of Advances in Modeling Earth Systems, 5(3), 459–482, doi: 10.1002/jame.20022.

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Smith, D.M. et al., 2016: Role of volcanic and anthropogenic aerosols in the recent global surface warming slowdown. Nature Climate Change, 6(10), 936–940, doi: 10.1038/nclimate3058.

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Toll, V., M. Christensen, J. Quaas, and N. Bellouin, 2019: Weak average liquid-cloud-water response to anthropogenic aerosols. Nature, 572(7767), 51–55, doi: 10.1038/s41586-019-1423-9.

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Vecchi, G.A. et al., 2006: Weakening of tropical Pacific atmospheric circulation due to anthropogenic forcing. Nature, 441(1), 73–76, doi: 10.1038/nature04744.

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Zhang, H., S. Zhao, Z. Wang, X. Zhang, and L. Song, 2016: The updated effective radiative forcing of major anthropogenic aerosols and their effects on global climate at present and in the future. International Journal of Climatology, 36(12), 4029–4044, doi: 10.1002/joc.4613.

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Zhou, C., H. Zhang, S. Zhao, and J. Li, 2017c: Simulated effects of internal mixing of anthropogenic aerosols on the aerosol–radiation interaction and global temperature. International Journal of Climatology, 37, 972–986, doi: 10.1002/joc.5050.

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Zickfeld, K., S. Solomon, and D.M. Gilford, 2017: Centuries of thermal sea-level rise due to anthropogenic emissions of short-lived greenhouse gases. Proceedings of the National Academy of Sciences, 114(4), 657–662, doi: 10.1073/pnas.1612066114.

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the snowpack). Land-water storage changes caused by climate variations may be indirectly affected by anthropogenic influences. It is difficult to assign a single confidence level to land-water storage as understanding can vary from low confidence in groundwater recharge processes to high confidence in water storage via snowpack changes (Sections 8.2.3 and 8.3.1.7).

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Extreme sea level is an exceptionally low or high local sea surface height arising from combined short-term phenomena (e.g., storm surges, tides and waves). RSL changes affect extreme sea levels directly by shifting the mean water levels, and indirectly by modulating the depth for propagation of tides, waves and/or surges. Extreme sea levels can be influenced by changes in the frequency, tracks, or strength of weather systems, or anthropogenic changes such as dredging. Extreme still water level refers to the combined contribution of RSL change, tides and storm surges. Wind-generated waves also contribute to coastal sea level. Extreme total water level is the extreme still water level plus wave setup (time-mean sea level elevation due to wave energy dissipation). When considering coastal impacts, swash (vertical displacement up the shore-face induced by individual waves) is also important and included in Extreme coastal water level. There is low to medium confidence in the understanding of extreme sea level processes (Sections 9.6.4 and 12.4).

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Air–sea fluxes of energy, freshwater, and momentum (wind stresses) are difficult to observe directly (Cronin et al., 2019), so estimates of the global mean net air–sea heat flux are inferred from observed ocean warming (Section 2.3.3.1, Box 7.2, and Cross-Chapter Box 9.1). Air–sea heat fluxes resemble the warming patterns of CMIP3 (Domingues et al., 2008; Levitus et al., 2012) and are consistent with the ensemble mean warming rate of CMIP5 (Cheng et al., 2017, 2019) and CMIP6 models (Section 3.5.1.3). Regional air–sea fluxes in models remain a key driver of uncertainty (Huber and Zanna, 2017; Tsujino et al., 2020). A substantial part of the upper 700 m energy increase is very likely attributed to anthropogenic forcing via increasing radiative forcing (Sections 3.5.1.3, 7.2 and 7.3).

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There is low confidence in long-term wind stress trends in most regions, but a few locations have likely trends over the scatterometer era and in projections, as shown in Figure 9.4 (Desbiolles et al., 2017; Young and Ribal, 2019; Yu, 2019). The AR5 (Rhein et al., 2013) assessed with medium confidence that zonal wind stress over the Southern Ocean increased from the early 1980s to the 1990s (medium confidence) (Figure 9.4). Over 1995–2014, the zonal wind stress over the Southern Ocean continued to increase, westerly winds in the North Pacific and North Atlantic weakened, while the easterly equatorial Pacific winds of the Walker circulation strengthened (Figure 9.4). In historical simulations, CMIP5 models projected annular modes (Annex IV) to move poleward and strengthen in both hemispheres (Yang et al., 2016), while in CMIP6 models westerlies only strengthen over the Southern Ocean, with a weaker trend than recently observed (Figure 9.4 and Sections 4.5.1 and 4.5.3). In the tropical Pacific Ocean, a weakening trend in easterly winds and Walker circulation in the 20th century has been inferred based on observed sea level pressure data (Vecchi et al., 2006; Vecchi and Soden, 2007) and coral proxies (Carilli et al., 2014) and is projected to continue by CMIP6 models (Figure 9.4). Yet, over 1995–2014 observed winds have strengthened (Figure 9.4). The observed strengthening may have been influenced by a combination of factors (Section 7.4.4.2.1), but there is low confidence in the attribution of this signal to anthropogenic warming (Section 3.3.3.1) and medium confidence that it reflects internal variability (Section 8.3.2.3). Near-term projected changes over the Southern Ocean result from ozone recovery and greenhouse gases (Sections 4.3.3 and 4.4.3). Overall, there is only low confidence in observed and projected wind stress trends in most regions because trends in oceanic wind stresses during the satellite era have not emerged or are inconsistent with historical simulated changes.

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The SROCC reported with high confidence that MHWs – defined as days exceeding the 99th percentile in sea surface temperature (SST) from 1982 to 2016 – have very likely doubled in frequency between 1982 and 2016. Additional observation-based evidence and acquisition of longer observation time series since SROCC have confirmed and expanded on this assessment: since the 1980s MHWs have also become more intense and longer (Frölicher and Laufkötter, 2018; Smale et al., 2019; Laufkötter et al., 2020). Satellite observations and reanalyses of SST show an increase in intensity of 0.04°C per decade from 1982 to 2016, an increase in spatial extent of 19% per decade from 1982 to 2016, and an increase in annual MHW days of 54% between the 1987–2016 period compared to 1925–1954 (Frölicher et al., 2018; Oliver, 2019). The SROCC assessed that 84–90% of all MHWs that occurred between 2006 and 2015 are very likely caused by anthropogenic warming. There is new evidence since SROCC that the frequency of the most impactful marine heatwaves over the last few decades has increased more than 20-fold because of anthropogenic global warming (Laufkötter et al., 2020). In summary, there is high confidence that MHWs have increased in frequency over the 20th century, with an approximate doubling from 1982 to 2016, and medium confidence that they have become more intense and longer since the 1980s.

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Ocean warming – that is, increasing ocean heat content (OHC) – is an important aspect of energy on Earth: SROCC (Bindoff et al., 2019) reported that there is high confidence that ocean warming during 1971–2010 dominated the increase in the Earth’s energy inventory, which is confirmed by the Box 7.2 assessment that the ocean has stored 91% of the total energy gained from 1971 to 2018. As reported in Sections 2.3.3.1, 3.5.1.3 and 7.2.2.2, Box 7.2 and Cross-Chapter Box 9.1, confidence in the assessment of global OHC change since 1971 is strengthened compared to previous reports, and extended backward to include likely warming since 1871. Table 7.1 updates the estimates of total ocean heat gains from 1971 to 2018, 1993 to 2018 and 2006 to 2018. Section 3.5.1.3 assesses that it is extremely likely that anthropogenic forcing was the main driver of the OHC increase over the historical period. Section 2.3.3.1 reports that current multi-decadal to centennial rates of OHC gain are greater than at any point since the last deglaciation (medium confidence).

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The rate of ocean warming varies regionally, with some regions having experienced slight cooling (Figure 9.6). The SROCC (Bindoff et al., 2019) assessed that ocean warming in the 0–700 m depth is globally widespread, with slower than global average warming in the subpolar North Atlantic. The SROCC (Meredith et al., 2019) also estimated that the Southern Ocean accounted for around 75% of global ocean heat uptake during 1870–1995 and that 35–43% of the upper 2000 m global ocean warming occurred in the Southern Ocean over 1970–2017 (45–62% for 2005–2017). The SROCC noted that this interhemispheric asymmetry might (at least partially) be explained by high concentrations of aerosols in the Northern Hemisphere. Here, we confirm these assessments, bring new evidence attributing these regional trends, and discuss the role of decadal ocean circulation variability in redistributing heat, driving interhemispheric asymmetry of the recent rate of ocean warming (Rathore et al., 2020; L. Wang et al., 2021). Since SROCC, one new study shows that the subpolar North Atlantic ‘warming hole’ observed since the 1980s has emerged from internal climate variability and can be attributed to greenhouse gas emissions (Chemke et al., 2020). A new analysis of a suite of climate models (Hobbs et al., 2021) confirms SROCC assessment, based on one paper (Swart et al., 2018), attributing the observed Southern Ocean warming to anthropogenic forcing. Given the large fraction of global ocean warming in the Southern Ocean and the sparse observations there before 2005, there is limited evidence that global OHC increase since 1971 might have been underestimated (Cheng and Zhu, 2014; Durack et al., 2014). Cross-Chapter Box 9.1 accounts for an increased error before 2005 in global OHC change. In summary, in the upper 2000 m since the 1970s, the subpolar North Atlantic has been slowly warming, and the Southern Ocean has stored a disproportionally large amount of anthropogenic heat (medium confidence).

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While heat redistribution reflects changes in ocean circulation and is a useful concept to understand the underlying processes driving OHC patterns, change in ocean heat transport (OHT) arises due to changes in ocean circulation and ocean temperature and affects regional OHC change. The AR5 did not assess change in OHT and SROCC (Meredith et al., 2019) only assessed projected OHT increases into the Nordic Seas and the Arctic Ocean. New evidence of increasing northward OHT into the Arctic has been observed in recent decades (Muilwijk et al., 2018; Q. Wang et al., 2019; Tsubouchi et al., 2021), similar to SROCC assessment, and consistent with observed increase in OHC in the ice-free Arctic ocean (Mayer et al., 2019). It is estimated that an increase of 0.021 PW of OHT occurred after 2001 into the Arctic, which is sufficient to account for the recent OHC change in the northern seas (Tsubouchi et al., 2021). However, these trends cannot yet be attributed to anthropogenic forcing due to potential internal variability (Muilwijk et al., 2018; Wang et al., 2019). New evidence strengthens the case that El Niño–Southern Oscillation (ENSO) and the Northern Annular Mode affect interannual OHT variability (Trenberth et al., 2019) and shows that a slowing AMOC reduces northward OHT in the Atlantic at 26.5°N (Section 9.2.3.1 and Figure 9.8; Bryden et al., 2020). Despite a decrease of AMOC northward heat (0.17 PW) and mass (2.5 Sverdrup (Sv); 1 Sv = 109kg s–1) transport, OHT has increased toward the Arctic through increased upper northern North Atlantic temperatures and stronger wind-driven gyres (medium confidence) (Section 9.2.3.4 and Figure 9.11; Singh et al., 2017; Oldenburg et al., 2018). In summary, OHT has increased toward the Arctic in recent decades, which at least partially explains the recent OHC change in the Arctic (medium confidence).

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The SROCC assessed that the warming of the deep ocean is slow to manifest, with multi-century or longer response times, so global OHC (and global mean thermosteric sea level) will continue to rise for centuries (Figures 9.9 and 9.30). New studies show that this continuation persists, even after cessation of greenhouse gas emissions (Ehlert and Zickfeld, 2018). Ocean warming will continue, even after emissions reach zero because of slow ocean circulation (Larson et al., 2020). OHC will increase until at least 2300, even for low-emissions scenarios, but with a scenario-dependent rate (Nauels et al., 2017; Palmer et al., 2018) and depends on cumulative CO2 emissions, as well as the time profile of emissions (Bouttes et al., 2013). Past long-term changes in total OHC illustrate adjustment relevant to expected future changes (Figure 9.9). Observational data from ice core rare gas elemental and isotopic ratios document a rise in global OHC relative to the Last Glacial Maximum of >17,000 ZJ (change in mean ocean temperature >3.1°C; 1 ZJ = 1021Joules) (Figure 9.9; Bereiter et al., 2018; Baggenstos et al., 2019; Shackleton et al., 2019, 2020). This temperature increase is significantly larger than the modelled OHC changes associated with collapse of AMOC alone, and tracks rising Southern Ocean SST (Uemura et al., 2018), strengthening of the deep abyssal overturning cell (Du et al., 2020) and increased North Atlantic water in the Southern Ocean (Wilson et al., 2020). This underscores the importance of Antarctic abyssal ventilation on long-term oceanic heat budgets (Section 9.2.3.2). An ensemble of four intermediate-complexity models project 10,000-year future responses to CO2 emissions (Clark et al., 2016) with SST change peaking around 2300 and a varying scenario-dependent magnitude approaching the scale of glacial-to-interglacial changes in paleodata (Figure 9.9). Long-term OHC commitments relative to 1850–1900 conditions are 2.6, 9.7, 15.2, 21.6, and 28.0 YJ (with mean ocean temperature change as much as 5.1°C) for emissions of 0, 1280, 2560, and 3840 and 5120 Gt after 2000 CE respectively, with OHC peaking near 4000 CE, reflecting whole-ocean warming lagging SST by thousands of years. The exact timing is uncertain, subject to rates of high-latitude meltwater input (Van Breedam et al., 2020) and circulation time (Gebbie and Huybers, 2019). In summary, there is very high confidence that there is a long-term commitment to increased OHC in response to anthropogenic CO2 emissions, which is essentially irreversible on human time scales.

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Subtropical mode waters (STMW) ventilate the main thermocline of the ocean at mid- to low-latitudes and have circulation time scales away from the surface of the order of years to decades. The SROCC (Bindoff et al., 2019) reported that warming in the subtropical gyres penetrates deeper than in other gyres, following the density surfaces in these gyres. Consistently, we assess that STMW have deepened worldwide, with greatest deepening in the Southern Hemisphere (high confidence) (Häkkinen et al., 2016; Desbruyères et al., 2017). Subsurface warming in the Northern Hemisphere STMW is larger than at the surface (Sugimoto et al., 2017) because they are formed in winter western boundary current extensions, where surface warming is larger than the global average (Section 9.2.1.1). Variability in STMW thickness or temperature has a large imprint on OHC (Section 9.2.2.1; Kolodziejczyk et al., 2019). STMW are observed to be freshening in the North Pacific and associated with increased salinity in the North Atlantic (Oka et al., 2017; Silvy et al., 2020), with large decadal variability (Oka et al., 2019; Wu et al., 2020). Anthropogenic temperature and salinity changes in the STMW layer are projected to intensify in the future, with emergence from natural variability around 2020 to 2040 (Silvy et al., 2020).

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Atlantic Meridional Overturning Circulation (AMOC) is the main overturning current system in the South and North Atlantic oceans. It transports warm upper-ocean water northwards, and cold, deep water southwards, as part of the global ocean circulation system (Section 2.3.3.4.1). Changes in AMOC influence global ocean heat content (OHC) and transport (Section 9.2.2.1); global ocean anthropogenic carbon uptake changes and climate sensitivity (Cross-Chapter Box 5.3); and dynamical sea level change (Section 9.2.4). Since AR5/SROCC, confidence in modelled and reconstructed AMOC has decreased due to new observations and model disagreement. Confidence levels have been revisited in modelled AMOC evolution during the 20th century, the magnitude of 21st-century AMOC decline, and the possibility of an abrupt collapse before 2100.

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The AMOC is a potential driver of Atlantic Multi-decadal Variability (AMV), but there is new evidence that anthropogenic aerosol changes have contributed to observed AMV changes, and that underestimation of the magnitude and duration of AMV changes in CMIP5 is tempered in CMIP6 (Section 3.7.7 and Annex IV.2.7). Comparison of observed AMOC variability at the RAPID section with modelled variability reveals that CMIP5 models appear to largely underestimate the interannual and decadal time scale variability (Roberts et al., 2014; Yan et al., 2018), and similar results are found when analysing CMIP6 models (Section 3.5.4.1). By underestimating the multi-decadal AMOC–AMV link and other low-frequency AMOC variability, climate models also underestimate internal variability in subpolar SSTs that feed back on the North Atlantic Oscillation (NAO). This causes the NAO to lack variability on multi-decadal time scales (Kim et al., 2018). Despite the role of the AMOC in generating AMV through subsurface temperatures in antiphase with SST and downward heat fluxes into the ocean that anticorrelate with SSTs (R. Zhang et al., 2019), it is generally accepted that AMOC forcing of SST variability exists alongside stochastic wind forcing and external forcing by aerosols (Bellomo et al., 2018; Haustein et al., 2019; O’Reilly et al., 2019; Wills et al., 2019).

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The SROCC (Collins et al., 2019) assessed that in situ observations (2004–2017) and sea surface temperature reconstructions indicate that AMOC has weakened relative to 1850–1900 (medium confidence). However, SROCC also assessed that there is insufficient data to quantify the magnitude of the weakening, or to properly attribute it to anthropogenic forcing, due to the limited length of the observational record. Here, this assessment is adjusted to low confidence in the weakening (as also discussed in Sections 2.3.3.4.1 and 3.5.4.1). The CMIP5 multi-model mean showed no 20th century trend in AMOC (Cheng et al., 2013). The CMIP6 multi-model mean slightly opposes the reconstructed decline due to a strong increase in the 1940–1985 period (Menary et al., 2020b; Weijer et al., 2020), thought to be in response to aerosol forcing (Section 3.5.4.1), followed by a smaller decline since the 1990s. Also, agreement between different proxy-based reconstructions is weak in many details (Moffa-Sánchez et al., 2019) and questions can be raised regarding various proxies used in reconstructions (Section 2.3.3.4.1). For instance, SST-based proxies can be influenced by atmospheric and other processes acting on different time scales (Moffa-Sánchez et al., 2019; Jackson and Wood, 2020). In addition, many proxies are indirect and based on AMOC-related processes assumed to be similar to those found in models, such as the link between AMOC and Labrador Sea convection, which has been questioned recently (see above). In addition, the subpolar gyre from which many AMOC proxies are taken may vary independently of AMOC, with similar patterns in SST and OHC driven by wind variability (Williams et al., 2014; Piecuch et al., 2017). Finally, a new dynamic reconstruction of the Atlantic inflow to the Nordic Seas suggests no slowdown over the past 70 to 100 years (Rossby et al., 2020), in contrast to a new compilation of proxy reconstructions which suggests that AMOC is presently in its weakest state in the last millennium (Caesar et al., 2021), reinforcing the evidence that motivated the previous SROCC assessment. Section 3.5.4.1 also questions the veracity of the models’ forced AMOC response during the 20th century. Given the large discrepancy between modelled and reconstructed AMOC in the 20th century, and the uncertainty over the realism of the 20th century modelled AMOC response (Section 3.5.4.1), we have low confidence in both.

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The changing Southern Ocean circulation system exerts a strong influence on the global climate by modulating: (i) global OHC (Section 9.2.2.1); (ii) global ocean anthropogenic carbon uptake (Cross-chapter Box 5.3); global ocean overturning circulation (Section 9.2.3.1); (iii) climate sensitivity (Section 7.4.4 and Cross-chapter Box 5.3); (iv) sea level through basal melt of ice shelves (9.4.2); and (v) Southern Hemisphere sea ice cover (Section 9.3.2).

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Although the observed wind stress curl shows systematic poleward shift in each basin as a result of anthropogenic warming (Section 2.3.1.4; Chen and Wu, 2012; Wu et al., 2012; Zhai et al., 2014), which has caused a systematic shift of the WBCs and subtropical gyres since 1993 (Wu et al., 2012; Yang et al., 2016, 2020), the response of current strength is more complex and inconsistent across regions (Sloyan and O’Kane, 2015; Y.-L. Wang et al., 2016; Elipot and Beal, 2018; McCarthy et al., 2018; Wang and Wu, 2018; Dong et al., 2019). The strength of WBCs and gyres exhibit inconsistent responses because they are dependent on wind stress forcing and because multi-scale interaction and air–sea interaction have an important role in their long-term trends and variability (Zhang et al., 2020). Observed changes in gyre circulation are dominated by interannual and decadal modes of variability globally (Qiu and Chen, 2012; Melzer and Subrahmanyam, 2017; McCarthy et al., 2018; Hu et al., 2020). The North Atlantic subpolar gyre is strongly modulated by variability associated with the NAO and AMV (Annex IV; Robson et al., 2016). Subpolar gyre systems can change abruptly due to a positive feedback between convective mixing and salinity transport (Born et al., 2013, 2016) and air–sea interaction (Moffa-Sánchez et al., 2014; Moreno-Chamarro et al., 2017) within the gyre. In the Arctic, both the Beaufort gyre and mesoscale eddies strengthened between 2003 and 2014 (Armitage et al., 2017), which might be partly due to increased wind stress (Oldenburg et al., 2018) or reduced sea ice thickness and changes in sea ice pack morphology (van der Linden et al., 2019). Presently, there is limited evidence in attributing causality to these changes for any of the proposed mechanisms. In the North Pacific, there has been an increasing trend in the Alaska Gyre from 1993 to 2017 (Cummins and Masson, 2018), which might be attributed to Pacific Decadal Oscillation (low confidence) (Hristova et al., 2019). In the Southern Ocean, limited evidence indicates that the subpolar gyres respond to Southern Hemisphere atmospheric modes of variability at interannual time scale (Armitage et al., 2018; Dotto et al., 2018).

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Eastern boundary upwelling systems (EBUS) exist where trade winds draw cold and generally low-pH/low-oxygen waters upward. Coastal upwelling plays a key role in supplying the food chain with nutrients, hence the richness and productivity of EBUS (Bindoff et al., 2019). The SROCC (Bindoff et al., 2019) assessed with high confidence that three out of the four major EBUS have experienced large-scale wind intensification in the past 60 years (only the trend for the Canary Current is considered uncertain). However, it also emphasized that various processes can also modulate, or even reverse, wind trends locally (Bindoff et al., 2019). Here we revisit SROCC assessment (Bindoff et al., 2019) based on evidence showing low agreement between studies that have investigated trends over past decadess of upwelling-favourable winds (Varela et al., 2015). This low agreement has been related to differences in wind products, season of interest, and length of the considered time series (Varela et al., 2015). Based on this, we assess that only the California Current system has experienced large-scale upwelling-favorable wind intensification over the period 1982–2010, albeit with regional differences (García-Reyes and Largier, 2010; Seo et al., 2012). In the Benguela, Canary, and Humboldt systems, large-scale, upwelling-favourable wind trends are ambiguous, owing to low confidence in long-term in situ marine wind data (Cardone et al., 1990; Bakun et al., 2010) and low agreement among available studies (Narayan et al., 2010; Sydeman et al., 2014; Varela et al., 2015). Our assessment confirms SROCC assessment (Bindoff et al., 2019) in that high natural variability of EBUS and their inadequate representation by most climate models gives low confidence in attribution of observed changes, while anthropogenic changes are projected to emerge primarily in the second half of the 21st century (limited evidence: one model and one study) (Brady et al., 2017).

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In summary, SROCC and this Report conclude that the California Current system has experienced some upwelling-favourable wind intensification since the 1980s (high confidence), while low agreement among reported wind changes in the Benguela, Canary, and Humboldt systems prevent a similar assessment. As in SROCC, there is low confidence in attribution of observed changes to anthropogenic or natural causes. New evidence reinforces our confidence in SROCC assessment that, under increased radiative forcing, EBUS winds will change with a dipole spatial pattern within each EBUS of reduction (weaker and/or shorter) at low latitude, and enhancement (stronger and/or longer) at high latitude (high confidence). There is medium confidence that, across all scenarios, upwelling wind changes in EBUS will remain moderate in the 21st century, within ±10–20% from present-day values.

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The observed fluctuations and trends of the Arctic sea ice cover arise from a combination of changes in natural external forcing and anthropogenic forcing, internal variability and internal feedbacks (e.g., Notz and Stroeve, 2018; Halloran et al., 2020). New paleo-proxy techniques indicate regional sea ice changes over epochs and millennia and allow possible drivers to be assessed. Biomarker IP25 (Belt et al., 2007) together with other sedimentary biomarkers (Belt, 2018) provide local temporal information on seasonal sea ice coverage, permanent sea ice coverage and ice-free waters, with occasional ambiguous contrasting results (Belt, 2019). These records and other proposed paleo proxies, including bromine in ice cores (Spolaor et al., 2016), dinocyst assemblages (e.g., De Vernal et al., 2013b) and driftwood (e.g., Funder et al., 2011), provide evidence of sea ice fluctuations that exceed internal variability (high confidence).

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The SROCC assessed that approximately half of the satellite-observed Arctic summer sea ice loss is driven by increased concentrations of atmospheric greenhouse gases (medium confidence). Recent attribution studies now allow the strengthened assessment that it is very likely that more than half of the observed Arctic sea ice loss in summer is anthropogenic (Section 3.4.1.1). This assessment is confirmed by process-based analyses of Arctic sea ice loss not assessed by SROCC. Similar to the paleorecord, the satellite record of Arctic sea ice area from 1979 onwards is strongly and linearly correlated with global mean temperature on decadal and longer time scales (Figures 9.14a,e) (e.g., Gregory et al., 2002; Rosenblum and Eisenman, 2017). The correlation holds across all months with R2 ranging from 0.61 to 0.81 (Niederdrenk and Notz, 2018). However, in contrast to paleorecords, sea ice fluctuations during the satellite period are only weakly correlated with Northern Hemisphere insolation (Notz and Marotzke, 2012); modern Northern Hemisphere sea ice area is more strongly correlated with atmospheric carbon dioxide (CO2) concentration (Johannessen, 2008; Notz and Marotzke, 2012) and cumulative anthropogenic CO2 emissions (Figures 9.14b,f; Zickfeld et al., 2012; Herrington and Zickfeld, 2014; Notz and Stroeve, 2016). The R2 values of the correlation between sea ice area and cumulative CO2 emissions range across all months from 0.76 to 0.92 (Stroeve and Notz, 2018). In summary, there is high confidence that satellite-observed Arctic sea ice area is strongly correlated with global mean temperature, CO2 concentration and cumulative anthropogenic CO2 emissions.

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In contrast, CMIP6 models capture the observed sensitivity of Arctic sea ice area to cumulative anthropogenic CO2 emissions well, providing high confidence that the Arctic Ocean will likely become practically sea ice free in the September mean for the first time for future CO2 emissions of less than 1000 Gt and before the year 2050 in all SSP scenarios (Notz and SIMIP Community, 2020). This new assessment is consistent with an observation-based projection of a practically sea ice-free Arctic Ocean in September for additional anthropogenic CO2 emissions of 800 ± 330 GtCO2 beyond the year 2018 (Notz and Stroeve, 2018; Stroeve and Notz, 2018). This estimate may, however, be too high due to neglecting possible future reduction in atmospheric aerosol load that would cause additional warming (Gagné et al., 2015a; Wang et al., 2018), and is subject to the same constraints as the carbon budget analysis for global mean temperature (see section 5.5 for details). Based on CMIP6 simulations, it is very likely that the Arctic Ocean will remain sea ice covered in winter in all scenarios throughout this century (Sections 4.3.2 and 4.4.2).

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There is low confidence in the attribution of the observed changes in Antarctic sea ice area (Section 3.4.1.2). Based on the available evidence, the lack of a negative trend of Antarctic sea ice area, despite substantial global warming in recent decades, has been attributed to internal variability in analyses of the observational record (Meier et al., 2013; Gallaher et al., 2014; Gagné et al., 2015b), reconstructions from early observations (Fan et al., 2014; Edinburgh and Day, 2016) and proxy data (Hobbs et al., 2016b) in model simulations (Turner et al., 2013; Zunz et al., 2013; L. Zhang et al., 2019). Nonetheless, without accurate simulations of observed changes, the possible contribution of anthropogenic forcing to the regional changes in sea ice area remains unclear (Hosking et al., 2013; Turner et al., 2013; Haumann et al., 2014; L. Zhang et al., 2019).

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The AR5 assessed that it is likely that anthropogenic forcing has contributed to the surface melting of Greenland since 1993 (Bindoff et al., 2013). Section 3.4.3.2 assesses that it is very likely that human influence has contributed to the observed surface melting of the Greenland Ice Sheet over the past two decades. There is medium confidence of an anthropogenic contribution to recent mass loss from Greenland.

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The SROCC reported limited evidence and medium agreement for anthropogenic forcing of the observed AIS mass balance changes. As stated in Section 3.4.3.2, there remains low confidence in attributing the causes of the observed mass of loss from the AIS since 1993, in spite of some additional process-based evidence to support attribution to anthropogenic forcing.

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(Section 3.4.3.1 assesses new attribution studies for glaciers and finds that human influence is very likely the main driver of the global, near-universal retreat of glaciers since the 1990s. The SROCC assessed that it is very likely that atmospheric warming is the primary driver for the global glacier recession. Since SROCC, a study of glaciers in New Zealand used event attribution to confirm a connection between extreme glacier mass loss years and anthropogenic warming (Vargo et al., 2020).

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There is a lack of formal studies attributing observed permafrost changes (thaw depth, thermal state) or associated landscape changes to anthropogenic forcing. However, the observed Arctic warming has been attributed to anthropogenic forcing (e.g., Najafi et al., 2015) and an obvious physical link exists between ground temperatures (and thus permafrost) and surface air temperatures. Therefore, physically consistent and convergent lines of evidence lead to medium confidence in anthropogenic forcing being the dominant cause of the observed pan-Arctic permafrost changes. Added to this, local permafrost change by soil and ecosystem disturbance is induced by increasing human industrial activities in the Arctic (e.g., Raynolds et al., 2014).

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As assessed in detail in Section 3.4.2, it is very likely that anthropogenic influence contributed to the observed reductions in Northern Hemisphere spring snow cover since the mid-20th century. The reasons for this assessment are: (i) physical consistency of the observed spring snowpack and surface temperature changes in observations and models; (ii) the strong observed hemispheric and regional spring SCE and SWE trends; and (iii) the general attribution of hemispheric temperature changes to human influence. Consistent between multiple observational products and historical climate model simulations, the observed NH SCE sensitivity to NH land (>30°N) warming (Mudryk et al., 2017) is approximately –1.9×106km2°C–1(95% confidence range of ±0.9×106km2°C–1) throughout the snow season.

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The sea level budget in SROCC included the anthropogenic contribution of land-water storage (LWS; Box 9.1) change from a single estimate (Wada, 2016). Since SROCC, two studies have combined estimates of natural LWS change with anthropogenic LWS changes from reservoir impoundment and groundwater depletion (Cáceres et al., 2020; Frederikse et al., 2020b). For Cáceres et al. (2020), zero change is assumed for the period 1901–1948, since their LWS change estimates are not available before 1948. Given the large year-to-year changes associated with hydrological variability, the assessed changes in LWS (Table 9.5) are based on linear trends for each period, following Palmer et al. (2021). Structural uncertainty is estimated from the standard deviation of the trends across the two studies, and parametric uncertainty is estimated based on the Monte Carlo simulations of Frederikse et al. (2020b). These two sources of uncertainty are combined in quadrature, and the assessed central estimate is taken as the average of the ensemble mean trends. Compared to SROCC-assessed LWS trend of -0.12 mm yr–1for the period 1901–1990, the updated assessment leads to a more negative trend of –0.16 [–0.35 to 0.04] mm yr–1, although the two are consistent within the estimated uncertainties. Previous studies and SROCC have highlighted the large uncertainty in estimates of LWS change over the 20th century (Gregory et al., 2013), and therefore SROCC assessment of low confidence in the estimated LWS contribution to GMSL change is maintained.

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The SROCC (Oppenheimer et al., 2019) attributed anthropogenic forcing to be the dominant cause of GMSL rise since 1970 (see also Section 3.5.3.2), but detection and attribution (Cross-Working Group Box: Attribution in Chapter 1) of 20th century externally forced regional sea level changes is more challenging, as regional variability is larger (Section 9.6.1.3), and therefore the signal-to-noise ratio is smaller (Richter and Marzeion, 2014; Monselesan et al., 2015; Palanisamy et al., 2015). Whereas SROCC assessed with high confidence that GMSL rise is attributable to anthropogenic greenhouse gas emissions, they assessed with medium confidence that the regional anomalies in ocean basins are a combination of the response to anthropogenic greenhouse gas emissions and internal variability.

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The simulated ocean dynamic and thermosteric response to external forcings during 1861–2005 is only larger than simulated internal variability in the Southern Ocean and North Pacific on a 1° grid (Slangen et al., 2015). However, on spatial scales exceeding 2000 km, a detectable signal is revealed in the last 45 years in 63% of the global ocean area (Richter et al., 2017). The thermosteric change in the upper 700 m in the period 1970–2005 shows similar observed and simulated forced geographical patterns, and anthropogenic forcing accounts for part (North Atlantic, 65%) or all (tropical Pacific, Southern Ocean) of the observed regional mean (Marcos and Amores, 2014). The influences of greenhouse gases and anthropogenic aerosols can be partially distinguished by considering geographical or vertical ocean temperature variations (Slangen et al., 2015; Bilbao et al., 2019; Fasullo et al., 2020). Zonal-mean forced ocean dynamic sea level change alone is not detectable but, using spatial correlation, the global geographical pattern during the altimeter period is detectable in sea level trends (Fasullo and Nerem, 2018). This patternmay already or will soon be detectable in individual years, based on an analysis of CMIP5 climate model simulations (Bilbao et al., 2015). Anthropogenic forcing, dominated by greenhouse gases, has strengthened the meridional sea level gradient in the Southern Ocean since the 1960s (Slangen et al., 2015; Bilbao et al., 2019; Fasullo et al., 2020). New evidence finds that observed zonal-mean total sea level trends during 1993–2018 in all basins are inconsistent with unforced variability alone, but are consistent with the modelled response to external forcing (Richter et al., 2020).

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A region that has been studied intensely in the context of sea level detection and attribution is the tropical Pacific. Observed sea level trends in the tropical Pacific show a PDO-like (Annex IV) east–west dipole (with a greater rate of rise in the west, see Section 9.6.1.3). This dipole does not occur in CMIP5 simulations with the magnitude and duration that was observed in the 1990s and 2000s, neither in response to historical forcing, nor as internal variability after removing the variability associated with the PDO (Bilbao et al., 2015). Hamlington et al. (2014) did obtain a residual trend pattern for 1993–2010 in the tropical Pacific that may link to anthropogenic warming of the tropical Indian Ocean. Allowing for PDO and ENSO variations, (Royston et al., 2018) describe patches of the Pacific Ocean where the sea level trend for 1993–2015 is distinguishable from temporally correlated noise. The acceleration in eastern Pacific sea level rise is largely accounted for by variations resembling PDO and ENSO (Hamlington et al., 2020a).

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In the future, the anthropogenic signal in regional sea level change from ocean density and dynamics is projected to emerge first in regions with relatively small internal variability, such as the tropical Atlantic Ocean and the tropical Indian Ocean (Jordà, 2014; Lyuet al., 2014; Richter and Marzeion, 2014; Bilbao et al., 2015). The signal is projected to emerge over 50% of the ocean area by the 2040s (Lyu et al., 2014), but in regions where variability is large and projected changes are small, such as the Southern Ocean, the signal will not emerge before late in the century. Adding the projected sea level change from land ice mass loss and groundwater extraction strengthens and modifies the forced signal, leading to times of emergence 10 to 20 years earlier in most parts of the ocean, except in regions close to sources of mass loss, with emergence over 50% of the ocean area by 2020, and nearly everywhere by 2100 (medium confidence) (Lyu et al., 2014; Richter et al., 2017).

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As noted by SROCC, VLM from sources other than GIA – including tectonics and mantle dynamic topography, volcanism, compaction, and anthropogenic subsidence – can be locally important, producing VLM rates comparable to or greater than rates of GMSL change. Complete global projections of these processes are not available because of the small spatial scales, the sensitivity of subsidence to local human activities, and the stochasticity of tectonics (Wöppelmann and Marcos, 2016; Oppenheimer et al., 2019). Therefore, integrated RSL projections to date have either included only the component of VLM associated with GIA (as in AR5 and SROCC), or used a constant long-term background rate of change (including both GIA and other long-term drivers of VLM) estimated from historical tide gauge trends (e.g., Kopp et al., 2014). The updated projections use the second approach and extrapolate the field of long-term background rates of RSL change, including long-term VLM derived from tide gauges, to global coverage using a spatio-temporal statistical approach (Supplementary Material 9.SM.4.6; Kopp et al., 2014). The combined GIA and long-term VLM is assumed to be scenario independent and constant over the projected period. In areas where rapid subsidence occurs in a cluster of tide gauges (e.g., the western Gulf of Mexico), the associated rates are interpolated between the tide gauges. In areas where the available tide gauges exhibit large, tectonically driven VLM that changes considerably in rate over short distances (e.g., Alaska and the Bering Strait), a sizable uncertainty propagates into the RSL projections (Figure 9.26). Rates of RSL rise are likely to be underestimated due to subsidence in shallow strata that are not recorded by tide gauges (Keogh and Törnqvist, 2019) and in some locations may therefore be minimum values, especially if anomalously high subsidence rates associated with fluid extraction are also considered (e.g., Minderhoud et al., 2017). Therefore, depending on location, there is low to medium confidence in the GIA and VLM projections employed in this Report. In many regions, higher-fidelity projections would require more detailed regional analysis.

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The AR5 indicated that the amplitude and phase of major tidal constituents have exhibited long-term change, but that their effects on ESL were not well understood. The SROCC (Bindoff et al., 2019) reported changes in tides (amplification and dampening) at some locations to be of comparable importance to changes in mean sea level for explaining changes in high water levels, with the sign of change being dependent on stability of shoreline position. RSL rise causes water depth-based alterations to the resonant characteristics of the basin, changes the bottom friction and increases the wave speed (Pickering et al., 2012) and remains the primary hypothesis for observed tidal changes. Other contributing processes include strong localized anthropogenic drivers (e.g., port development, dredging, flood defences, land reclamation), changes in stratification associated with ocean warming (Section 9.2.1.3), and changes in seabed roughness associated with ecological change (e.g., Haigh et al., 2019). Tide gauge data show that, although principal tidal components have varied in amplitude on the order of 2% to 10% per century (Jay, 2009; Ray, 2009), identifying direct causality remains challenging (Haigh et al., 2019). Combined, observations and models indicate RSL rise and direct anthropogenic factors are the primary drivers of observed tidal changes at tide gauge stations (medium confidence).

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Only a few studies have attempted to quantify the role of anthropogenic climate change in ESL events (e.g., Mori et al., 2014; Takayabu et al., 2015; Turki et al., 2019). Detection and attribution of the human influence on climatic changes in surges, and waves remains a challenge (Ceres et al., 2017), with limited evidence to suggest in some instances – for example, poleward migration of tropical cyclones in the Western North Pacific (Section 11.7.1.2), changes in surges and waves can be attributed to anthropogenic climate change (low confidence). With RSL change being considered the primary driver of observed tidal changes, there is medium confidence that these changes can be attributed to human influence. The close relationship between local ESL and long-term RSL change, combined with the robust attribution of GMSL change (Section 9.6.1.4), implies that observed global changes in ESL can be attributed, at least in part, to human-caused climate change (medium confidence), but reconciling regional variation in these changes is not yet possible (Section 9.6.1.4).

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The SROCC (Bindoff et al., 2019) concluded that the majority of coastal regions will experience statistically significant changes in tidal amplitudes through the 21st century. Comprehensive high-resolution (of the order 10 km) numerical modelling studies provide evidence for spatially coherent changes in tidal amplitudes in shelf seas as a result of RSL rise (Haigh et al., 2019, and references therein). There is high confidence that GMSL rise will be the primary driver of global tidal amplitude increases and decreases over the next 100–200 years, changing the baseline tide that ESLs are imposed on. At local and regional scales, anthropogenic factors such as major land reclamation efforts, as in the East China Sea (Song et al., 2013) or differing national coastal management strategies (maintaining the present coastline position or managed retreat) will locally modulate the influence of GMSL rise on tidal amplitude (medium confidence).

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Laliberté, F., S.E.L. Howell, J.-F. Lemieux, F. Dupont, and J. Lei, 2018: What historical landfast ice observations tell us about projected ice conditions in Arctic archipelagoes and marginal seas under anthropogenic forcing. The Cryosphere, 12(11), 3577–3588, doi: 10.5194/tc-12-3577-2018.

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Lyu, K., X. Zhang, J.A. Church, and Q. Wu, 2020b: Processes Responsible for the Southern Hemisphere Ocean Heat Uptake and Redistribution under Anthropogenic Warming. Journal of Climate, 33(9), 3787–3807, doi: 10.1175/jcli-d-19-0478.1.

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Marcos, M. and A. Amores, 2014: Quantifying anthropogenic and natural contributions to thermosteric sea level rise. Geophysical Research Letters, 41(7), 2502–2507, doi: 10.1002/2014gl059766.

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Notz, D. and J. Stroeve, 2016: Observed Arctic sea-ice loss directly follows anthropogenic CO2 emission. Science, 354(6313), 747–750, doi: 10.1126/science.aag2345.

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Impacts of climate change are driven not only by changes in climate conditions, but also by changes in exposure and vulnerability (Cross-Chapter Box 1.3). This chapter concentrates on drivers of impacts that are of climatic origin (see also the IPCC Special Report on Global Warming of 1.5°C (SR1.5, IPCC, 2018), and Section 1.3.2 in this Report), referred to in WGI as ‘climatic impact-drivers’ (CIDs). CIDs are physical climate system conditions (e.g., means, events, extremes) that affect an element of society or ecosystems. Depending on system tolerance, CIDs and their changes can be detrimental, beneficial, neutral, or a mixture of each across interacting system elements and regions. However, this chapter largely focuses on drivers commonly connected to hazards, and adopts the IPCC risk framework (Cross-Chapter Box 1.3) since the main objective of the United Nations Framework Convention on Climate Change (UNFCCC) is to ‘prevent dangerous anthropogenic interference with the climate system’ (Article 2).

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Several climatic impact-drivers are reliant on many factors beyond their associated primary climatic phenomenon. For example, river flooding is heavily dependent on river management and engineering and could also be affected by tidal water levels due to sea level rise and/or storm surge. Coastal flooding could be affected by coastal protection structures, port and harbour structures, as well as river flows (on inlet-interrupted coasts). Coastal erosion could be influenced by coastal protection measures as well as fluvial sediment supply to the coast. Furthermore, air pollution weather is not the only or dominant driver, for instance, of surface ozone pollution, but precursor emissions from anthropogenic sources can play a significant role (Section 6.5). Chapter 12 focuses only on the influence of the atmospheric, land and oceanic conditions associated with the climatic impact-drivers and the confidence in the direction of CID changes given here does not take into account existing or potential future adaptation measures, unless otherwise stated.

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A global perspective on climatic impact-drivers is provided in Section 12.5.1. Section 12.5.2 focuses on assessing evidence for the emergence (Section 1.4.2.2) of an anthropogenic climate change signal on the change in CIDs beyond natural climate variability, based on the literature assessed in other chapters and additional literature, at both global and regional scales. The process of generating user-relevant regional climate information in the context of co-production and climate services is assessed in Sections 10.5, 12.6, Box 10.2 and Cross-Chapter Boxes 10.3 and 12.2. Cross-Chapter Box 12.1 provides a global perspective on climatic impact-drivers related to their evolution for different GWLs (Section 1.6).

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Although future air pollution will be strongly driven by air quality policies, anthropogenically-driven changes to temperature, humidity, precipitation and synoptic patterns have the potential to affect the emissions, production, concentration and transport of particulate matter (e.g., from dust, fires, pollen) and gaseous pollutants such as sulphur dioxide, tropospheric ozone and nitrogen dioxide (Section 6.5) with resulting impacts on human health, agriculture and ecosystems (Ren et al., 2011; Fiore et al., 2015; Kinney et al., 2015a; Tian et al., 2016; Orru et al., 2017; Emberson et al., 2018; Hayes et al., 2020). Information about conditions leading to poor air quality is also important for visibility in natural parks and tourist locations (Yue et al., 2013; Val Martin et al., 2015), as well as the efficiency of solar photovoltaic panels (Sweerts et al., 2019). Relevant information about conditions favouring air pollution includes tracking warmer conditions that accelerate ozone formation (Peel et al., 2013; Schnell et al., 2016) and the frequency and duration of stagnant air events (Horton et al., 2014; Fann et al., 2015; Lelieveld et al., 2015; Vautard et al., 2018), although no regional index has proven sufficient to capture regional changes or acute events (Kerr and Waugh, 2018; Schnell et al., 2018). By contrast, precipitation and moister air tend to reduce pollution (Section 6.5).

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Air pollution weather: The effect of climate change on air quality is assessed in Section 6.5 with limitations for local planning explained in Section 6.1.3, and only a brief summary is given here. Section 6.5 notes that climate change will have a small burden on particulate matter (PM) pollution (medium confidence) while the main controlling factor in determining future concentrations will be future emissions policy for PM and their precursors (high confidence). Surface ozone is sensitive to temperature and water vapour changes, but future levels depend on precursor emissions. Although there is low confidence in precise regional changes (Section 6.5), climate change will generally introduce a surface ozone (O3) penalty (increasing concentrations with increasing warming levels) over regions with high anthropogenic and/or natural ozone precursor emissions, while in less polluted regions higher temperatures and humidity favour destruction of ozone (Schnell et al., 2016). There is low confidence in changes to future stagnation events given the lack of robust projections of related atmospheric conditions, such as future atmospheric blocking events (Sections 3.3.3 and 8.4.2). The response of regional air pollution to climate change will also be affected by other CIDs like fire weather, as well as by ecosystem responses such as shifts in emissions by vegetation (Fiore et al., 2015). Section 6.5 assessed medium confidence that climate-driven changes to meteorological conditions generally favour extreme air pollution episodes in heavily polluted environments, but noted strong regional and metric dependencies. Given the dominant influence of future air quality policies, uncertainties around stagnation or blocking events, and the potential contrasting regional changes of conditions favouring ozone and PM formation, accumulation and destruction, cells in Tables 12.3–12.11 for air pollution weather are marked as low confidence, and the reader is referred to Section 6.5 for further details.

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Agricultural and ecological drought: Farmers and food security experts in East Africa have noted spatial extensions in seasonal agricultural droughts in recent decades (Elagib, 2014), but it is difficult to disentangle these trends from climate variability. In Ethiopia, past severe agricultural drought conditions in the northern regions are moderately common events in recent years (Zeleke et al., 2017). In Southern Africa, the number of ‘flash’ droughts (with rapid onset and durations from a few days to couple of months) have increased by 220% between 1961 and 2016 as a result of anthropogenic warming (Yuan et al., 2018). Section 11.9 notes medium confidence increases in agricultural and ecological drought trends in North, Western and Central Africa as well as both Southern Africa regions. The most striking drought is the Western Cape drought in 2015–2018, a prolonged drought that resulted in acute water shortages (Wolski, 2018; Burls et al., 2019; Section 10.6.2). Anthropogenic climate change caused a threefold increase in the probability of such a drought to occur (Chapters 10 and 11; Botai et al., 2017; Otto et al., 2018). Section 11.9 assesses increases in agricultural and ecological drought at 2°C GWL for North Africa and West Southern Africa (high confidence) and for East Southern Africa and Madagascar (medium confidence), with confidence generally rising for higher emissions scenarios (Sylla et al., 2016b; Zhao and Dai, 2017; Diedhiou et al., 2018; Abiodun et al., 2019; Todzo et al., 2020; Coppola et al., 2021b). Liu et al. (2018b) identified the Southern Africa region as the drought ‘hottest spot’ in Africa in 1.5°C and 2°C global warming scenarios.

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Changes to the frequency and intensity of dust storms also remain largely uncertain due to uncertainty in future regional wind and precipitation as the climate warms, CO2 fertilization effects on vegetation (Huang et al., 2017), and anthropogenic land use and land-cover change due to land management and invasive species (Ginoux et al., 2012; Webb and Pierre, 2018). Dust loadings and related air pollution hazards (from fine particles that affect health) are projected to generally decrease in many regions of the Sahara and Sahel due to the changing winds (Evan et al., 2016) and slightly increase over the Guinea coast and West Africa (low confidence) (Ji et al., 2018).

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Extreme heat: There is increased evidence and high confidence of more frequent heat extremes in the recent decades than in previous ones in most of Asia (Acar Deniz and Gönençgil, 2015; Rohini et al., 2016; Mishra et al., 2017; You et al., 2017; Imada et al., 2018; Khan et al., 2019b; Krishnan et al., 2019; Rahimi et al., 2019; Yin et al., 2019; Chapter 11) due to the effects of anthropogenic global warming, El Niño and urbanization (Luo and Lau, 2017; Thirumalai et al., 2017; Imada et al., 2019; Y. Sun et al., 2019; Zhou et al., 2019). But there is medium confidence of heat extremes increasing in frequency in many parts of India (Rohini et al., 2016; Mazdiyasni et al., 2017; van Oldenborgh et al., 2018; Sen Roy, 2019; Kumar et al., 2020) partly due to the alleviation of anthropogenic warming by increased air pollution with aerosols and expanding irrigation (van Oldenborgh et al., 2018; Thiery et al., 2020).

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Sand and dust storm: The Asia-Pacific region contributes 26.8 per cent to global dust emissions as of 2012 (UNESCAP, 2018). In West Asia, the frequency of dust events has increased markedly in some areas (east and north-east of Saudi Arabia, north-west of Iraq and east of Syria) from 1980 to the present (Nabavi et al., 2016; Alobaidi et al., 2017). This marked dust increase has been associated with drought conditions in the Fertile Crescent (Notaro et al., 2015; Yu et al., 2015), likely amplified by anthropogenic warming (Kelley et al., 2015; Chapter 10). Dust storm frequency in most regions of northern China show a decreasing trend since the 1960s due to the decrease in surface wind speed (medium confidence) (Guan et al., 2017).

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Extreme heat: The region has a very likely trend of increasing frequency and severity of hot extremes since the 1950s (Table 11.10). Extreme minimum temperatures have increased in all seasons over most of Australia and exceeds the increase in extreme maximum temperatures (X.L. Wang et al., 2013; Jakob and Walland, 2016). Heatwave characteristics and hot extremes have increased across many Australian regions since the mid-20th century (Table 11.10; CSIRO and BOM, 2020). The number of days per year with maximum temperature greater than 35°C has increased over most parts of Australia from 1957–2015, with the largest increasing trends of 0.4–1 days/year occurring in north-western, Northern, north-eastern Australia and parts of Central Australia (CSIRO and BOM, 2016). Long-term changes of hot extremes in Australia have been attributed to anthropogenic influence (Table 11.10). In New Zealand, the number of annual heatwave days increased at 18 of 30 sites during the period 1972–2019 (MfE and Stats NZ, 2020).

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Cold spell and frost: Excepting parts of Southern Australia, the Australasian region has a significant trend of decreasing frequency in cold extremes since the 1950s (high confidence) (Table 11.10) and there is high confidence that such trends are attributable to anthropogenic influence (Table 11.10). The number of frost days per year in Australia has on average declined at a rate of 0.15 days/decade in the past century (Alexander and Arblaster, 2017), except in some regions of Southern Australia, where an increase in both number and season length has been reported (Dittus et al., 2014; Crimp et al., 2016b). The number of frost days has decreased at 12 of 30 monitoring sites around New Zealand over the period 1972–2019 (MfE and Stats NZ, 2020).

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In terms of wet climatic impact-drivers, detectable anthropogenic increases in precipitation in Australia have been reported particularly for north-central Australia for the period 1901–2010 (Knutson and Zeng, 2018). Figure Atlas.11 indicates no significant trend in precipitation over the region during the baseline period 1960–2015, except for the Global Precipitation Climatology Project (GPCP) dataset, which shows an increasing trend in north-central Australia. In New Zealand, increases in annual rainfall have been observed between 1960–2019 in the south and west of the South Island and east of the North Island. Note however, for the most part, the above reported trends in New Zealand have been classified as statistically not significant (Figure Atlas.20).

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Aridity: In terms of dry climatic impact-drivers, a substantial decrease in precipitation has been observed across Southern Australia during the cool season (April–October) (medium confidence). The drying trend has been particularly strong over south-west Western Australia between May and July, with rainfall since 1970 being around 20% less than the 1900–1969 average (CSIRO and BOM, 2020). Detectable decreases in mean precipitation, attributable at least in part to anthropogenic forcing, have been reported for parts of south-west Australia (Delworth and Zeng, 2014; Knutson and Zeng, 2018), south-east Australia, and Tasmania (Knutson and Zeng, 2018). In New Zealand, the north-east of the South Island and western and the northern parts of the North Island show decreasing precipitation trends during 1960–2019 (MfE and Stats NZ, 2020).

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Fire weather: Dowdy and Pepler (2018) examined atmospheric conditions conducive to pyroconvection in the period 1979–2016, and found an increased risk in south-east Australia during spring and summer, due to changes in vertical atmospheric stability and humidity, in combination with adverse near-surface fire weather conditions. CSIRO and BOM (2018) and Dowdy (2018) found that the annual 90th percentile daily Forest Fire Danger Index (FFDI) has increased from 1950–2016 in parts of Australia, especially in Southern Australia (1–2.5 per decade) and in spring and summer. These studies indicate an increase in the frequency and magnitude of FFDI extreme quantiles, as well as a shift of the fire season start towards spring, lengthening the fire season. The unprecedented large fires of austral spring and summer of 2019 in south-east Australia were a result of extreme hot and dry weather in significantly drier than average conditions that had persisted since 2017, in combination with consistently stronger than average winds, resulting in above average to highest on record FFDI values in much of the country (Abram et al., 2021). These fires have been attributed to climate change through the temperature component of fire weather indices (van Oldenborgh et al., 2021). In New Zealand, days with very high and extreme fire weather increased in 12 out of 28 monitored sites, and decreased in 8, in the period 1997–2019 (MfE and Stats NZ, 2020). Attribution studies indicate that there is medium confidence of an anthropogenically driven past increase in fire weather conditions, essentially due to increase in frequency of extreme heat waves. (Hope et al., 2019; Lewis et al., 2020; van Oldenborgh et al., 2021).

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Fire weather indices are projected to increase in most of Australia (high confidence) and many parts of New Zealand (medium confidence), in particular with respect to extreme fire and induced pyroconvection (Dowdy et al., 2019b). Increasing mean temperature, cool season rainfall decline, and changes in tropical climate variability all contribute to a future increase in extreme fire risk in Australia (Abram et al., 2021). Projections indicate that the annual cumulative FFDI will increase by 31–33% in Southern and Eastern Australia, and by 17–25% in Northern Australia and the Rangelands by 2090 (relative to 1995) under RCP8.5 (CSIRO and BOM, 2015). Using a CMIP5 ensemble of 17 models, Abatzoglou et al. (2019) found a statistically significant positive trend for fire weather intensity and fire season length for future mid-century conditions under RCP8.5, including a detectable anthropogenic influence on fire risk magnitude and fire season length by 2040 in Western Australia and along the Queensland coastline. Using the C-Haines and FFDI indices with A2 and RCP8.5 respectively, Di Virgilio et al. (2019) and Clarke et al. (2019) have shown that extreme fire weather frequency will increase in south-eastern Australia by the end of the 21st century. Most of these projections indicate that the biggest increases in fire weather conditions will be in late spring, effectively resulting in longer (stronger) fire seasons in areas where spring is the shoulder (peak) season. In New Zealand, Watt et al. (2019) projected that the number of days with very high to extreme fire risk will increase by 71% by 2040, and by a further 12% by 2090, for the A1B scenario, with fire risk increase all along the east coast. The most marked relative changes by 2090 were projected for Wellington and Dunedin, where very high to extreme fire risk is projected to increase by, respectively, 89% to 32 days and 207% to 18 days, compared to the baseline period 1970–1999.

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River flood: Emerging literature in the region documents ongoing changes in river floods. Mernild et al. (2018) report decreases and increases in annual runoff west of the Andes Cordillera’s continental divide, with the greatest decreases in the number of low (<10th percentile) runoff conditions and the greatest increases in high (>90th percentile) runoff conditions. In coastal north-east Peru, extreme precipitation events recently caused devastating river floods and landslides (Son et al., 2020). In Brazil, floods are becoming more frequent and intense in wet regions but less frequent and intense in drier regions (Bartiko et al., 2019; Borges de Amorim and Chaffe, 2019), with higher propagation of hydrological changes through anthropogenically modified agricultural basins (Chagas and Chaffe, 2018). Record, catastrophic, unprecedented, and once-in-a-century flooding events have also been reported in recent decades in the tributaries of the Amazon River or along its mainstream (Sena et al., 2012; Espinoza et al., 2013; Marengo et al., 2013; Filizola et al., 2014), in Argentinean rural and urban areas (Barros et al., 2015), in the lower reaches of the Atrato, Cauca and Magdalena rivers in Colombia (Hoyos et al., 2013; Ávila et al., 2019), in basins whose mainstreams flow through important metropolitan areas such as Concepción, Chile (Rojas et al., 2017), and even in one of Earth’s driest regions, the Atacama Desert (Wilcox et al., 2016). In the Amazon basin, the significant increase in extreme flow is associated with the strengthening of the Walker circulation (Barichivich et al., 2018).

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Aridity: Several regional studies suggest increasing trends in the frequency and length of droughts in the region, such as: over NWS (Domínguez-Castro et al., 2018), NSA (Marengo and Espinoza, 2016; Cunha et al., 2019) and NES (Marengo and Bernasconi, 2015), over southern Amazonia (Fu et al., 2013; Boisier et al., 2015), in the São Francisco River basin and the capital city Distrito Federal in Brazil (Borges et al., 2018; Bezerra et al., 2019), in the southern Andes (Vera and Díaz, 2015), in central southern Chile (Boisier et al., 2018), in SES (Rivera and Penalba, 2014) and, during recent years, in SSA (Rivera and Penalba, 2014). Chapter 8 indicated medium confidence of anthropogenic forcing on observed drying trends in central Chile. Additional discussion on droughts and aridity trends in South America is presented in Sections 8.3.1.6, 8.4.1.6 and 8.6.2.1.

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Extreme heat: The frequency of heatwaves observed in Europe has very likely increased in recent decades due to human-induced change in atmospheric composition (Section 11.3) and a detectable anthropogenic increase in a summer heat stress index over all regions of Europe has been identified based on WBGT index trends for 1973–2012 (medium confidence, limited evidence) (Knutson and Ploshay, 2016).

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Aridity: The Mediterranean region shows evidence of large-scale decreasing precipitation trends over 1901–2010, which are at least partly attributable to anthropogenic forcing according to CMIP5 models (Knutson and Zeng, 2018). Nevertheless, there is low agreement among studies on observed precipitation trend in the Mediterranean region (Section 11.9.4 and Atlas.8.2).

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Hydrological drought: There is high confidence that hydrological droughts have increased in the Mediterranean basin with medium confidence in anthropogenic attribution of the signal, and high confidence that they will continue to increase through the 21st century for 2°C GWL and higher and all scenarios except RCP2.6/SSP1-2.6. (Sections 8.3.1.6, 8,4.1.6, and 11.9.4). There is medium confidence in hydrological drought increase in WCE and low confidence in direction of change for EEU and NEU from mid-century onwards and for 2°C GWL and higher and all scenarios except RCP2.6/SSP1-2.6 (Section 11.9 and Figure 12.4g–i).

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Agricultural and ecological drought: There is medium confidence that agricultural and ecologicaldroughts have increased in Western and Central Europe and in the Mediterranean region, and medium confidence that anthropogenic drivers contributed to the Mediterranean increase (Sections 8.3.1.6 and 11.9).

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Fire weather: Fire weather conditions have been increasing since about 1980 over a few regions in Europe including Mediterranean areas (low confidence) (Venäläinen et al., 2014; Urbieta et al., 2019; Barbero et al., 2020; Giannaros et al., 2021). However, beyond a few studies, evidence is largely missing on attribution of these trends to anthropogenic climate change (Forzieri et al., 2016). An increase in fire weather is projected for most of Europe, especially western, eastern and central regions, by 2080 (current 100-year events will occur every 5–50 years), with a progressive increase in confidence and model agreement along the 21st century (medium confidence) (Forzieri et al., 2016; Abatzoglou et al., 2019). With increased drying and heat combined, in Mediterranean areas, an increase in fire weather indices is projected under RCP4.5 and RCP8.5, or SRES A1B, as early as by mid-century (high confidence) (Bedia et al., 2014; Abatzoglou et al., 2019; Dupuy et al., 2020; Fargeon et al., 2020; Ruffault et al., 2020) and an increase in burned area of 40% and 100% for a 2°C and 3°C GWL, respectively (Turco et al., 2018).

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Lake, river and sea ice: Anthropogenic warming reduces the seasonal extent of lake and river ice over many North American freshwater systems, with ice-free winter conditions pushing further north with rising temperatures (high confidence). Observations in Central and Eastern North America show reduced average seasonal lake-ice cover duration (Benson et al., 2012; Mason et al., 2016; US EPA, 2016). Satellite observations show declines in lake ice (Du et al., 2017) and loss of more than 20% of winter river-ice length in much of Alaska (2008–2018 compared to 1984–1994; Yang et al., 2020a). Spring lake and river ice in Canada is projected to break up 10–25 days earlier while autumn freeze-up occurs 5–15 days later by mid-century, with larger declines in lake-ice season closer to the coasts (Dibike et al., 2012) and for rivers in the Rocky Mountains and north-eastern USA (Yang et al., 2020a), although global models have difficulty with frozen freshwater system dynamics (Derksen et al., 2018). Substantial ice loss is projected over the Laurentian Great Lakes (Hewer and Gough, 2019; Matsumoto et al., 2019). The southern extent of lakes experiencing intermittent winter ice cover moves northward with rising temperature, pushing nearly out of the continental USA at low elevations under a 4.5°C GWL (Sharma et al., 2019). Higher spring flows and the potential for winter thaws are also projected to heighten the threat of ice jams (Rokaya et al., 2018; Bonsal et al., 2019) while reducing the seasonal viability of ice roads and recreational use (Pendakur, 2016; Mullan et al., 2017; Knoll et al., 2019).

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Mean air temperature: Significant warming trends are clearly evident in the small islands, such as those in the Pacific, CAR, and western Indian Ocean, particularly over the latter half of the 20th century (see Figure Atlas.11; Atlas.10.2; Cross-Chapter Box Atlas.2, Table 1). This observed warming signal in the tropical western Pacific has been attributed to anthropogenic forcing (Wang et al., 2016). There is high confidence of warming over small islands even at 1.5°C GWL (Atlas.10.4 and Figure Atlas.28; Hoegh-Guldberg et al., 2018). Mean temperature is very likely to increase by 1°C–2°C (2°C–4°C) by 2041–2060 (2081–2100) under RCP8.5 (BOM and CSIRO, 2014) and SSP3-7.0 (Atlas.10.4, Figure 4.19 and Figure Atlas.12; Almazroui et al., 2021).

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Extreme heat: Observational records indicate warming trends in the temperature extremes since the 1950s in CAR and the Pacific small islands (high confidence) (Sections 11.3.2 and 11.9, and Table 11.13). A detectable anthropogenic increase in summer heat stress has been identified over a number of island regions in CAR, western tropical Pacific, and tropical Indian Ocean, based on wet bulb globe temperature (WBGT) index trends for 1973–2012 (medium confidence) (Knutson and Ploshay, 2016). An increasing trend in the maximum daytime heat index is also noted in CAR during the 1980–2014 period, as well as more extreme heat events since 1991 (Ramirez-Beltran et al., 2017).

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Coastal erosion: Recent studies have indicated variable and dynamic changes in shorelines of reef islands (medium confidence), including both erosion and accretion, which suggest factors other than SLR affecting shoreline changes, such as in the central and western Pacific within the past 50-to-60-year timeframe (Webb and Kench, 2010; Le Cozannet et al., 2014; Ford and Kench, 2015; Duvat and Pillet, 2017). For example, islands on atolls in the central and western Pacific have not substantially eroded or reduced in size in the past decades while sea level has been rising, but their position and morphology have changed due to anthropogenic factors (e.g., seawalls, reclamation) and climate–ocean processes (Biribo and Woodroffe, 2013; McLean and Kench, 2015). Analysis of aerial and satellite imagery revealed severe shoreline retreat in six islands and the disappearance of five vegetated reef islands in Solomon Islands in the western Pacific between 1947 and 2014, which may be due to the interaction between SLR and waves (Albert et al., 2016). In French Polynesia, changes in shoreline and island area have been observed since the 1960s, partly due to the effect of TCs on sediment changes and human activities (Duvat and Pillet, 2017; Duvat et al., 2017). Coastal erosion has also been noted over the small, low-lying, sandy islands, such as in French Polynesia and Solomon Islands, among others (Luijendijk et al., 2018; Mentaschi et al., 2018). Average shoreline retreat rates between 1 and 2 m yr–1 are estimated for the islands in the equatorial Pacific and in CAR, while a retreat rate of 0.5 m yr–1is estimated for islands in the South Pacific, based on satellite observations from 1984–2016 (Luijendijk et al., 2018; Mentaschi et al., 2018). There was also a loss of 610 km2 compared with a gain of 520 km2 in coastal area in Oceania during the 1984–2015 period (Mentaschi et al., 2018).

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Oceans face challenges from anthropogenic perturbations to the global Earth system, which cause increasing ocean warming, carbon dioxide-induced acidification and oxygen loss (Bindoff et al., 2019). Climate change will affect the major oceanic CIDs described in Section 12.2: mean ocean temperature, marine heatwave, ocean acidity, ocean salinity, and dissolved oxygen (O2), as well as severe wind storm and sea ice. These changes result in a shifting profile of hazards relevant to impact and risk assessments (Section 12.3). New evidence, the SROCC (IPCC 2019b) assessments and advances in the new CMIP6 climate simulations reinforce confidence in projected changes in climatic impact-drivers in the global oceans. As the ocean has taken up about 90% of the global warming for the period 1971–2018 (Section 7.2.2.2), the emergence of the sea surface temperature increase signal has already been observed in global oceans over the last century (Hawkins et al., 2020). The signal in sea ice extent decrease has already emerged in the Arctic Ocean (Landrum and Holland, 2020), while ocean acidification and low oxygen have also already emerged in many oceanic regions and will emerge in all global oceans by 2050 under RCP8.5 (Section 12.5.2 and Table 12.10). This section assesses key climatic impact-drivers that can be linked with sectoral and regional vulnerability and exposure in open and deep oceans, drawing from previous Chapters (Chapters 2, 3, 4, 5 and 9).

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Ocean acidity, ocean salinity and dissolved oxygen: The global ocean pH decline has very likely emerged from natural variability for more than 95% of the global open ocean (SROCC, Chapter 2). The regional signals are more variable, but in all ocean basins, the signal of ocean acidification in the surface ocean is projected to emerge in the early 21st century (Chapter 5). The mean ToE for acidity in the coastal subtropical to temperate north-east Pacific and north-west Atlantic is above two decades (high agreement, medium evidence) (Section 5.3.5.2). Salinity change signals have already emerged with 20–45% of the zonally averaged basin in the Atlantic, 20–55% in the Pacific and 25–50% in the Indian oceans and will be reaching 35–55% in the Atlantic in 2050 to 55–65% in 2080; 45–65% to 60–75% in the Pacific; and 45–65% to 60–80% in the Indian oceans (Chapter 9; Silvy et al., 2020). Deoxygenization has already emerged in many open oceans. The signal is most evident in the Pacific and Southern oceans but not evident in the North Atlantic Ocean (Andrews et al., 2013; Levin, 2018). However, there is medium confidence in the emergence of the anthropogenic signal in many other oceanic regions by 2050 (Henson et al., 2017; Levin, 2018).

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There is high confidence that several CID changes have already emerged above historical period natural variability in many regions (e.g., mean temperature in most regions, heat extremes in tropical areas, sea ice, salinity). Heat and cold CIDs (excluding frost) that have not already emerged will emerge by 2050 whatever the scenario in almost all land regions (medium confidence). The emergence of increasing precipitation before the middle of the century is also projected in Siberian regions, Russian Far East, Northern Europe and the northernmost parts of North America and Arctic regions across scenarios with the various methods and emergence definitions used (high confidence). Studies are missing to properly assess S/N emergence for droughts and for wind CIDs. Arctic sea ice extent declines have mostly emerged above noise level (medium to high confidence), and the emergence of declining snow cover is expected by the end of the century under RCP8.5. There is medium confidence that, under RCP8.5, the anthropogenic forced signal in near-coast relative sea level change will emerge by mid-century in all regions with coasts, except in the West Antarctic region where emergence is projected to occur before 2100. In all ocean basins, the signal of ocean acidification in the surface ocean is projected to emerge before 2050 (high confidence).

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Observed increases in well-mixed GHG concentrations since around 1750 are unequivocally caused by GHG emissions from humanactivities. Land and ocean sinks have taken up a near-constant proportion (globally about 56% per year) of CO2 emissions from humanactivities over the past six decades, with regional differences (high confidence). In 2019, atmospheric CO2 concentrations reached 410 parts per million (ppm), CH4 reached 1866 parts per billion (ppb) and nitrous oxide (N2O) reached 332 ppb68 . Other major contributors to warming aretropospheric ozone (O3) and halogenated gases. Concentrations of CH4 and N2O have increased to levels unprecedented in at least 800,000 years (very high confidence), and there ishigh confidencethat current CO2 concentrations are higher than at any time over at least the past two million years. Since 1750, increases in CO2 (47%) and CH4 (156%) concentrations far exceed – and increases in N2O (23%) are similar to – the natural multi-millennial changes between glacial and interglacialperiods overat least the past 800,000 years (very high confidence). The net cooling effect which arises from anthropogenic aerosols peaked in the late 20th century (high confidence). {WGI SPM A1.1, WGI SPM A1.3, WGI SPM A.2.1, WGI Figure SPM.2, WGI TS 2.2, WGI 2ES, WGI Figure 6.1}

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Average annual GHG emissions during 2010 –2019 were higher than in any previous decade, but the rate of growth between 2010 and 2019 (1.3% yr-1 ) was lower than that between 2000 and 2009 (2.1% yr-1 ) 69. Historical cumulative net CO2 emissions from 1850 to 2019 were 2400 ±240 GtCO2. Of these, more than half (58%) occurred between 1850 and 1989 [1400 ±195 GtCO2], and about 42% between 1990 and 2019 [1000 ±90 GtCO2]. Global net anthropogenic GHG emissions have been estimated to be 59±6.6 GtCO2-eq in 2019, about 12% (6.5 GtCO2-eq) higher than in 2010 and 54% (21 GtCO2-eq) higher than in 1990. By 2019, the largest growth in gross emissions occurred in CO2 from fossil fuels and industry (CO2-FFI) followed by CH4, whereas the highest relative growth occurred in fluorinated gases (F-gases), starting from low levels in 1990. (high confidence) {WGIII SPM B1.1, WGIII SPM B.1.2, WGIII SPM B.1.3, WGIII Figure SPM.1, WGIII Figure SPM.2}

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Regional contributions to global human-caused GHG emissions continue to differ widely. Historical contributions of CO2 emissions vary substantially across regions in terms of total magnitude, but also in terms of contributions to CO2-FFI (1650 ± 73 GtCO2-eq) and net CO2-LULUCF (760 ± 220 GtCO2-eq) emissions (Figure 2.2). Variations in regional and national per capita emissions partly reflect different development stages, but they also vary widely at similar income levels. Average per capita net anthropogenic GHG emissions in 2019 ranged from 2.6 tCO2-eq to 19 tCO2-eq across regions (Figure 2.2). Least Developed Countries (LDCs) and Small Island Developing States (SIDS) have much lower per capita emissions (1.7 tCO2-eq and 4.6 tCO2-eq, respectively) than the global average (6.9 tCO2-eq), excluding CO2-LULUCF. Around 48% of the global population in 2019 lives in countries emitting on average more than 6 tCO2-eq per capita, 35% of the global population live in countries emitting more than 9 tCO2-eq per capita 70 (excluding CO2-LULUCF) while another 41% live in countries emitting less than 3 tCO2-eq per capita. A substantial share of the population in these low-emitting countries lack access to modern energy services. (high confidence) {WGIII SPM B.3, WGIII SPM B3.1, WGIII SPM B.3.2, WGIII SPM B.3.3}

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WGI assessed the climate response to five illustrative scenarios based on SSPs 105 that cover the range of possible future development of anthropogenic drivers of climate change found in the literature. These scenarios combine socio-economic assumptions, levels of climate mitigation, land use and air pollution controls for aerosols and non-CH4 ozone precursors. The high and very high GHG emissions scenarios (SSP3-7.0 and SSP5-8.5) have CO2 emissions that roughly double from current levels by 2100 and 2050, respectively 106 . The intermediate GHG emissions scenario (SSP2-4.5) has CO2 emissions remaining around current levels until the middle of the century. The very low and low GHG emissions scenarios (SSP1-1.9 and SSP1-2.6) have CO2 emissions declining to net zero around 2050 and 2070, respectively, followed by varying levels of net negative CO2 emissions. In addition, Representative Concentration Pathways (RCPs)107 were used by WGI and WGII to assess regional climate changes, impacts and risks. {WGI BoxSPM.1} (Cross-Section Box.2 Figure 1)

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Changes in short-lived climate forcers (SLCF) resulting from the five considered scenarios lead to an additional net global warming in the near and long term (high confidence) . Simultaneous stringent climate change mitigation and air pollution control policies limit this additional warming and lead to strong benefits for air quality (high confidence) . In high and very high GHG emissions scenarios (SSP3-7.0 and SSP5-8.5), combined changes in SLCF emissions, such as CH4, aerosol and ozone precursors, lead to a net global warming by 2100 of likely 0.4°C to 0.9°C relative to 2019. This is due to projected increases in atmospheric concentration of CH4, tropospheric ozone, hydrofluorocarbons and, when strong air pollution control is considered, reductions of cooling aerosols. In low and very low GHG emissions scenarios (SSP1-1.9 and SSP1-2.6), air pollution control policies, reductions in CH4 and other ozone precursors lead to a net cooling, whereas reductions in anthropogenic cooling aerosols lead to a net warming (high confidence). Altogether, this causes a likely net warming of 0.0°C to 0.3°C due to SLCF changes in 2100 relative to 2019 and strong reductions in global surface ozone and particulate matter (high confidence). {WGI SPMD.1.7, WGI Box TS.7}. (Cross-Section Box.2)

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Solar Radiation Modification (SRM) approaches, if they were to be implemented, introduce a widespread range of new risks to people and ecosystems, which are not well understood. SRM has the potential to offset warming within one or two decades and ameliorate some climate hazards but would not restore climate to a previous state, and substantial residual or overcompensating climate change would occur at regional and seasonal scales (high confidence). Effects of SRM would depend on the specific approach used 122 , and a sudden and sustained termination of SRM in a high CO2 emissions scenario would cause rapid climate change (high confidence). SRM would not stop atmospheric CO2 concentrations from increasing nor reduce resulting ocean acidification under continued anthropogenic emissions (high confidence). Large uncertainties and knowledge gaps are associated with the potential of SRM approaches to reduce climate change risks. Lack of robust and formal SRM governance poses risks as deployment by a limited number of states could create international tensions.{WGI 4.6; WGII SPM B.5.5; WGIII 14.4.5.1; WGIII 14 Cross-Working Group Box Solar Radiation Modification; SR1.5 SPM C.1.4}

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The likelihood of abrupt and irreversible changes and their impacts increase with higher global warming levels (high confidence). As warming levels increase, so do the risks of species extinction or irreversible loss of biodiversity in ecosystems such as forests (medium confidence), coral reefs (very high confidence) and in Arctic regions (high confidence). Risks associated with large-scale singular events or tipping points, such as ice sheet instability or ecosystem loss from tropical forests, transition to high risk between 1.5°C to 2.5°C (medium confidence) and to very high risk between 2.5°C to 4°C (low confidence). The response of biogeochemical cycles to anthropogenic perturbations can be abrupt at regional scales and irreversible on decadal to century time scales (high confidence). The probability of crossing uncertain regional thresholds increases with further warming (high confidence). {WGI SPMC.3.2, WGI Box TS.9, WGI TS.2.6; WGII Figure SPM.3, WGII SPM B.3.1, WGII SPM B.4.1, WGII SPM B.5.2, WGII Table TS.1, WGII TS.C.1, WGII TS.C.13.3; SROCC SPM B.4}

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Limiting human-caused global warming requires net zero anthropogenic CO2 emissions. Pathways consistent with 1.5°C and 2°C carbon budgets imply rapid, deep, and in most cases immediate GHG emission reductions in all sectors (high confidence). Exceeding a warming level and returning (i.e. overshoot) implies increased risks and potential irreversible impacts; achieving and sustaining global net negative CO2 emissions would reduce warming (high confidence).

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The best estimates of the remaining carbon budget (RCB) from the beginning of 2020 for limiting warming to 1.5°C with a 50% likelihood127 is estimated to be 500 GtCO2 ; for 2°C (67% likelihood) this is 1150 GtCO2 . 128 Remaining carbon budgets have been quantified based on the assessed value of TCRE and its uncertainty, estimates of historical warming, climate system feedbacks such as emissions from thawing permafrost, and the global surface temperature change after global anthropogenic CO2 emissions reach net zero, as well as variations in projected warming from non-CO2 emissions due in part to mitigation action. The stronger the reductions in non-CO2 emissions the lower the resulting temperatures are for a given RCB or the larger RCB for the same level of temperature change. For instance, the RCB for limiting warming to 1.5°C with a 50% likelihood could vary between 300 to 600 GtCO2 depending on non-CO2 warming 129 . Limiting warming to 2°C with a 67% (or 83%) likelihood would imply a RCB of 1150 (900) GtCO2 from the beginning of 2020. To stay below 2°C with a 50% likelihood, the RCB is higher, i.e., 1350 GtCO2130 . {WGI SPM D.1.2, WGI Table SPM.2; WGIII Box SPM.1, WGIII Box 3.4; SR1.5 SPM C.1.3}

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From a physical science perspective, limiting human-caused global warming to a specific level requires limiting cumulative CO2 emissions, reaching net zero or net negative CO2 emissions, along with strong reductions of other GHG emissions (see Cross-Section Box.1). Global modelled pathways that reach and sustain net zero GHG emissions are projected to result in a gradual decline in surface temperature (high confidence). Reaching net zero GHG emissions primarily requires deep reductions in CO2, methane, and other GHG emissions, and implies net negative CO2 emissions. 134 Carbon dioxide removal (CDR) will be necessary to achieve net negative CO2 emissions 135 . Achieving global net zero CO2 emissions, with remaining anthropogenic CO2 emissions balanced by durably stored CO2 from anthropogenic removal, is a requirement to stabilise CO2-induced global surface temperature increase (see 3.3.3). (high confidence). This is different from achieving net zero GHG emissions, where metric-weighted anthropogenic GHG emissions (see Cross-Section Box.1) equal CO2 removal (high confidence). Emissions pathways that reach and sustain net zero GHG emissions defined by the 100-year global warming potential imply net negative CO2 emissions and are projected to result in a gradual decline in surface temperature after an earlier peak (high confidence). While reaching net zero CO2 or net zero GHG emissions requires deep and rapid reductions in gross emissions, the deployment of CDR to counterbalance hard-to-abate residual emissions (e.g., some emissions from agriculture, aviation, shipping, and industrial processes) is unavoidable (high confidence). {WGI SPM D.1, . WGI SPM D.1.1, WGI SPM D.1.8; WGIII SPM C.2, WGIII SPM C.3, WGIII SPM C.11, WGIII Box TS.6; SR1.5 SPM A.2.2}

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Climate change has caused regionally different, but mostly negative, impacts on crop yields and quality and marketability of products (high confidence) (see Section 5.4.1 for observed impacts). There is medium evidence and high agreement that the effects of human-induced climate warming since the pre-industrial era has had significantly negative effects on global crop production, acting as a drag on the growth of agricultural production (Iizumi et al., 2018; Moore, 2020; Ortiz-Bobea et al., 2021). One global study using an empirical model estimated the negative effect of anthropogenic warming trends from 1961 to 2017 to be on average 5.3% for three staple crops (5.9% for maize, 4.9% for wheat and 4.2% for rice) (Moore, 2020). Another study using a process-based crop model found a yield loss of 4.1% (0.5–8.4%) for maize and 4.5% (0.5–8.4%) for soybean between 1981 and 2010 relative to the non-warming condition, even with CO2 fertilisation effects (Iizumi et al., 2018). Human-induced warming trends since 1961 have also slowed down the growth of agricultural total factor productivity by 21% (Ortiz-Bobea et al., 2021). Regionally, heat and rainfall extremes intensified by human-induced warming in West Africa have reduced millet and sorghum yields by 10–20%, and 5–15%, respectively (Sultan et al., 2019).

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Methane emissions significantly impact crop yields by increasing temperatures as a greenhouse gas (GHG) and surface ozone concentrations as a precursor (medium confidence) (Shindell, 2016; Van Dingenen, 2018; Shindell et al., 2019). Shindell (2016) estimated a net yield loss of 9.5±3.0% for four major crops due to anthropogenic emissions (1850–2010), after incorporation of the positive effect of CO2 (6.5±1.0%) and the negative effects of warming (10.9±3.2%) and tropospheric ozone elevation (5.0±1.5%). Although these estimates were not linked with historical yield changes, more than half of the estimated yield loss is attributable to increasing temperature and ozone concentrations from methane emissions, suggesting the importance of methane mitigation in alleviating yield losses (medium confidence) (Section 5.4.1.4).

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The impacts of climate change on food provisioning have cascading effects on key elements of food security, such as food prices, household income, food safety and nutrition of vulnerable groups (Peri, 2017; Ubilava, 2018; 5.11, 5.12). Climate extreme events are frequently causing acute food insecurity (Section 5.12.3, FSIN, 2021). There is growing evidence that human-induced climate warming has amplified climate extreme events (Seneviratne et al., 2021), but detection and attribution of food insecurity to anthropogenic climate change is still limited by a lack of long-term data and complexity of food systems (Phalkey et al., 2015; Cooper et al., 2019). A recent event attribution study by Funk (2018) demonstrated that anthropogenic enhancement of the 2015/2016 El Niño increased drought-induced crop production losses in Southern Africa. Human-induced warming also exacerbated the 2007 drought in southern Africa, causing food shortages, price spikes and acute food insecurity in Lesotho (Verschuur et al., 2021).

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Global yields of major crops per unit land area have increased 2.5- to 3-fold since 1960. Plant breeding, fertilisation, irrigation and integrated pest management have been the major drivers, but many studies have found significant impacts from recent climate trends on crop yield (high confidence) (Figure 5.3; see Section 5.2.1 for the change attributable to anthropogenic climate change).

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There is growing evidence that anthropogenic climate warming has already intensified climate extreme events induced by large-scale SST oscillations such as ENSO (Herring et al., 2018; Seneviratne et al., 2021). For example, the 2015–2016 El Niño, the strongest in the past 145 years, induced severe droughts in Southeast Asia and eastern and southern Africa, some intensified by anthropogenic warming (Funk et al., 2018). As a result, 20.5 million people faced acute food insecurity in 2016 (FSIN, 2017) and an estimated additional 5.9 million children became underweight (Anttila-Hughes et al., 2021).

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Bannister, W., et al., 2019: Potential anthropogenic regime shifts in three freshwater lakes in Tropical East Asia. Freshw Biol, 64 (4), 708–722, doi:10.1111/fwb.13256.

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Funk, C., et al., 2018: Anthropogenic enhancement of moderate-to-strong el nino events likely contributed to drought and poor harvests in Southern Africa during 2016. Bull. Am. Meterol. Soc. , 99 (1), 91–S96, doi:10.1175/Bams-D-17-0112.1.

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Harrod, C., A. Ramírez, J. Valbo-Jørgensen and S. Funge-Smith, 2018a: Current anthropogenic stress and projected effect of climate change on global inland fisheries. In: Impacts of Climate Change on Fisheries and Aquaculture: Synthesis of Current Knowledge, Adaptation and Mitigation Options[Barange, M., et al.(ed.)]. Food and Agricultural Organization of the United Nations, Rome, Italy, pp. 393–448. ISBN 978-9251306079.

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Iizumi, T., et al., 2018: Crop production losses associated with anthropogenic climate change for 1981–2010 compared with preindustrial levels. Int. J. Climatol. , 38 (14), 5405–5417, doi:10.1002/joc.5818.

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Moore, F., 2020: The fingerprint of anthropogenic warming on global agriculture. EarthArXiv, doi:10.31223/x5q30z.

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Ortiz-Bobea, A., et al., 2021: Anthropogenic climate change has slowed global agricultural productivity growth. Nat. Clim. Change, 11 (4), 306–312, doi:10.1038/s41558-021-01000-1.

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Smith, M.R. and S.S. Myers, 2018: Impact of anthropogenic CO2 emissions on global human nutrition. Nat. Clim. Chang. , 8 (9), 834, doi:10.1038/s41558-018-0253-3.

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Thomas, E., C. Alcázar Caicedo, J. Loo and R. Kindt, 2014: The distribution of the Brazil nut (Bertholletia excelsa) through time: from range contraction in glacial refugia, over human-mediated expansion, to anthropogenic climate change. Bol Do Mus Para Emílio Goeldi Ciências Nat , 9, 267–291.

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When anthropogenic climate change affects ecosystems, it can also affect ecosystem services for people. Climate change connects to ecosystem services by means of three links, i.e., climate change–species–ecosystems–ecosystem services. This chapter assesses these connections via all three links when end-to-end published scientific analyses are available for terrestrial and freshwater ecosystems. This type of robust evidence exists for some key ecosystem services (Section 2.5.3, 2.5.4), and is assessed in specific report sections: biodiversity habitat creation and maintenance (Sections 2.4, 2.5), regulation of detrimental organisms and biological processes (Sections 2.4.2.3, 2.4.2.7, 2.4.4, 2.5.3, 2.6.4, Cross-Chapter Box ILLNESS in this chapter), regulation of climate through ecosystem feedbacks in terms of carbon storage (Sections 2.4.4.4, 2.5.2.10, 2.5.3.4, 2.5.3.5) and albedo (Section 2.5.3.5) and the provision of freshwater from ecosystems to people (Section 2.5.3.6).

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Table 2.1 | Connections of ecosystem services to climate change, indicating the 18 categories of nature’s contributions to people (IPBES, 2019), the most relevant sections in the AR6, and the level of evidence in this report for attribution to anthropogenic climate change of observed impacts on ecosystem services. The order of services in the table follows the order presented by IPBES and does not denote importance or priority. Connections denote observed impacts, future risks and adaptation. The order of connections follows the relevance or the order of sections. Numbers in parentheses refer to sections in this chapter.

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Depending on how the intensification of the global water cycle affects individual lake water budgets, the amount of water stored in specific lakes may increase, decrease or have no substantial cumulative effect (Notaro et al., 2015; Pekel et al., 2016; Rodell et al., 2018; Busker et al., 2019; Woolway et al., 2020b). The magnitude of hydrological changes that can be assuredly attributed to climate change remains uncertain (Hegerl et al., 2015; Gronewold and Rood, 2019; Kraemer et al., 2020). Attribution of water storage variation in lakes due to climate change is facilitated when such variations occur coherently across broad geographic regions and long time scales, preferably absent of other anthropogenic hydrological influences (Watras et al., 2014; Kraemer et al., 2020). There is increasing awareness that climate change contributes to the loss of small temporary ponds which cover a greater global area than lakes (Bagella et al., 2016).

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In a study of 1567 lakes across Europe and North America, Kakouei (2021) identified climate change as the major driver of increases in phytoplankton biomass in remote areas with minimal LULCC. Greater temperature variability can be more important than long-term temperature trends as a driver of zooplankton biodiversity (Shurin et al., 2010). Reductions of winter severity attributed to anthropogenic climate change are increasing winter algal biomass, and motile and phototropic species, at the expense of mixotrophic species (Özkundakci et al., 2016; Hampton et al., 2017).

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Anthropogenic impacts, such as disturbances caused by climate change, can reduce biodiversity via multiple mechanisms and increase the risk of human diseases (limited evidence, low agreement ), but more research is needed to understand the underlying mechanisms (Civitello et al., 2015; Young et al., 2017b; Halliday et al., 2020; Rohr et al., 2020; Glidden et al., 2021). Known wildlife hosts of human-shared pathogens and parasites overall comprise a greater proportion of local species richness (18–72% higher) and abundance (21–144% higher) at sites under substantial human use (agricultural and urban land) compared with nearby undisturbed habitats (Gibb et al., 2020).

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AR5 and a meta-analysis found that vegetation at the biome level shifted poleward latitudinally and upward altitudinally due to anthropogenic climate change in at least 19 sites in boreal, temperate and tropical ecosystems from 1700 to 2007 (Gonzalez et al., 2010; Settele et al., 2014). In these areas, temperature increased to 0.4°C–1.6°C above the pre-industrial period (Gonzalez et al., 2010; Settele et al., 2014). Field research since the AR5 detected additional poleward and upslope biome shifts over periods of 24–210 years at numerous sites (described below), but were not directly attributed to anthropogenic climate change as the studies were not designed or conducted properly for full attribution assessment.

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Many of the recently detected shifts are nevertheless consistent with climate change-induced temperature increases and observed in areas without agriculture, livestock grazing, timber harvesting and other anthropogenic land uses. For example, in the Andes Mountains in Ecuador, a biome shift was detected by comparing a survey by Alexander von Humboldt in 1802 to a re-survey in 2012, making this the longest time span in the world for this type of data (Morueta-Holme et al., 2015; Moret et al., 2019). Over 210 years, temperature increased by 1.7°C (Morueta-Holme et al., 2015) and the upper edge of alpine grassland shifted 100–450 m upslope (Moret et al., 2019).

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In summary, anthropogenic climate change caused latitudinal and elevational biome shifts in at least 19 sites in boreal, temperate and tropical ecosystems between 1700 and 2007, where temperature increased to 0.4°C–1.6°C above the pre-industrial period (robust evidence, high agreement ). Additional cases of 5–20 km northward and 20–300 m upslope biome shifts between 1860 and 2016, under a mean global temperature increase of approximately 0.9°C above the pre-industrial period, are consistent with climate change (medium evidence, high agreement ).

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Divergent responses to anthropogenic climate change are occurring within and across arid regions, depending on time period, location, detection methodology and vegetation type (see Cross-Chapter Paper 3). Emerging shifts in ecosystem structure, functioning and biodiversity are supported by evidence from modelled impacts of projected climate and CO2 levels. While observed responsiveness of arid vegetation productivity to rising atmospheric CO2 (Fensholt et al., 2012) may offset risks from reduced water availability (Fang et al., 2017), climate- and CO2-driven changes are key risks in arid regions, interacting with habitat degradation, wildfires and invasive species (Hurlbert et al., 2019).

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The AR5 found increased tree mortality, wildfire and plant phenology changes in boreal and temperate forests (Settele et al., 2014). Expanding on these conclusions, this assessment, using analyses of causal factors, attributes the following observed changes in boreal and temperate forests in the 20th and 21st centuries to anthropogenic climate change: upslope and poleward biome shifts at sites in Asia, Europe and North America (Section 2.4.3.2.1); range shifts of plants (Section 2.4.2.1); earlier blooming and leafing of plants (Section 2.4.2.4); poleward shifts in tree-feeding insects (Section 2.4.2.1); increases in insect pest outbreaks (Section 2.4.4.3.3); increases in the area burned by wildfire in western North America (Section 2.4.4.2.1); increased drought-induced tree mortality in western North America (Section 2.4.4.3.1); and thawing of the permafrost that underlies extensive areas of boreal forest (Section 2.4.3.9)(Section 2.3.2.5 in (Gulev et al., 2021)). Atmospheric CO2 from anthropogenic sources has also increased net primary productivity (NPP) (Section 2.4.4.5.1). In summary, anthropogenic climate change has caused substantial changes in temperate and boreal forest ecosystems, including biome shifts and increases in wildfire, insect pest outbreaks and tree mortality, at a global mean surface temperature (GMST) increase of 0.9°C above the pre-industrial period (robust evidence, high agreement ).

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Globally, peatland ecosystems store approximately 25% (600 ± 100 GtC) of the world’s soil organic carbon (Yu et al., 2010; Page et al., 2011; Hugelius et al., 2020) and 10% of the world’s freshwater resources (Joosten and Clarke, 2002), despite only occupying 3% of the global land area (Xu et al., 2018a). The long-term role of northern peatlands in the carbon cycle was mentioned for the first time in IPCC AR4 (IPCC, 2007), while SR1.5 briefly mentioned the combined effects of changes in climate and land use on peatlands (IPCC, 2018b). New evidence confirms that climate change, including extreme weather events (e.g., droughts; Section 8.3.1.6), permafrost degradation (Section 2.3.2.5), SLR (Section 2.3.3.3) and fire (Section 5.4.3.2) (Henman and Poulter, 2008; Kirwan and Mudd, 2012; Turetsky et al., 2015; Page and Hooijer, 2016; Swindles et al., 2019; Hoyt et al., 2020; Hugelius et al., 2020; Jovani-Sancho et al., 2021; Veraverbeke et al., 2021), superimposed on anthropogenic disturbances (e.g., draining for agriculture or mining; Section 5.2.1.1), has led to rapid losses of peatland carbon across the world (robust evidence, high agreement ) (Page et al., 2011; Leifeld et al., 2019; Hoyt et al., 2020; Turetsky et al., 2020; Loisel et al., 2021). Other essential peatland ecosystem services, such as water storage and biodiversity, are also being lost worldwide (robust evidence, high agreement ) (Bonn et al., 2014; Martin-Ortega et al., 2014; Tiemeyer et al., 2017).

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In large, lowland tropical peatland basins that are less impacted by anthropogenic activities (i.e., the Amazon and Congo river basins), the direct impact of climate change is that of a decreased carbon sink (limited evidence, medium agreement ) (Roucoux et al., 2013; Gallego-Sala et al., 2018; Wang et al., 2018a; Dargie et al., 2019; Ribeiro et al., 2021). As for the temperate and boreal regions, climatic drying also tends to promote peat oxidation and carbon loss to the atmosphere (medium evidence, medium agreement ) (Section 2.3.1.3.4) (Helbig et al., 2020; Zhang et al., 2020). In Europe, increasing mean annual temperatures in the Baltic, Scandinavia, and continental Europe (Section 12.4.5.1) have led to widespread lowering of peatland water tables at intact sites (Swindles et al., 2019), desiccation and die-off of sphagnum moss (Bragazza, 2008; Lees et al., 2019) and increased intensity and frequency of fires, resulting in a rapid carbon loss (Davies et al., 2013; Veraverbeke et al., 2021). Nevertheless, longer growing seasons and warmer, wetter climates have increased carbon accumulation and promoted thick deposits regionally, as reported for some North American sites (limited evidence, medium agreement ) (Cai and Yu, 2011; Shiller et al., 2014; Ott and Chimner, 2016).

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The CO2 emissions from degrading peatlands is contributing to climate change in a positive feedback loop (robust evidence, high agreement). At mid-latitudes, widespread anthropogenic disturbance led to large historical GHG emissions and current legacy emissions of 0.15 PgC yr -1 between 1990 and 2000 (limited evidence, high agreement ) (Maljanen et al., 2010; Tiemeyer et al., 2016; Drexler et al., 2018; Qiu et al., 2021). About 80 million hectares of peatland have been converted to agriculture, equivalent to 72 PgC emissions in 850–2010 CE (Leifeld et al., 2019; Qiu et al., 2021). In Southeast Asia (SEA), an estimated 20–25 Mha of peatlands have been converted to agriculture with carbon currently being lost at a rate of ~155 ± 30 MtC yr −1 (Miettinen et al., 2016; Leifeld et al., 2019; Hoyt et al., 2020). Extensive deforestation and drainage have caused widespread peat subsidence and large CO2 emissions at a current average of ~10 ± 2 tonnes ha -1 yr -1, excluding fires (Hoyt et al., 2020), with values estimated from point subsidence measurements being as high as 30–90 tonnes CO2 ha −1 yr −1 locally (robust evidence, high agreement ) (Wösten et al., 1997; Matysek et al., 2018; Swails et al., 2018; Evans et al., 2019; Conchedda and Tubiello, 2020; Anshari et al., 2021). On average, at the global scale, increases in GHG emissions from peatlands have primarily come from the compounded effects of LUC, drought and fire, with additional emissions from some thawing-permafrost peatlands (robust evidence, high agreement ).

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Wildfire is a natural and essential component of many forest and other terrestrial ecosystems. Excessive wildfire, however, can kill people, cause respiratory disease, destroy houses, emit carbon dioxide and damage ecosystem integrity (see Sections 2.4.4.2 and 2.4.4.4). Anthropogenic climate change increases wildfire by exacerbating its three principal driving factors: heat, fuel and ignition (Moritz et al., 2012; Jolly et al., 2015). Non-climatic factors also contribute to wildfires—in tropical areas, fires are set intentionally to clear forest for agricultural fields and livestock pastures (Bowman et al., 2020). Urban areas and roads create ignition hazards. Governments in many temperate-zone countries implement policies to suppress fires, even natural ones, producing unnatural accumulations of fuel in the form of coarse woody debris and high densities of small trees (Ruffault and Mouillot, 2015; Hessburg et al., 2016; Andela et al., 2017; Balch et al., 2017; Lasslop and Kloster, 2017; Aragao et al., 2018; Kelley et al., 2019). Globally, 4.2 million km 2 of land per year burned on average from 2002 to 2016 (Giglio et al., 2018), with the highest fire frequencies in the Amazon rainforest, deciduous forests and savannas in Africa and deciduous forests in northern Australia (Earl and Simmonds, 2018; Andela et al., 2019).

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Since the AR5 and the IPCC Special Report on Land, published research has detected increases in the area burned by wildfire, analysed relative contributions of climate and non-climate factors and attributed burned area increases above natural (recent historical) levels to anthropogenic climate change in one part of the world, western North America (robust evidence, high agreement) (Abatzoglou and Williams, 2016; Partain et al., 2016; Kirchmeier-Young et al., 2019; Mansuy et al., 2019; Bowman et al., 2020). Across the western USA, increases in vegetation aridity due to higher temperatures from anthropogenic climate change doubled burned area from 1984 to 2015 over what would have burned due to non-climate factors including unnatural fuel accumulation from fire suppression, with the burned area attributed to climate change accounting for 49% (32–76%, 95% confidence interval) of cumulative burned area (Abatzoglou and Williams, 2016). Anthropogenic climate change doubled the severity of a southwest North American drought from 2000 to 2020 that has reduced soil moisture to its lowest levels since the 1500s (Williams et al., 2020), driving half of the increase in burned area (Abatzoglou and Williams, 2016; Holden et al., 2018; Williams et al., 2019). In British Columbia, Canada, the increased maximum temperatures due to anthropogenic climate change increased burned area in 2017 to its highest extent in the 1950–2017 record, seven to eleven times the area that would have burned without climate change (Kirchmeier-Young et al., 2019). In Alaska, USA, the high maximum temperatures and extremely low relative humidity due to anthropogenic climate change accounted for 33–60% of the probability of wildfire in 2015, when the area burned was the second highest in the 1940–2015 record (Partain et al., 2016). In protected areas of Canada and the USA, climate factors (temperature, precipitation, relative humidity and evapotranspiration) accounted for 60% of burned area from local human and natural ignitions from 1984 to 2014, outweighing local human factors (population density, roads and built area) (Mansuy et al., 2019).

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In summary, field evidence shows that anthropogenic climate change has increased the area burned by wildfire above natural levels across western North America in the period 1984–2017, at GMST increases of 0.6°C–0.9°C, increasing burned area up to 11 times in one extreme year and doubling it (over natural levels) in a 32-year period (high confidence).

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While burned area has increased in parts of Asia, Australia, Europe and South America, published research has not yet attributed the increases to anthropogenic climate change (medium evidence, high agreement ).

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In Australia, burned area increased significantly between the periods 1950–2002 and 2003–2020 in the southeast state of Victoria, with the area burned in the 2019–2020 bushfires being the highest on record (Lindenmayer and Taylor, 2020). In addition to the deaths of dozens of people and the destruction of thousands of houses, the 2019–2020 bushfires burned almost half of the area protected for conservation in Victoria, two-thirds of the forests allocated for timber harvesting (Lindenmayer and Taylor, 2020), wildlife and extensive areas of habitat for threatened plant and animal species (Geary et al., 2021). Generally, past timber harvesting did not lead to more severe fire canopy damage (Bowman et al., 2021b). Across southeastern Australia, the fraction of vegetated area that burned increased significantly in eight of the 32 bioregions from 1975 to 2009, but decreased significantly in three bioregions (Bradstock et al., 2014). Increases in four bioregions were correlated to increasing temperature and decreasing precipitation. Decreases in burned area occurred despite increased temperature and decreased precipitation. Analyses of climate across Australia from 1950 to 2017 (Dowdy, 2018; Harris and Lucas, 2019) and during periods with extensive fires in 2017 in eastern Australia (Hope et al., 2019), in 2018 in northeastern Australia (Lewis et al., 2020), and in period 2019–2020 in southeastern Australia (Abram et al., 2021; van Oldenborgh et al., 2021) indicate that temperature and drought extremes due to the ENSO, Southern Annular Mode and other natural inter-decadal cycles drive inter-annual variability of fire weather. While the effects of inter-decadal climate cycles on fire are superimposed on long-term climate change, the relative importance of anthropogenic climate change in explaining changes in burned area in Australia remains unquantified (medium evidence, high agreement ).

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Overall, burned area has increased in the Amazon, Arctic, Australia and parts of Africa and Asia, consistent with, but not formally attributed to anthropogenic climate change (medium evidence, high agreement ). Deforestation, peat draining, agricultural expansion or abandonment, fire suppression and inter-decadal cycles such as the ENSO exert a stronger influence than climate change on wildfire trends in numerous regions outside of North America (high confidence).

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The IPCC AR6 WGI assessed fire weather (Ranasinghe et al., 2021), while this chapter assesses the impacts of changes in fire weather: burned area and fire frequency. The global increases in temperature from anthropogenic climate change have increased aridity and drought, lengthening the fire weather season (the annual period with a heat and aridity index greater than half of its annual range) on one-quarter of global vegetated area and increasing the average fire season length by one-fifth from 1979 to 2013 (Jolly et al., 2015). Climate change has contributed to increases in the fire weather season or the probability of fire weather conditions in the Amazon (Jolly et al., 2015), Australia (Dowdy, 2018; Abram et al., 2021; van Oldenborgh et al., 2021), Canada (Hanes et al., 2019), central Asia (Jolly et al., 2015), East Africa (Jolly et al., 2015) and North America (Jain et al., 2017; Williams et al., 2019; Goss et al., 2020). In forest areas, the burned area correlates with fuel aridity, a function of temperature; in non-forest areas, the burned area correlates with high precipitation in the previous year, which can produce high grass fuel loads (Abatzoglou et al., 2018). Fire use in agriculture and raising livestock or other factors have generated a second fire season on approximately one-quarter of global land where fire is present, despite sub-optimal fire weather in the second fire season (Benali et al., 2017). In summary, anthropogenic climate change, through a 0.9°C surface temperature increase since the pre-industrial period, has lengthened or increased the frequency of periods with heat and aridity that favour wildfire on up to one-quarter of vegetated area since 1979 (robust evidence, high agreement ).

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Globally, fire has contributed to biome shifts (Section 2.4.3.2) and tree mortality (Sections 2.4.4.2, 2.4.4.3) attributed to anthropogenic climate change. Research since the AR5 has also found vegetation changes from wildfire due to climate change. Through increased temperature and aridity, anthropogenic climate change has driven post-fire changes in plant regeneration and species composition in South Africa (Slingsby et al., 2017), and tree regeneration in the western USA (Davis et al., 2019b). In the fynbos vegetation of the Cape Floristic Region, South Africa, post-fire heat and drought and the legacy effects of exotic plant species reduced the regeneration of native plant species, decreasing species richness by 12% from 1966 to 2010 and shifting the average temperature tolerance of species communities upward by 0.5°C (Slingsby et al., 2017). In burned areas across the western USA, the increasing heat and aridity of anthropogenic climate change from 1979 to 2015 pushed low-elevation ponderosa pine (Pinus ponderosa) and Douglas fir (Pseudotsuga menziesii) forests across critical thresholds of heat and aridity that reduced the post-fire tree regeneration by half (Davis et al., 2019b). In the southwestern USA, where anthropogenic climate change has caused drought (Williams et al., 2019) and increased wildfire (Abatzoglou and Williams, 2016), high-severity fires have converted some forest patches to shrublands (Barton and Poulos, 2018). Field evidence shows that anthropogenic climate change and wildfire, together, altered vegetation species composition in the southwestern USA and Cape floristic region, South Africa, reducing post-fire natural regeneration and species richness of tree and other plant species, between 1966 and 2015, at GMST increases of 0.3°C–0.9°C (medium evidence, high agreement ).

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Anthropogenic climate change can cause tree mortality directly via increased aridity or drought (Section 2.4.4.3.3) or indirectly through wildfire (Section 2.4.4.2.1) and insect pests (Section 2.4.4.3.3). Catastrophic failure of the plant hydraulic system, in which a lack of water causes the xylem to lose hydraulic conductance, is the principal mechanism of drought-induced tree death (Anderegg et al., 2016; Adams et al., 2017; Anderegg et al., 2018; Choat et al., 2018; Menezes-Silva et al., 2019; Brodribb et al., 2020).

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Up through the AR5 (Settele et al., 2014), detection and attribution analyses had found that anthropogenic climate change, with global temperature increases of 0.3°C–0.9°C above the pre-industrial period and the increases in aridity exceeding the effects of local non-climate change factors, caused three cases of drought-induced tree mortality of up to 20% in the period 1945–2007 in western North America (van Mantgem et al., 2009), the African Sahel (Gonzalez et al., 2012) and North Africa (le Polain de Waroux and Lambin, 2012). Increased wildfire and pest infestations, driven by climate change, also contributed to North American tree mortality (van Mantgem et al., 2009). In addition, a meta-analysis of published cases found that drought consistent with, but not formally attributed to, climate change had caused tree mortality at 88 sites in boreal, temperate and tropical ecosystems (Allen et al., 2010), with 49 additional cases found by the AR5 (Settele et al., 2014).

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In summary, anthropogenic climate change caused drought-induced tree mortality of up to 20% in the period 1945–2007 in western North America, the African Sahel and North Africa, via global temperature increases of 0.3°C–0.9°C above the pre-industrial period and increases in aridity, and it contributed to over 100 other cases of drought-induced tree mortality in Africa, Asia, Australia, Europe and North and South America (high confidence). Field observations document accelerating mortality rates, rising background mortality and post-mortality vegetation shifts (high confidence). Water stress, leading to plant hydraulic failure, is the principal mechanism of drought-induced tree mortality. Timber cutting, agricultural expansion, air pollution and other non-climate factors also contribute to tree death.

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The most extensive research into tree mortality since the AR5 has been in the western USA, where anthropogenic climate change accounted for half the magnitude of a drought in the period 2000–2020 that has been the most severe since the 1500s, (Williams et al., 2020) and for one-tenth to one-quarter of the magnitude of the 2012–2014 period of th e severe drought in California that lasted from 2012 to 2016 (Williams et al., 2015a). Across the western USA, anthropogenic climate change doubled tree mortality between 1955 and 2007 (van Mantgem et al., 2009). Lodgepole pine (Pinus contorta) mortality increased 700% from 2000 to 2013 (Anderegg et al., 2015) and piñon pine (P. edulis) experienced >50% mortality from 2002 to 2014 (Redmond et al., 2018). In montane conifer forest in California, anthropogenic climate change has increased tree mortality by one-quarter (Goulden and Bales, 2019). One-quarter of the trees died in some areas, with mortality rates of ponderosa pine (P. ponderosa) and sugar pine (P. lambertiana) increasing to up to 700% of pre-drought rates (Stephenson et al., 2019; Stovall et al., 2019). Substantial field evidence shows that anthropogenic climate change has caused extensive tree mortality in North America (robust evidence, high agreement ).

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Deforestation, draining of peatlands and the expansion of agricultural fields, livestock pastures and human settlements and other LULCCs emitted carbon at a rate of 1.6 ± 0.7 Gt yr -1 from 2010 to 2019, (Friedlingstein et al., 2020), of which wildfires and peat burning emitted 0.4 ± 0.2 Gt yr -1 from 1997 to 2016 (van der Werf et al., 2017). Anthropogenic climate change has caused some of these emissions through increases in wildfire (Section 2.4.4.2.1) and tree mortality (Section 2.4.4.3.1), but the fraction of the total remains unquantified. LUC produced ~15% of global anthropogenic emissions, from fossil fuels and land (Friedlingstein et al., 2020). Terrestrial ecosystems removed carbon from the atmosphere through plant growth at a rate of -3.4 ± 0.9 Gt yr -1 from 2010 to 2019 (Friedlingstein et al., 2020).

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In North America, wildfire emitted 0.1 ± 0.02 GtC yr -1 from in the period 1990–2012, but regrowth was slightly greater, producing a net sink (Chen et al., 2017). In California, USA, two-thirds of the 70 MtC emitted from natural ecosystems in 2001–2010 came from the 6% of the area that burned (Gonzalez et al., 2015). Anthropogenic climate change caused up to half of the burned area (Section 2.4.4.2.1).

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In the Arctic, anthropogenic climate change has thawed permafrost (Guo et al., 2020), leading to emissions of 1.7 ± 0.8 GtC yr -1 in winter in the period 2003–2017 (Natali et al., 2019). Wildfires in the Arctic tundra in Alaska from ~1930 to 2010 caused up to a depth of 0.5 m of permafrost thaw (Brown et al., 2015), exposing peatland carbon (Brown et al., 2015; Gibson et al., 2018) including soil carbon deposits up to 1600 years old (Walker et al., 2019).

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Responses in freshwater species are consistent with responses in terrestrial species, including poleward and upward range shifts, earlier timing of spring plankton development, earlier spawning by fish and the extension of the growing season (high confidence). Observed changes in freshwater species are strongly related to anthropogenic climate change-driven changes in the physical environment (e.g., increased water temperature, reduced ice cover, reduced mixing in lakes, loss of oxygen and reduced river connectivity) (high confidence). While evidence is robust for an increase in primary production in nutrient rich lakes along with warming trends (high confidence), increasing or declining algal formations are lake-specific and are modulated through variability in weather conditions, lake morphology, changes in salinity, stoichiometry, land use and restoration measures and food web interactions. In boreal coniferous forest, there has been an increase in terrestrial-derived DOM transported into rivers and lakes as a consequence of climate change (which has induced increases in runoff and greening of the Northern Hemisphere) as well as from changes in forestry practices. This has caused waters to become brown, resulting in an acceleration of upper-water warming and an overall cooling of deep water (high confidence). Browning may accelerate primary production through the input of nutrients associated with DOM in nutrient-poor lakes and increases the growth of cyanobacteria, which cope better with low light intensity (medium confidence) (Sections 2.4.2.1, 2.4.2.2, 2.4.2.3, 2.4.2.4).

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Field research since the AR5 has detected biome shifts at numerous sites, poleward and upslope, that are consistent with increased temperatures and altered precipitation patterns driven by climate change, and support prior studies that attributed such shifts to anthropogenic climate change (high confidence). New studies help fill previous geographic and habitat gaps, for example, documenting upward shifts in the forest/alpine tundra ecotone in the Andes, Tibet and Nepal, and northward shifts in the deciduous/boreal forest ecotones in Canada. Globally, woody encroachment into open areas (grasslands, arid regions and tundra) is likely being driven by climate change and increased CO2, in concert with changes in grazing and fire regimes (medium confidence) (Section 2.4.3).

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Climate change has driven, or is contributing to, increased tree mortality directly through increased aridity and droughts and indirectly through increased wildfires and insect pests in many locations (high confidence). Analyses of causal factors have attributed increasing tree mortality at sites in Africa and North America to anthropogenic climate change, and field evidence has detected tree mortality due to drought, wildfires and insect pests in temperate and tropical forests around the world (high confidence). Water stress, leading to plant hydraulic failure, is a principal mechanism of drought-induced tree mortality, along with the indirect effects of climate change mediated by community interactions (high confidence) (Section 2.4.4.3).

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Globally, increases in temperature, aridity and drought have increased the length of fire seasons and doubled the potentially burnable land area (medium confidence). Increases in the area burned have been attributed to anthropogenic climate change in North America (high confidence). In parts of Africa, Asia, Australia and South America, the area burned has also increased, consistent with anthropogenic climate change. Deforestation, peat-burning, agricultural expansion or abandonment, fire suppression and inter-decadal cycles strongly influence fire occurrence. The areas with the greatest increases in the length of the fire season include the Amazon, western North America, western Asia and East Africa (Section 2.4.4.2).

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Reforestation, either natural or anthropogenic, leads to summer cooling and winter warming of the ground, while forest thinning or removal by fire has reverse effects, deepening the upper layer that is free of permafrost (Stuenzi et al., 2021a). Interactions between permafrost and vegetation are important. For example, trees in the east Siberian taiga obtained water mostly from rain in wet summers and permafrost melt water in dry summers (Sugimoto et al., 2002), suggesting that these forests will be particularly vulnerable to the combination of drought with the retraction of permafrost further underground due to climate warming.

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Table 2.5 | Key risks to terrestrial and freshwater ecosystems from climate change. This IPCC chapter assesses these as the most fundamental risks of climate change to ecosystem integrity and the ecosystem services that support human well-being. Climate factors include the primary variables governing the risk. Non-climate factors include other phenomena that can dominate or contribute to the risk. Detection and attribution comprise cases of observed changes attributed predominantly, or in part, to climate change, with some cases being attributed to anthropogenic climate change (Sections 2.4.2, 2.4.3, 2.4.4, 2.4.5, Table 2.2, Table 2.3, Table SM2.1). Adaptation includes options to address the risk (Section 2.6). Risk transitions (defined in Figure 2.11) indicate an approximate GSAT increase, relative to the pre-industrial period (1850–1900), to move from one level of risk to the other as well as assessed confidence. Table SM2.5 provides details of the temperature levels for risk transitions. Both tables provides details for the key risk burning embers diagram (Figure 2.11).

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Low-probability events can have a very high impact (e.g., the transmission of SARS-CoV-2 from wild animals to humans, causing the Covid-19 pandemic ). A robust disease risk reduction policy would include utilising One Biosecurity (Meyerson and Reaser, 2002; Hulme, 2020; MacLeod and Spence, 2020) or One Health (Monath et al., 2010; Deem et al., 2018; Destoumieux-Garzón et al., 2018; Zinsstag et al., 2018) approaches with actions to reduce disease risk across multiple sectors and from a variety of anthropogenic drivers, including climate change, even if there is high uncertainty in projected risk (see Cross-Chapter Boxes ILLNESS in this chapter, COVID in Chapter 7 and DEEP in Chapter 17). Kraemer et al. (2019) found that vector importation was a key risk factor and that the focus should be on preventing the introduction of invasive species. Furthermore, many neglected tropical diseases (NTDs) are also VBDs, and the UN SDG of good health and well-being explicitly calls for increased control and intervention with a focus on emergency preparedness and response (Stensgaard et al., 2019a).

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Joshua Tree National Park conserves 3200 km 2 of the Mojave and Sonoran Desert ecosystems. The climate of the national park is arid, with an average summer temperature of 27.3°C ± 0.7°C and average annual precipitation of 170 ± 80 mm yr -1 in the period 1971–2000 (Gonzalez et al., 2018). From 1895 to 2017, the average annual temperature increased at a significant (P < 0.0001) rate of 1.5°C ± 0.1°C per century and the average annual precipitation decreased at a significant (P = 0.0174) rate of -32 ± 12% per century (Gonzalez et al., 2018). Anthropogenic climate change accounts for half the magnitude of a 2000–2020 drought in the southwestern USA, the most severe since the 1500s (Williams et al., 2020).

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Detection and attribution efforts have increased since AR5, but there are some key impacts of high societal importance that would benefit from more detailed and sophisticated attribution studies. For example, while it is clear that diseases have altered considerably in both wild animals and humans in some areas (high confidence in detection), there are many regions that are under-studied, and few regions that provide robust assessments of the role of climate change, particularly with respect to human infectious diseases. While wildfire has been robustly linked to climate change in some regions, there are still a lack of attribution studies in some regions that have experienced large burns recently, and only one fire impact—the increase of the area burned by wildfire in western North America in the period 1984–2017 (Section 2.4.4.2.1) —has been formally attributed to anthropogenic climate change. Global changes in soil and freshwater ecosystem carbon over time remain uncertainties in global carbon stocks and changes (Section 2.4.4.4); due to the physical inability to conduct repeat-monitoring and the lack of remote sensing to scale up point measurements, no global methods can yet produce repeating spatial estimates of soil carbon stock changes.

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Williams, A. P. et al., 2019: Observed Impacts of Anthropogenic Climate Change on Wildfire in California. Earth’s Future, 7 (8), 892–910, doi:10.1029/2019ef001210.

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Williams, A. P. et al., 2020: Large contribution from anthropogenic warming to an emerging North American megadrought. Science, 368 (6488), 314-+, doi:10.1126/science.aaz9600.

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Williams, A. P. et al., 2015a: Contribution of anthropogenic warming to California drought during 2012–2014. Geophysical Research Letters, 42 (16), 6819–6828, doi:10.1002/2015gl064924.

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The ocean sustains life on Earth by providing essential resources and modulating planetary flows of energy and materials. Together, harvests from the ocean and inland waters provide more than 20% of dietary animal protein for more than 3.3 billion people worldwide and livelihoods for about 60 million people (FAO, 2020b). The global ocean is centrally involved in sequestering anthropogenic atmospheric CO2 and recycling many elements, and it regulates the global climate system by redistributing heat and water (WGI AR6 Chapter 9; Fox-Kemper et al., 2021). The ocean also provides a wealth of aesthetic and cultural resources (Barbier et al., 2011), contains vast biodiversity (Appeltans et al., 2012), supports more animal biomass than on land (Bar-On et al., 2018) and produces at least half the world’s photosynthetic oxygen (Field et al., 1998). Ecosystem services (Annex II: Glossary) delivered by ocean and coastal ecosystems support humanity by protecting coastlines, providing nutrition and economic opportunities (Figure 3.1; Selig et al., 2019) and providing many intangible benefits. Even though ecosystem services and biodiversity underpin human well-being and support climate mitigation and adaptation (Pörtner et al., 2021b), there are also ethical arguments for preserving biodiversity and ecosystem functions regardless of the beneficiary (e.g., Taylor et al., 2020). This chapter assesses the impact of climate change on the full spectrum of ocean and coastal ecosystems, on their services and on related human activities, and it assesses marine-related opportunities within both ecological and social systems to adapt to climate change.

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Previous IPCC Assessment Reports (IPCC, 2014b; IPCC, 2014c; IPCC, 2018; IPCC, 2019b) have expressed growing confidence in the detection of climate-change impacts in the ocean and their attribution to anthropogenic greenhouse gas emissions. Heat and CO2 taken up by the ocean (high to very high confidence) (IPCC, 2021b) directly affect marine systems, and the resultant “climatic impact-drivers (CIDs) (e.g., ocean temperature and heatwaves, sea level, dissolved oxygen levels, acidification; Annex II: Glossary, WGI Figure SPM.9; IPCC, 2021b) also influence ocean and coastal systems (Section 3.2; Cross-Chapter Box SLR in Chapter 3; Cross-Chapter Box EXTREMES in Chapter 2; Figure 3.SM.1), from individual biophysical processes to dependent human activities. Several marine outcomes of CIDs are themselves drivers of ecological change (e.g., climate velocities, stratification, sea ice changes). This chapter updates and extends the assessment of SROCC (IPCC, 2019b) and WGI AR6 by assessing the ecosystem effects of the CIDs in WGI AR6 Figure SPM.9 (IPCC, 2021b) and their biologically relevant marine outcomes (detailed in Section 3.2), which are referred to collectively hereafter as ‘climate-induced drivers’4 .

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Detrimental human impacts on ocean and coastal ecosystems are not only caused by climate. Other anthropogenic activities are increasingly affecting the physical, chemical and biological conditions of the ocean (Doney, 2010; Halpern et al., 2019), and these ‘non-climate drivers 5 ’ also alter marine ecosystems and their services. Fishing and other extractive activities are major non-climate drivers in many ocean and coastal systems (Steneck and Pauly, 2019). Many activities, such as coastal development, shoreline hardening and habitat destruction, physically alter marine spaces (Suchley and Alvarez-Filip, 2018; Ducrotoy et al., 2019; Leo et al., 2019; Newton et al., 2020; Raw et al., 2020). Other human activities decrease water quality by overloading coastal water with terrestrial nutrients (eutrophication) and by releasing runoff containing chemical, biological and physical pollutants, toxins, and pathogens (Jambeck et al., 2015; Luek et al., 2017; Breitburg et al., 2018; Froelich and Daines, 2020). Some human activities disturb marine organisms by generating excess noise and light (Davies et al., 2014; Duarte et al., 2021), while others decrease natural light penetration into the ocean (Wollschläger et al., 2021). Several anthropogenic activities alter processes that span the land–sea interface by changing coastal hydrology or causing coastal subsidence (Michael et al., 2017; Phlips et al., 2020; Bagheri-Gavkosh et al., 2021). Atmospheric pollutants can harm marine systems or unbalance natural marine processes (Doney et al., 2007; Hagens et al., 2014; Lamborg et al., 2014; Ito et al., 2016). Organisms frequently experience non-climate drivers simultaneously with climate-induced drivers (Section 3.4), and feedbacks may exist between climate-induced drivers and non-climate drivers that enhance the effects of climate change (Rocha et al., 2015; Ortiz et al., 2018; Wolff et al., 2018; Cabral et al., 2019; Bowler et al., 2020; Gissi et al., 2021). SROCC assessed with high confidence that reduction of pollution and other stressors, along with protection, restoration and precautionary management, supports ocean and coastal ecosystems and their services (IPCC, 2019b). This chapter examines the combined influence of climate-induced drivers and primary non-climate drivers on many ecosystems assessed.

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This chapter assesses the current understanding of climate-induced drivers, ecological vulnerability and adaptability, risks to coastal and ocean ecosystems, and human vulnerability and adaptation to resulting changes in ocean benefits, now and in the future (Figure 3.2). It starts by assessing the biologically relevant outcomes of anthropogenic climate-induced drivers (Section 3.2). Next, it sets out the mechanisms that determine the responses of ocean and coastal organisms to individual and combined drivers from the genetic to the ecosystem level (Section 3.3). This supports a detailed assessment of the observed and projected responses of coastal and ocean ecosystems to these hazards, placing them in context using the paleorecord (Section 3.4). These observed and projected impacts are used to quantify consequent risks to delivery of ecosystem services and the socioeconomic sectors that depend on them, with attention to the vulnerability, resilience and adaptive capacity of social–ecological systems (Section 3.5). The chapter concludes by assessing the state of adaptation and governance actions available to address these emerging threats while also advancing human development (Section 3.6). Abbreviations used repeatedly in the chapter are defined in Table 3.1.

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Marine heatwaves (MHWs) are periods of extreme seawater temperature relative to the long-term mean seasonal cycle, that persist for days to months, and that may carry severe consequences for marine ecosystems and their services (WGI AR6 Box 9.2; Hobday et al., 2016a; Smale et al., 2019; Fox-Kemper et al., 2021). MHWs became more frequent over the 20th century (high confidence) and into the beginning of the 21st century, approximately doubling in frequency (high confidence) and becoming more intense and longer since the 1980s (medium confidence) (WGI AR6 Box 9.2; Fox-Kemper et al., 2021). These trends in MHWs are explained by an increase in ocean mean temperatures (Oliver et al., 2018), and human influence has very likely contributed to 84–90% of them since at least 2006 (WGI AR6 Box 9.2; Fox-Kemper et al., 2021). The probability of occurrence (as well as duration and intensity) of the largest and most impactful MHWs that have occurred in the past 30 years has increased more than 20-fold due to anthropogenic climate change (Laufkötter et al., 2020).

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Most coastal ecosystems (mangroves, seagrasses, salt marshes, shallow coral reefs, rocky shores and sandy beaches) are affected by changes in relative sea level (RSL, the change in the mean sea level relative to the land; Section 3.4.2). Regional rates of RSL rise differ from the global mean due to a range of factors, including local subsidence driven by anthropogenic activities such as groundwater and hydrocarbon extraction (WGI AR6 Box 9.1; Fox-Kemper et al., 2021). In many deltaic regions, anthropogenic subsidence is currently the dominant driver of RSL rise (WGI AR6 Section 9.6.3.2; Tessler et al., 2018; Fox-Kemper et al., 2021). RSL rise is driving a global increase in the frequency of extreme sea levels (high confidence) (WGI AR6 Section 9.6.4.1; Fox-Kemper et al., 2021).

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Continuous observation of the Atlantic meridional overturning circulation (AMOC) has improved the understanding of its variability (Frajka-Williams et al., 2019), but there is low confidence in the quantification of AMOC changes in the 20th century because of low agreement in quantitative reconstructed and simulated trends (WGI AR6 Sections 2.3.3, 9.2.3.1; Fox-Kemper et al., 2021; Gulev et al., 2021). Direct observational records since the mid-2000s remain too short to determine the relative contributions of internal variability, natural forcing and anthropogenic forcing to AMOC change (high confidence) (WGI AR6 Sections 2.3.3, 9.2.3.1; Fox-Kemper et al., 2021; Gulev et al., 2021). Over the 21st century, AMOC will very likely decline for all SSP scenarios but will not involve an abrupt collapse before 2100 (WGI AR6 Sections 4.3.2, 9.2.3.1; Fox-Kemper et al., 2021; Lee et al., 2021).

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The ocean’s uptake of anthropogenic carbon affects its chemistry in a process referred to as ocean acidification, which increases the concentrations of aqueous CO2, bicarbonate and hydrogen ions, and decreases pH, carbonate ion concentrations and calcium carbonate mineral saturation states (Doney et al., 2009). Ocean acidification affects a variety of biological processes with, for example, lower calcium carbonate saturation states reducing net calcification rates for some shell-forming organisms and higher CO2 concentrations increasing photosynthesis for some phytoplankton and macroalgal species (Section 3.3.2).

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Ocean acidification is also developing in the ocean interior (very high confidence) due to the transport of anthropogenic CO2 to depth by ocean currents and mixing (WGI AR6 Section 5.3.3.1; Canadell et al., 2021). There, it leads to the shoaling of saturation horizons of aragonite and calcite (high confidence) (WGI AR6 Section 5.3.3.1; Canadell et al., 2021), below which dissolution of these calcium carbonate minerals is thermodynamically favoured. The calcite or aragonite saturation horizons have migrated upwards in the North Pacific (1–2 m yr –1 over 1991–2006) (Feely et al., 2012) and in the Irminger Sea (10–15 m yr –1 for the aragonite saturation horizon over 1991–2016) (Perez et al., 2018). In some locations of the western Atlantic Ocean, calcite saturation depth has risen by ~300 m since the pre-industrial era due to increasing concentrations of deep-ocean dissolved inorganic carbon (Sulpis et al., 2018). In the Arctic, where some coastal surface waters are already undersaturated with respect to aragonite due to the degradation of terrestrial organic matter (Mathis et al., 2015; Semiletov et al., 2016), the deep aragonite saturation horizon shoaled on average 270 ± 60 m during 1765–2005 (Terhaar et al., 2020).

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Detection and attribution of ocean acidification in coastal environments are more difficult than in the open ocean due to larger spatio-temporal variability of carbonate chemistry (Duarte et al., 2013; Laruelle et al., 2017; Torres et al., 2021) and to the influence of other natural acidification drivers such as freshwater and high-nutrient riverine inputs (Cai et al., 2011; Laurent et al., 2017; Fennel et al., 2019; Cai et al., 2020) or anthropogenic acidification drivers (Section 3.1) like atmospherically deposited nitrogen and sulphur (Doney et al., 2007; Hagens et al., 2014). Since AR5, the observing network in coastal oceans has expanded substantially, improving understanding of both the drivers and amplitude of observed variability (Sutton et al., 2016). Recent studies indicate that two more decades of observations may be required before anthropogenic ocean acidification emerges over natural variability in some coastal sites and regions (WGI AR6 Section 5.3.5.2; Sutton et al., 2019; Turk et al., 2019; Canadell et al., 2021).

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Ocean deoxygenation, the loss of oxygen in the ocean, results from ocean warming, through a reduction in oxygen saturation, increased oxygen consumption, increased ocean stratification and ventilation changes (Keeling et al., 2010; IPCC, 2019a). In recent decades, anthropogenic inputs of nutrients and organic matter (Section 3.1) have increased the extent, duration and intensity of coastal hypoxia events worldwide (Diaz and Rosenberg, 2008; Rabalais et al., 2010; Breitburg et al., 2018), while pollution-induced atmospheric deposition of soluble iron over the ocean has accelerated open-ocean deoxygenation (Ito et al., 2016). Deoxygenation and acidification often coincide because biological consumption of oxygen produces CO2. Deoxygenation can have a range of detrimental effects on marine organisms and reduce the extent of marine habitats (Sections 3.3.2, 3.4.3.1; Vaquer-Sunyer and Duarte, 2008; Chu and Tunnicliffe, 2015).

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Anthropogenic changes in climate-induced drivers assessed here exhibit vastly distinct times of emergence, which is the time scale over which an anthropogenic signal related to climate change is statistically detected to emerge from the background noise of natural climate for a specific region (Christensen et al., 2007; Hawkins and Sutton, 2012). SROCC concluded that for ocean properties, the time of emergence ranges from under a decade (e.g., surface ocean pH) to over a century (e.g., net primary production; see Section 3.4.3.3.4 for time of emergence of biological properties; Bindoff et al., 2019a).

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The literature assessed in SROCC mainly focused on surface ocean properties and gradual mean changes. Since then, the time of emergence has also been investigated for subsurface properties, ocean extreme events and particularly vulnerable regions, such as the Arctic Ocean (Hameau et al., 2019; Oliver et al., 2019; Burger et al., 2020; Landrum and Holland, 2020; Schlunegger et al., 2020), but subsequent assessments are low confidence due to limited evidence. Below the surface, changes in temperature typically emerge from internal variability prior to changes in oxygen; however, in about a third of the global thermocline, deoxygenation emerges prior to warming (Hameau et al., 2019). Permanent MHW states, defined as when SST exceeds the MHW threshold continuously over a full calendar year, will emerge during the 21st century in many parts of the surface ocean (Oliver et al., 2019). Ocean acidification extremes have already emerged from background natural internal variability during the 20th century in most of the surface ocean (Burger et al., 2020). In the Arctic, anthropogenic sea ice changes have already emerged from the background internal variability, and anthropogenic alteration of air temperatures will emerge in the early- to mid-21st century (Landrum and Holland, 2020).

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Paleoclimatology observations are useful to assess multiple hazards of environmental change while excluding direct anthropogenic impacts (Section 3.4.3.3). Ancient intervals of rapid climate warming that occurred between 300 and 50 million years ago (Ma) were triggered by the release of greenhouse gases (high confidence). The sources of greenhouse gases varied but include volcanic degassing from continental flood basalts and methane hydrates stored in marine sediments and soils (Foster et al., 2018). Six extreme ancient hyperthermal events are known from the last 300 Ma, when tropical SSTs reached 1.5°C–10°C warmer than pre-industrial conditions, and with substantial impacts on ancient life (Cross-Chapter Box PALEO in Chapter 1). Warming and deoxygenation in the oceans were closely associated in hyperthermal events (high confidence), with anoxia reaching the photic zone and abyssal depths (Kaiho et al., 2014; Müller et al., 2017; Penn et al., 2018; Weissert, 2019), whereas ocean acidification has not been demonstrated consistently (medium confidence) (Hönisch et al., 2012; Penman et al., 2014; Clarkson et al., 2015; Harper et al., 2020a; Jurikova et al., 2020; Müller et al., 2020).

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Anthropogenic CO2 emissions trigger a suite of changes that alter ocean temperature, pH and CO2 concentration, oxygen concentration and nutrient supply at global scales (Section 3.2). The response pathways of these climate-induced drivers have been investigated primarily as single variables.

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Multi-species and integrated end-to-end ecosystem models are powerful tools to explore and project outcomes to the often-interacting cumulative effects of climate change and other anthropogenic drivers (Section 3.1; Kaplan and Marshall, 2016; Koenigstein et al., 2016; Peck and Pinnegar, 2018; Tittensor et al., 2018; Gissi et al., 2021). These models can integrate some aspects of the knowledge accrued from manipulation experiments, paleo- and contemporary observations, help test the relative importance of specific drivers and driver combinations, and identify synergistic or antagonistic responses (Koenigstein et al., 2016; Payne et al., 2016; Skogen et al., 2018; Tittensor et al., 2018). As these models are associated with wide-ranging uncertainties (SM3.2.2; Payne et al., 2016; Trolle et al., 2019; Heneghan et al., 2021), they cannot be expected to accurately project the trajectories of complex marine ecosystems under climate change; hence, they are most useful for assessing overall trends and in particular for providing a plausible envelope of trajectories across a range of assumptions (Fulton et al., 2018; Peck et al., 2018 ; Tittensor et al., 2018). On a global scale, ecosystem models project a −5.7 ± 4.1% (very likely range) to −15.5 ± 8.5% decline in marine animal biomass with warming under SSP1-2.6 and SSP5-8.5, respectively, by 2080–2099 relative to 1995–2014, albeit with significant regional variation in both trends and uncertainties (medium confidence) (Section 3.4.3.4; Tittensor et al., 2021). Biological interactions may exacerbate or buffer the projected impacts. For instance, trophic amplification (strengthening of responses to climate-induced drivers at higher trophic levels) may result from combined direct and indirect food-web-mediated effects (medium confidence) (Section 3.4.3.4; Lotze et al., 2019). Alternatively, compensatory species interactions can dampen strong impacts on species from ocean acidification, resulting in weaker responses at functional-group or community level than at species level (medium confidence) (Marshall et al., 2017; Hoppe et al., 2018b; Olsen et al., 2018; Gissi et al., 2021). Globally, the projected reduction of biomass due to climate-induced drivers is relatively unaffected by fishing pressure, indicating additive responses of fisheries and climate change (low confidence) (Lotze et al., 2019). Regionally, projected interactions of climate-induced drivers, fisheries and other regional non-climate drivers can be both synergistic and antagonistic, varying across regions, functional groups and species, and can cause nonlinear dynamics with counterintuitive outcomes, underlining the importance of adaptations and associated trade-offs (high confidence) (Sections 3.5.3, 3.6.3.1.2, 4.5, 4.6; Weijerman et al., 2015; Fulton et al., 2018; Hansen et al., 2019; Trolle et al., 2019; Zeng et al., 2019; Holsman et al., 2020; Pethybridge et al., 2020; Gissi et al., 2021).

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Given the limitations of individual ecological models discussed above, model intercomparisons, such as the Fisheries and Marine Ecosystem Model Intercomparison Project (Fish-MIP; Tittensor et al., 2018) show promise in increasing the robustness of projected ecological outcomes (Tittensor et al., 2018). Model ensembles include a greater number of relevant processes and functional groups than any single model and thus capture a wider range of plausible responses. Among the global Fish-MIP models, there is high (temperate and tropical areas) to medium agreement (coastal and polar regions) on the direction of change, but medium (temperate and tropical regions) to low agreement (coastal and polar regions) on magnitude of change (Lotze et al., 2019; Heneghan et al., 2021). Although model outputs are validated relative to observations to assess model skills (Payne et al., 2016; Tittensor et al., 2018), the Fish-MIP models under-represent some sources of uncertainty, as they often do not include parameter uncertainties and do not usually include impacts of ocean acidification, oxygen loss or evolutionary responses because there remains high uncertainty regarding the influences of these processes across functional groups. Ensemble model investigations like Fish-MIP have also identified gaps in our mechanistic understanding of ecosystems and their responses to anthropogenic forcing, leading to model improvement and more rigorous benchmarking. These investigations could inspire future targeted observational and experimental research to test the validity of model assumptions (Payne et al., 2016; Lotze et al., 2019; Heneghan et al., 2021). The state of the art in such experimental research is presented in Box 3.1.

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These observed and projected impacts are supported by geological and paleo-ecological evidence showing a decline in coral reef extent and species richness under previous episodes of climate change and ocean acidification (Kiessling and Simpson, 2011; Pandolfi et al., 2011; Kiessling et al., 2012; Pandolfi and Kiessling, 2014; Kiessling and Kocsis, 2015). Major reef crises in the past 300 million years were governed by hyperthermal events (medium confidence) (Section 3.2.4.4; Cross-Chapter Box PALEO in Chapter 1) longer in time scale than anthropogenic climate change, during which net coral reef accretion was more strongly affected than biodiversity (medium confidence).

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The sensitivity of salt marshes and mangroves to RSLR depends on whether they accrete inorganic sediment and/or organic material at rates equivalent to rising water levels (very high confidence) (Peteet et al., 2018; FitzGerald and Hughes, 2019; Friess et al., 2019; Gonneea et al., 2019; Leo et al., 2019; Marx et al., 2020; Saintilan et al., 2020). Otherwise, wetland ecosystems must migrate either inland or upstream, or face gradual submergence in deeper, increasingly saline water (very high confidence) (Section 3.4.2.4; Andres et al., 2019; Jones et al., 2019b; Cohen et al., 2020; Mafi-Gholami et al., 2020; Magolan and Halls, 2020; Sklar et al., 2021). Ability to migrate depends on local topography, the positioning of anthropogenic infrastructure and structures placed to defend such infrastructure (Schuerch et al., 2018; Fagherazzi et al., 2020; Cahoon et al., 2021). Submergence drives changes in community structure (high confidence) (Jones et al., 2019b; Yu et al., 2019; Douglass et al., 2020; Langston et al., 2020) and functioning (high confidence) (Charles et al., 2019; Buffington et al., 2020; Stein et al., 2020), and will eventually lead to extirpation of the most sensitive vegetation (medium confidence) (Schepers et al., 2017; Scalpone et al., 2020) and associated animals (low confidence) (Rosencranz et al., 2018). The impacts of storms on wetlands are variable and described in SM3.3.1.

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As noted in SROCC, given the diversity of coastal wetlands as well as the dependence of their future vulnerability to climate change on adaptation pathways (Krauss, 2021; Rogers, 2021), projections of future impacts based on shoreline elevation estimated from satellite data and CMIP5 projections (Spencer et al., 2016; Schuerch et al., 2018) vary greatly. Although all approaches have individual strengths and weaknesses (Törnqvist et al., 2021), paleorecords provide some clarity because they yield estimates of wetland responses to changes in climate in the absence of other anthropogenic drivers and are therefore inherently conservative. On the basis of paleorecords (Table 3.8), we assess that mangroves and salt marshes are likely at high risk from future SLR, even under SSP1-1.9, with impacts manifesting in the mid-term (medium confidence). Under SSP5-8.5, wetlands are very likely at high risk from SLR, with larger impacts manifesting before 2040 (medium confidence). By 2100, these ecosystems are at high risk of impacts under all scenarios except SSP1-1.9 (high confidence), with impacts most severe along coastlines with gently sloping shorelines, limited sediment inputs, small tidal ranges and limited space for inland migration (very high confidence) (Cross-Chapter Box SLR in Chapter 3; Schuerch et al., 2018; FitzGerald and Hughes, 2019; Leo et al., 2019; Schuerch et al., 2019; Raw et al., 2020; Saintilan et al., 2020).

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Since SROCC, observed trends in coastal erosion continue to be obscured by beach nourishment that replaces eroded sediment or by coastal protection of areas at risk of erosion (Section 3.6.3.1.1; Cross-Chapter Box SLR in Chapter 3). Nevertheless, RSLR, increases in wave energy and/or changes in wave direction, disruptions to sediment supplies (including sand mining) and other anthropogenic modifications of the coast have driven localised beach erosion (very high confidence) at rates up to 0.5–3 m yr –1 (Vitousek et al., 2017a; Vitousek et al., 2017b; Cambers and Wynne, 2019; Enríquez-de-Salamanca, 2020; Sharples et al., 2020). Corresponding analyses of coarse-scale (30-m resolution) global data estimate that 15% of tidal flats (including beaches) have been lost since 1984 (medium confidence) (Mentaschi et al., 2018; Murray et al., 2019) but with a corresponding number of the world’s beaches accreting (28%) as eroding (24%) (medium confidence) (Luijendijk et al., 2018).

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Progress is being made towards models that can project beach erosion under future scenarios despite inherent uncertainties and the presence of multiple confounding drivers in the coastal zone (Vitousek et al., 2017b; Le Cozannet et al., 2019; Cooper et al., 2020a; Vousdoukas et al., 2020b; Vousdoukas et al., 2020a). In the interim, models with varying levels of complexity estimate local loss of beach area to SLR by 2100 under RCP8.5-like scenarios, assuming minimal human intervention, ranging 30–70% (low confidence) (Vitousek et al., 2017b; Mori et al., 2018; Ritphring et al., 2018; Hallin et al., 2019; Kasmi et al., 2020). Within regions, projected impacts scale negatively with beach width and positively with the magnitude of projected SLR. None of these local studies, however, considered high-energy storm events, which are known to also impact sandy coasts (high confidence) (e.g., Burvingt et al., 2018; Garrote et al., 2018; Duvat et al., 2019; Sharples et al., 2020), and model structure often had more influence on projected shoreline responses than did physical drivers (Le Cozannet et al., 2019). Nevertheless, the most-advanced available models, which incorporate multiple coastal processes, including SLR, project that without anthropogenic barriers to erosion, 13.6–15.2% and 35.7–49.5% of the world’s beaches likely risk undergoing at least 100 m of shoreline retreat (relative to 2010) by 2050 and 2100, respectively (low confidence) (Vousdoukas et al., 2020b). Aggregating these trends regionally suggests that relative rates of shoreline change under RCP4.5 and RCP8.5 diverge strongly after mid-century, with trends towards erosion escalating under RCP8.5 by 2100 (medium confidence) (Figure 3.14; Vousdoukas et al., 2020b). This trend supports the WGI AR6 assessment that projected SLR will contribute to erosion of sandy beaches, especially under high-emissions futures (high confidence) (WGI AR6 Technical Summary; Arias et al., 2021).

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Although upwelling is important in many other oceanic regions, we focus here on the most documented examples provided by the EBUS. Yet even here, observed changes in upwelling, temperature, acidification and loss of oxygen (Seabra et al., 2019; Abrahams et al., 2021; Gallego et al., 2021; Varela et al., 2021) cannot be robustly attributed to anthropogenic climate change, and projected future changes in upwelling are expected to be relatively small and variable among and within EBUS (Section 3.2.2.3; WGI AR6 Chapter 9; Fox-Kemper et al., 2021). We therefore have few updates to assessments provided by AR5 and SROCC (Table 3.13) and restrict our brief assessment to the limited amount of new evidence (Figure 3.12).

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The California EBUS is arguably the best-studied of the four ecosystems in terms of robust projections of climate change, although even here, there is limited evidence and low agreement among projections. For example, trends in outputs from high-resolution, downscaled models in the California EBUS generally reflect those from underlying coarser-scale ESMs, but projections for physical variables are more convergent among modelling approaches than are those for biogeochemical variables (high confidence) (Howard et al., 2020a; Pozo Buil et al., 2021). Models agree on general warming in the California EBUS, with concomitant declines in oxygen content (medium confidence) (Howard et al., 2020b; Fiechter et al., 2021; Pozo Buil et al., 2021). But implications for the future spatial distribution of species, including for some fisheries resources (Howard et al., 2020b; Fiechter et al., 2021), are confounded by local-scale oceanographic processes (Siedlecki et al., 2021) and by lateral input of anthropogenic land-based nutrients (Kessouri et al., 2021), suggesting that such projections should be accorded low confidence.

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The polar seas cover ~20% of the global ocean and include the deep Arctic Ocean and surrounding shelf seas as well as the Southern Ocean south of the polar front. They play a significant role in ocean circulation and absorption of anthropogenic CO2 (Meredith et al., 2019). The Arctic is characterised by polar seas surrounded by land, while the Antarctic comprises continental Antarctica surrounded by the Southern Ocean. These high-latitude ecosystems share key properties, including strong seasonality in solar radiation and sea ice coverage. Sea ice regulates water-column physics, chemistry and biology, air–sea exchange and is a critical habitat for many species. In spring, when solar radiation returns and sea ice melts, intense phytoplankton blooms fuel food webs that include rich communities of both resident and summer-migrant species, with typically high dependency on a few key species for trophic transfer (Meredith et al., 2019; Rogers et al., 2020). Over the past two decades, Arctic Ocean surface temperature has increased in line with the global average, while there has been no uniform warming across the Antarctic (high confidence) (WGI AR6 Chapter 9; Fox-Kemper et al., 2021). Thus, the rate of change due to warming, and associated sea ice loss, is greater in the Arctic than in the Antarctic (high confidence) (Section 3.2; Table 3.14; WGI AR6 Chapter 9; Fox-Kemper et al., 2021). Both Arctic and Antarctic regions have a long history of living resource extraction, including some of the largest fisheries on the globe in terms of catches. However, only the Arctic hosts human populations, holding a rich Indigenous knowledge and local knowledge (IKLK) on these social–ecological systems (Cross-Chapter Paper 6; Meredith et al., 2019).

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Recent MHWs (Section 3.2.2.1) have caused major ecosystem shifts and mass mortality in oceanic and coastal ecosystems, including corals, kelp forests and seagrass meadows (Sections 3.4.2.1, 3.4.2.3, 3.4.2.5, 3.4.2.6, 3.4.2.10; Cross-Chapter Box MOVING SPECIES in Chapter 5; Cross-Chapter Box EXTREMES in Chapter 2), with dramatic declines in species foundational for habitat formation or trophic flow, biodiversity declines, and biogeographic shifts in fish stocks (very high confidence) (Table 3.15; Cross-Chapter Box MOVING SPECIES in Chapter 5; Canadell and Jackson, 2021). Three major bleaching episodes on Australia’s Great Barrier Reef in 5 years corresponded with extreme temperatures in 2016, 2017 and 2020 (Pratchett et al., 2021). Between 1981 and 2017, MHWs have increased more than 20-fold due to anthropogenic climate change (Section 3.2.2.1; WGI AR6 Chapter 9; Laufkötter et al., 2020; Fox-Kemper et al., 2021), increasing the risk of abrupt ecosystem shifts (high confidence) (Figure 3.19a; Cross-Chapter Box EXTREMES in Chapter 2; van der Bolt et al., 2018; Garrabou et al., 2021; Wernberg, 2021).

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Anthropogenically driven changes in chlorophyll-a concentrations across an ensemble of 30 ESMs are expected to exceed natural variability under RCP8.5 by 2100 in ~65–80% of the global oceans, when the natural variability is calculated using the ensemble’s standard deviation (Schlunegger et al., 2020); however, if two standard deviations are used, then significant trends in chlorophyll-a concentration are expected under RCP8.5 across ~31% of the global oceans by 2100 (Dutkiewicz et al., 2019). In contrast, the anthropogenic signal in phytoplankton community structure, which has a lower natural variability, will emerge under RCP8.5 across 63% of the ocean by 2100 when two standard deviations are used (limited evidence) (Dutkiewicz et al., 2019).

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Increased emergence of infectious disease in mammals and birds is expected with ocean warming, due to new transmission pathways from changing species distributions, higher species densities caused by habitat loss and increased vulnerability due to environmental stress on individuals (limited evidence) (Sydeman et al., 2015; VanWormer et al., 2019; Sanderson and Alexander, 2020). Marine birds and mammals are likely to suffer from increased mortalities due to increasing frequencies of HABs, and of extreme weather, at sea, on sea ice, and in terrestrial breeding habitats (Broadwater et al., 2018; Gibble and Hoover, 2018; Ropert-Coudert et al., 2019; Grose et al., 2020). Also, climate-change driven distributional shifts have strengthened interactions with other anthropogenic impacts, through, for example, increasing risks of ship strikes and bycatch (medium confidence) (e.g., Hauser et al., 2018; Krüger et al., 2018; Record et al., 2019; Santora et al., 2020).

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Climate change and other anthropogenic drivers, including eutrophication, land-use changes and overexploitation, directly and indirectly threaten blue carbon ecosystems (Annex II: Glossary). Commonly considered blue carbon ecosystems include vegetated coastal ecosystems (Sections 3.4.2.3–3.4.2.5), whose mangroves, salt marshes and seagrass beds host rooted, vascular plants known to store large amounts of carbon for long periods and to be amenable to management (Lovelock and Duarte, 2019). Other ocean and coastal taxa, including rooted or floating macroalgae (e.g., non-vascular multicellular kelp or seaweed genera such as Macrocystis spp., Sargassum spp. or Laminaria spp. (Filbee-Dexter and Wernberg, 2020), phytoplankton and even pelagic fauna (e.g., finfish or whales; Chami et al., 2019), have also been proposed as blue carbon ecosystems. Terrestrial vascular-plant-derived material can also carry and store significant amounts of carbon in marine environments (Cragg et al., 2020). There is increasing evidence about the coverage and carbon content of macroalgal, planktonic and faunal taxa, but low agreement about their long-term carbon-storage potential and manageability (Alongi, 2018b; Wernberg and Filbee-Dexter, 2018; Lovelock and Duarte, 2019; Ortega et al., 2019; Pfister et al., 2019; Queirós et al., 2019; Filbee-Dexter et al., 2020a; Gallagher, 2020; Mariani et al., 2020; Thorhaug et al., 2020; van Son et al., 2020; Bach et al., 2021; Bayley et al., 2021; Cavanagh et al., 2021; Frontier et al., 2021; Martin et al., 2021; Pedersen et al., 2021; Weigel and Pfister, 2021). This section focuses on the array of ecosystem services and adaptation opportunities provided by vegetated coastal blue carbon ecosystems, where consensus and evidence are most abundant. Mitigation potential of blue carbon ecosystems is assessed with land-based mitigation options in WGIII AR6 Section 7.4.

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Barton, A.D., A.J. Irwin, Z.V. Finkel and C.A. Stock, 2016: Anthropogenic climate change drives shift and shuffle in North Atlantic phytoplankton communities. Proc. Natl. Acad. Sci. U.S.A. , 113 (11), 2964–2969, doi:10.1073/pnas.1519080113.

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Cai, W.-J., et al., 2021: Natural and anthropogenic drivers of acidification in large estuaries. Annu. Rev. Mar. Sci. , 13 (1), 23–55, doi:10.1146/annurev-marine-010419-011004.

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Doney, S.C., et al., 2007: Impact of anthropogenic atmospheric nitrogen and sulfur deposition on ocean acidification and the inorganic carbon system. Proc. Natl. Acad. Sci. U.S.A. , 104 (37), 14580–14585, doi:10.1073/pnas.0702218104.

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Duarte, C.M., et al., 2013: Is ocean acidification an open-ocean syndrome? Understanding anthropogenic impacts on seawater pH. Estuaries Coasts, 36 (2), 221–236, doi:10.1007/s12237-013-9594-3.

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Fox, L., S. Stukins, T. Hill and C.G. Miller, 2020: Quantifying the effect of Anthropogenic climate change on calcifying plankton. Sci. Rep. , 10 (1), 1620, doi:10.1038/s41598-020-58501-w.

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Gattuso, J.-P., et al., 2015: Contrasting futures for ocean and society from different anthropogenic CO2 emissions scenarios. Science, 349 (6243), aac4722, doi:10.1126/science.aac4722.

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Hameau, A., J. Mignot and F. Joos, 2019: Assessment of time of emergence of anthropogenic deoxygenation and warming: insights from a CESM simulation from 850 to 2100 CE. Biogeosciences, 16 (8), 1755–1780, doi:10.5194/bg-16-1755-2019.

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Hassoun, A.E.R., et al., 2015: Acidification of the Mediterranean Sea from anthropogenic carbon penetration. Deep Sea Res. Part I Oceanogr. Res. Pap. , 102, 1–15, doi:10.1016/j.dsr.2015.04.005.

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Kane, H.H. and C.H. Fletcher, 2020: Rethinking reef island stability in relation to Anthropogenic sea level rise. Earth’s Future, 8 (10), e2020EF001525, doi:10.1029/2020EF001525.

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Lamborg, C.H., et al., 2014: A global ocean inventory of anthropogenic mercury based on water column measurements. Nature, 512 (7512), 65–68, doi:10.1038/nature13563.

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McKenzie, L.J. and R.L. Yoshida, 2020: Over a decade monitoring Fiji’s seagrass condition demonstrates resilience to anthropogenic pressures and extreme climate events. Mar. Pollut. Bull. , 160, 111636, doi:10.1016/j.marpolbul.2020.111636.

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McMahon, K.W., et al., 2019: Divergent trophic responses of sympatric penguin species to historic anthropogenic exploitation and recent climate change. Proc. Natl. Acad. Sci. U.S.A. , 116 (51), 25721–25727, doi:10.1073/pnas.1913093116.

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McNeil, B.I. and T.P. Sasse, 2016: Future ocean hypercapnia driven by anthropogenic amplification of the natural CO2 cycle. Nature, 529 (7586), 383–386, doi:10.1038/nature16156.

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Monaco, C.J., et al., 2021: Natural and anthropogenic climate variability shape assemblages of range-extending coral-reef fishes. J. Biogeogr. , 48 (5), 1063–1075, doi:10.1111/jbi.14058.

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Nagelkerken, I., Doney S.C., and Munday, P.L., 2019: Consequences of Anthropogenic changes in the sensory landscape of marine animals. In: Oceanography and Marine Biology[Hawkins, S.J., et al.(ed.)]. CRC Press, Leiden, The Netherlands, pp. 229–263. ISBN 978-0429026379.

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Newton, A., et al., 2020: Anthropogenic, direct pressures on coastal wetlands. Front. Ecol. Evol. , 8, 144, doi:10.3389/fevo.2020.00144.

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Orr, J.C., et al., 2005: Anthropogenic ocean acidification over the twenty-first century and its impact on calcifying organisms. Nature, 437 (7059), 681–686, doi:10.1038/nature04095.

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Paerl, H.W., et al., 2016: Mitigating cyanobacterial harmful algal blooms in aquatic ecosystems impacted by climate change and anthropogenic nutrients. Harmful Algae, 54, 213–222, doi:10.1016/j.hal.2015.09.009.

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Schlunegger, S., et al., 2019: Emergence of anthropogenic signals in the ocean carbon cycle. Nat. Clim. Change, 9 (9), 719–725, doi:10.1038/s41558-019-0553-2.

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Strauss, B.H., et al., 2021: Economic damages from Hurricane Sandy attributable to sea level rise caused by anthropogenic climate change. Nat. Commun. , 12 (1), 2720, doi:10.1038/s41467-021-22838-1.

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Sulpis, O., et al., 2018: Current CaCO3 dissolution at the seafloor caused by anthropogenic CO2. Proc. Natl. Acad. Sci. U.S.A. , 115 (46), 11700–11705, doi:10.1073/pnas.1804250115.

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Sutton, A.J., et al., 2019: Autonomous seawater pCO2 and pH time series from 40 surface buoys and the emergence of anthropogenic trends. Earth Syst. Sci. Data, 11 (1), 421–439, doi:10.5194/essd-11-421-2019.

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Sutton, A.J., et al., 2016: Using present-day observations to detect when anthropogenic change forces surface ocean carbonate chemistry outside preindustrial bounds. Biogeosciences, 13 (17), 5065–5083, doi:10.5194/bg-13-5065-2016.

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The Sixth Assessment Report (AR6) Working Group I (WGI) (Douville et al., 2021) concluded that anthropogenic climate change has increased atmospheric moisture and precipitation intensity (very likely by 2–3% per 1°C) (high confidence), increased terrestrial ET (medium confidence) and contributed to drying in dry summer climates including in the Mediterranean, southwestern Australia, southwestern South America, South Africa and western North America (medium to high confidenc e), and has caused earlier onset of snowmelt and increased melting of glaciers (high confidence) since the mid-20th century. The report also stated with high confidence that the water cycle variability and extremes are projected to intensify, regardless of the mitigation policy. The share of the global population affected by water-related hazards and water availability issues is projected to increase with the intensification of water cycle variability and extremes. They concluded with high confidence that strong and rapid mitigation initiatives are needed to avert the manifestation of climate change in all components of the global water cycle.

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Using a Water Scarcity Index (WSI) defined as the ratio of demand and availability, accounting for EFRs, it is estimated that 4 billion people live under conditions of severe water scarcity for at least one month per year (Figure Box 4.1.1a; Mekonnen and Hoekstra, 2016). Nearly half of these people live in India and China. Although regions with high water scarcity are already naturally dry (virtually certain2), 1 human influence on climate is leading to reduced water availability in many regions. It is very likely that global patterns of soil moisture change are being driven by human influence on climate, and an overall global decline in soil moisture is attributable to greenhouse forcing [4.2.1.3]. Climate change patterns of streamflow change include declines in western North America, northeast South America, the Mediterranean and South Asia (medium confidence) [4.2.3]. However, quantification of the contribution of anthropogenic climate change to current levels of water scarcity is not yet available.

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WGI (Douville et al., 2021) conclude with high confidence that global terrestrial annual ET has increased since the early 1980s, driven by both increasing atmospheric water demand and vegetation greening (medium confidence), and can be partly attributed to anthropogenic forcing (high confidence).

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AR6 WGI (Douville et al., 2021) find that a global trend in soil moisture is detectable in a reanalysis and is attributable to GHG forcing, and conclude that it is very likely that anthropogenic climate change affected global patterns of soil moisture over the 20th century.

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AR5 (Jiménez Cisneros et al., 2014) concluded with medium evidence and high agreement that trends in annual streamflow have generally followed observed changes in regional precipitation and temperature since the 1950s. AR6 WGI (Eyring et al., 2021; Gulev et al., 2021) (12.4.5) conclude with medium confidence that anthropogenic climate change has altered local and regional streamflow in various parts of the world, but with no clear signal in the global mean.

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The spatial differences in annual mean streamflow trends around the world are influenced by climatic factors, particularly changes in precipitation and evaporation (Zang and Liu, 2013; Greve et al., 2014; Hannaford, 2015; Ficklin et al., 2018), as well as by anthropogenic forcing (Gudmundsson et al., 2016; 2017; 2021).. Other factors (e.g., land use change and CO2 effects on vegetation) dominate in some areas, especially dryland regions (Berghuijs et al., 2017b). Human activities can reduce runoff through water withdrawal and land use changes (Zaherpour et al., 2018; Sun et al., 2019a; Vicente-Serrano et al., 2019), and human regulation of streamflows via impounding reservoirs can also play a major role (Hodgkins et al., 2019).

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Streamflow trends are attributed to varying combinations of climate change and direct human influence through water and land use in different basins worldwide, with conclusions on the relative contribution of climatic and anthropogenic factors sometimes depending on the methodology (Dey and Mishra, 2017). Precipitation explains over 80% of the changes in discharge of large rivers from 1950 to 2010 in northern Asia and northern Europe, where the impact of human activities is relatively limited (Li et al., 2020b). In northwest Europe, precipitation and evaporation changes explain many observed trends in streamflow (Vicente-Serrano et al., 2019). In several polar areas in northern Europe (e.g., Finland), North America (e.g., British Columbia in Canada) and Siberia, many studies reported increased winter streamflow primarily due to climate warming, for instance, more rainfall instead of snowfall and more glacier runoff in the winter period (e.g., Bonsal et al., 2020) (Section 4.2.2). A similar phenomenon of the earlier snowmelt runoff is also found in North America during 1960–2014 (Dudley et al., 2017). Thus, climate drivers largely explain changes in the average and maximum runoff of predominantly snow-fed rivers (Yang et al., 2015 a; Bring et al., 2016; Tananaev et al., 2016; Frolova et al., 2017b; Ficklin et al., 2018; Magritsky et al., 2018; Rets et al., 2018).

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Shi et al. (2019) found that in 40 major basins worldwide, both climatic and direct human impact contribute to observed flow changes to varying degrees. Climate change or variability is the main contributor to changes in basin-scale trends for 75% of rivers, while direct human effects on streamflow dominate for 25%. However, this does not consider attribution of the climate drivers to anthropogenic forcing. Using time series of low, mean and high river flows from 7250 observatories around the world (1971–2010) and global hydrological models (GHMs) driven by Earth System Model (ESM) simulations with and without anthropogenic forcing of climate change, Gudmundsson et al. (2021) also found direct human influence to have a relatively small impact on global patterns of streamflow trends. Gudmundsson et al. (2021) further identified anthropogenic climate change as a causal driver of the global pattern of recent trends in mean and extreme river flow (Figure 4.7). Overall, the sign of observed trends and simulations accounting for human influence on the climate system was found to be consistent for decreased mean flows in western and eastern North America, southern Europe, northeast South America and the Indian sub-continent, and increased flows in northern Europe. Similar conclusions were drawn for low and high flows, except for the Indian sub-continent. However, in some regions, the observed trend was opposite to that simulated with anthropogenic climate forcing. Thus, human water and land use alone did not explain the observed pattern of trends.

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In summary, both climate change and human activities influence the magnitude and direction of change in runoff and streamflow. There are no clear trends of changing streamflow on the global level. However, trends emerge on a regional level (a general increasing trend in the northern higher latitude region and mixed trend in the rest of the word) (high confidence). Climatic factors contribute to these trends in most basins (high confidence). They are more important than direct human influence in a larger share of major global basins (medium confidence), although direct human influence dominates in some (medium confidence). Overall, anthropogenic climate change is attributed as a driver to the global pattern of change in streamflow (medium confidence).

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The link between rainfall and flooding is complex. While observed increases in extreme precipitation have increased the frequency and magnitude of pluvial floods and river floods in some regions, floods could decrease in some regions due to other factors. These factors could include soil wetness condition, cryospheric change, land cover change and river system management, adaptation measures or water usage within the river basin (WGI FAQ8.2). For example, in the USA and Europe, a study indicated that major (e.g., 25–100-year return period) floods did not show significant long-term trends (Hodgkins et al., 2019). Nevertheless, anthropogenic climate change increased the likelihood of a number of major heavy precipitation events and floods that resulted in disastrous impacts in southern and eastern Asia, Europe, North America and South America (Table 4.3) (high confidence). Davenport et al. (2021) demonstrated that anthropogenic changes in precipitation extremes had contributed one third of the cost of flood damages (from 1988 to 2017) in the USA. Anthropogenic climate change has altered 64% (eight out of 22 events increased, eight decreased) of floods events with significant losses and damages during 2010–2013 (Hirabayashi et al., 2021a). Gudmundsson et al. (2021) attributed observed change in extreme river flow trends to anthropogenic climate change (Section 4.2.3). Although there is growing evidence on the effects of anthropogenic climate change on each event, given the relatively poor regional coverage and high model uncertainty, there is low confidence in the attribution of human-induced climate change to flood change on the global scale.

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Table 4.3 | Selected major heavy-precipitation events from 2014 to 2021 that led to flooding and their impacts. Studies were selected for presentation based on the availability of scientific literature with impacts information and do not necessarily represent the most severe events. Impactful events are included even if not found to have a component attributable to climate change. This is not a systematic assessment of event attributions studies and their physical science conclusions. ‘Sign of influence’ indicates whether anthropogenic climate change was found to have made the event more or less likely , and ‘mechanism/magnitude of influence’ quantifies the change in likelihood and the processes or quantities involved.

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In summary, the frequency and magnitude of river floods have changed in the past several decades with high regional variations (high confidence). Anthropogenic climate change has increased the likelihood of extreme precipitation events and the associated increase in the frequency and magnitude of river floods (high confidence). There is high confidence that the warming in the last 40–60 years has led to a maximum of 10 days earlier spring floods per decade, shifts in timing and magnitude of ice-jam floods and changes in frequency and magnitude of snowmelt floods.

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Table 4.4 | Selected major drought events from 2013 to 2020 and their societal impact. Studies were selected for presentation based on the availability scientific literature impacts information and do not necessarily represent the most severe events.Impactful events are included even if not found to have a component attributable to climate change. This is not a systematic assessment of event attributions studies and their physical science conclusions. ‘Sign of influence’ indicates whether anthropogenic climate change was found to have made the event more or less likely , and ‘mechanism/magnitude of influence’ quantifies the change in likelihood and the processes or quantities involved.

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AR6 WGI (Douville et al., 2021; Seneviratne et al., 2021) found that increasing agricultural and ecological droughts trends are more evident than increasing trends in meteorological drought in several regions due to increased evaporative demand. Therefore, WGI concluded with high confidence that the increased frequency and the severity of agricultural/ecological droughts over the last decades in the Mediterranean and western North America can be attributed to anthropogenic warming.

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In addition, there is high confidence in anthropogenic influence on increased meteorological drought in southwestern Australia and medium confidence that recent drying and severe droughts in southern Africa and southwestern South America can be attributed to human influence. Increased agricultural/ecological and (or) meteorological and (or) hydrological drought is also seen with either medium confidence or high confidence in the trend but with low confidence on attribution to anthropogenic climate change in western, northeastern and central Africa; central, eastern and southern Asia; eastern Australia; southern and northeastern South America and the South American monsoon region; and western and central Europe. Finally, decreased drought in one or more categories is seen with medium confidence in western and eastern Siberia; northern and central Australia; southeastern South America; central North America and northern Europe, but with low confidence in attribution to anthropogenic influence, except in northern Europe, where anthropogenic influence on decreased meteorological drought is assessed with medium confidence.

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Major drought events worldwide have had substantial societal and ecological impacts, including reduced crop yields, shortages of drinking water, wildfires causing deaths of people and very large numbers of animals, impacting the habitats of threatened species, and widespread economic losses (Table 4.4, Cross-Chapter Box DISASTER in Chapter 4). In addition, anthropogenic climate change was found to have increased the likelihood or severity of most such events examined in event attribution studies.

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In summary, droughts can have substantial societal impacts (virtually certain), and agricultural and ecological drought conditions in particular have become more frequent and severe in many parts of the world but less frequent and severe in some others (high confidence). Drought-induced economic losses relative to GDP are approximately twice as high in lower-income countries compared to higher-income countries, although the gap has narrowed since the 1980s, and at the global scale there is a decreasing trend of economic vulnerability to drought (medium confidence). Nevertheless, anthropogenic climate change has contributed to the increased likelihood or severity of drought events in many parts of the world, causing reduced agricultural yields, drinking water shortages for millions of people, increased wildfire risk, loss of lives of humans and other species and loss of billions of dollars of economic damages (medium confidence).

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In the largest river basin of the Colombian Andes, regional climate change and land use activities (ploughing, grazing and deforestation) caused a 34% erosion rate increase over 10 years, with the anthropogenic soil erosion rate exceeding the climate-driven erosion rate (Restrepo and Escobar, 2018). Sedimentation increases due to soil erosion in mountainous regions burned by wildfires, as a result of warming and altered precipitation, is documented with high confidence in the USA (Gould et al., 2016; DeLong et al., 2018), Australia (Nyman et al., 2015; Langhans et al., 2016), China (Cui et al., 2014) and Greece (Karamesouti et al., 2016) and can potentially damage downstream aquatic ecosystems (Section 4.3.5) and water quality (Section 4.2.7) (Cui et al., 2014; Murphy et al., 2015; Langhans et al., 2016) (medium confidence). In Australia, for instance, sediment yields from post-fire debris flows (113–294 t ha –1) are 2–3 orders of magnitude higher than annual background erosion rates from undisturbed forests (Nyman et al., 2015). The positive trend in sediment yield in small ponds in the semiarid southwestern USA over the last 90 years was not entirely related to the rainfall or runoff trends, but was a result of complex interaction between long-term changes in vegetation, soil and channel networks (Polyakov et al., 2017).

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The intensification of the hydrological cycle due to anthropogenic climate change has multifaceted and severe impacts for cultural, economic, social and political pathways. In this section, we assess burgeoning evidence since AR5 which shows that environmental quality, economic development and social well-being have been affected by climate-induced hydrological changes since many aspects of the economy, environment and society are dependent upon water resources. We advance previous IPCC reports by assessing evidence on the impacts of climate change-induced water insecurity for energy production (Section 4.3.2), urbanisation (Section 4.3.4), conflicts (Section 4.3.6), human mobility (Section 4.3.7) and cultural usage of water (Section 4.3.8).

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Yields of major crops in semiarid regions, including the Mediterranean, sub-Saharan Africa, South Asia and Australia, are negatively affected by precipitation declines in the absence of irrigation (Iizumi et al., 2018; Ray et al., 2019), but this trend is less evident in wetter regions (Iizumi et al., 2018). Precipitation and temperature changes reduced global mean yields of maize, wheat and soybeans by 4.1, 1.8 and 4.5%, respectively (Iizumi et al., 2018). Of the global rice yield variability of ~32%, precipitation variability accounted for a larger share in drier South Asia than in wetter East and Southeast Asia (Ray et al., 2015). Between 1910 and 2014 agro-climatic conditions became more conducive to maize and soybean yield growth in the American Midwest due to increases in summer precipitation and cooling due to irrigation (Iizumi and Ramankutty, 2016; Mueller et al., 2016) (Box 4.3). In Australia, between 1990 and 2015, the negative effects of reduced precipitation and rising temperature led to yield losses, but yield losses were partly avoided because of elevated CO2 atmospheric concentration and technological advancements (Hochman et al., 2017a). Overall, temperature-only effects are stronger in wetter regions like Europe and East and Southeast Asia, and precipitation-only effects are stronger in drier regions (Iizumi et al., 2018; Ray et al., 2019) (medium evidence, high agreement ). In Asia, the gap between rain-fed and irrigated maize yield widened from 5% in the 1980s to 10% in the 2000s (Meng et al., 2016). In North America, yields of maize and soybeans have increased (1958–2007), yet meteorological drought has been associated with 13% of overall yield variability. However, yield variability was not a concern where irrigation is prevalent (Zipper et al., 2016). However, when water scarcity has reduced irrigation, yields have been negatively impacted (Elias et al., 2016). In Europe, yields have been affected negatively by droughts (Beillouin et al., 2020), with losses tripling between 1964 and 2015 (Brás et al., 2021). In West Africa, between 2000 and 2009, drought, among other altered climate conditions, led to millet and sorghum yield reductions between 10 and 20% and 5 and 15%, respectively (Sultan et al., 2019). Between 2006 and 2016, droughts contributed to food insecurity and malnutrition in northern, eastern and southern Africa, Asia and the Pacific. In 36% of these nations—mainly in Africa—where severe droughts occurred, undernourishment increased (Phalkey et al., 2015; Cooper et al., 2019). An attribution study showed that anthropogenic emissions increased the chances of October–December droughts over the region by 1.4–4.3 times and resulted in below-average harvests in Zambia and South Africa (Nangombe et al., 2020). Root crops, a staple in many tropics and subtropical countries, and vegetables are particularly prone to drought, leading to smaller fruits or crop failure (Daryanto et al., 2017; Bisbis et al., 2018). Livestock production has also been affected by changing seasonality, increasing frequency of drought, rising temperatures and vector-borne diseases and parasites through changes in the overall availability, as well as reduced nutritional value, of forage and feed crops (Varadan and Kumar, 2014; Naqvi et al., 2015; Zougmoré et al., 2016; Henry et al., 2018; Godde et al., 2019) (medium confidence).

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Floods have led to harvest failure and crop and fungal contamination (Liu et al., 2013; Uyttendaele et al., 2015). Globally, between 1980 and 2018, excess soil moisture has reduced rice, maize, soybean and wheat yields between 7 and 12% (Borgomeo et al., 2020). Changes in groundwater storage and availability, which are affected by the intensity of irrigated agriculture, also negatively impacted crop yields and cropping patterns (Section 4.2.6, Box 4.3, 4.7.2). Moreover, extreme precipitation can lead to increased surface flooding, waterlogging, soil erosion and susceptibility to salinisation (high confidence). For example, in Bangladesh, in March and April 2017, floods affected 220,000 ha of a nearly harvest-ready summer paddy crop and resulted in almost a 30% year-on-year increase in paddy prices. An attribution study of those pre-monsoon extreme rainfall events in Bangladesh concluded that anthropogenic climate change doubled the likelihood of the extreme rainfall event (Rimi et al., 2019). Moreover, floods, extreme weather events and cyclones have led to animal escapes and infrastructure damage in aquaculture (Beveridge et al., 2018; Islam and Hoq, 2018; Naskar et al., 2018; Lebel et al., 2020) (see Section 5.9.1).

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Advances in the attribution of extreme weather events have made it possible to determine the causal relationship between droughts, floods and climate change for some cities, particularly those with long hydro-meteorological records (Bader et al., 2018; Otto et al., 2020). Attribution analysis shows that urbanisation contributed to the increase in both frequencies of local and abrupt heavy rainfall events in the city, at a rate of 1.5 and 1.8 10 yr –1, respectively (Liang and Ding, 2017). A multi-method attribution showed that the likelihood of prolonged rainfall deficit in Cape Town (South Africa) during 2015–2017 was made more likely by a factor of 3.3 (1.4–6.4) due to anthropogenic climate change (Otto et al., 2018). These results show that climate change has impacted the return time of extreme droughts in the Western Cape, exceeding the capacity of the existing water supply system to cope (Otto et al., 2018) (Box 9.4; 9.8.2). In Baton Rouge (USA), a rapid attribution study showed that the probability of an event such as the intense precipitation and flash flooding of August 2016 has increased by at least a factor of 1.4 due to radiative forcing (USA) (van der Wiel et al., 2017). In Houston (USA), a study found that the combination of urbanisation and climate change nearly doubled peak discharge (84%) during Hurricane Harvey (August 2017), suggesting that land use change magnified the effects of climate change on catchment response to extreme precipitation events (Sebastian et al., 2019) (14.4.3.1; Box 14.5 The Economic Consequences of Climate Change in North America, Cross-Chapter Box DISASTER in Chapter 4). According to a multi-method approach, the 2014/2015 drought event in Sao Paulo (Brazil) was more likely to have been driven by water use changes and population growth than climate change (Otto et al., 2015) (Cross-Chapter Box DISASTER in Chapter 4).

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The science of weather event attribution requires high-quality observational data and climate models that are currently available only in highly developed countries (Otto et al., 2020). In addition, further research is necessary to determine the impacts of climate change on water-related extremes in the urban areas of developing countries (Bai et al., 2018). For example, a combination of observational analysis and global coupled climate models showed that the 2015 flooding event in Chennai (India) could not be attributed to anthropogenic climate change, with the effects of that being relatively small in the region due to the impact of GHG increases being largely counteracted by those of aerosols (van Oldenborgh et al., 2017a) (Section 4.2.5). Further research is also required to determine the impacts of climate change on water-related extremes in informal settlements where vulnerability to water insecurity is high due to poverty, overcrowding, poor-quality housing and lack of basic infrastructure (Scovronick et al., 2015; Grasham et al., 2019; Williams et al., 2019; Satterthwaite et al., 2020).

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AR5 concluded that climate change is projected to be an important stressor on freshwater ecosystems in the second half of the 21st century, especially under high-warming scenarios of RCP6.0 and RCP8.5 (high confidence), even though direct human impacts will continue to be the dominant threat (Settele et al., 2014). Rising water temperatures are also projected to cause shifts in freshwater species distribution and worsen water quality problems (high confidence), especially in those systems that already experience high anthropogenic loading of nutrients (Settele et al., 2014).

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Anthropogenic climate change has impacted every aspect of the water cycle (Section 4.2), and risks are projected to intensify with every degree of global warming (Section 4.4), with impacts already visible in all sectors of the economy and ecosystems (Section 4.3) and projected to intensify further (Section 4.5). In response to climate- and non-climate-induced water insecurity, people and governments worldwide are undertaking various adaptation responses across all sectors. In addition, there are several projected studies for future adaptation responses. We draw upon a list of 359 case studies of observed adaptation and 45 articles on projected future adaptation. Further information on selection and inclusion criteria is available in SM4.2. In this section, we document those adaptation responses (current and future) in different water use sectors. In the next (Sections 4.7.1, 4.7.2, 4.7.3), benefits of current adaptation and effectiveness of future adaptation are discussed.

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Advances in climate change attribution (Section 4.2; SM4.3; Figure 4.20) show the direct effects of anthropogenic climate change, also with regard to climate extremes. These advances also provide the basis for climate litigation (Marjanac and Patton, 2018) to hold countries/companies accountable for climate change impacts, for example, concerning risks of glacial lake outburst in Peru (Frank et al., 2019).

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Anthropogenic land use changes and climate change will exacerbate the intensity, frequency and spatial extent of floods and droughts, leading to populations becoming more vulnerable. According to projections, these increases in extreme events will be more significant with higher levels of global warming. However, the location and severity of floods and droughts are context-dependent and complex phenomena.

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Flood hazard natural processes usually result from increases in heavy precipitation events, but they can also be caused by saturated soils, increased runoff and land use changes. A warming climate usually causes greater energy for the intense upward motion for storm formation and increases evapotranspiration, which leads to heavier precipitation. Many places around the world will experience more-than-average rainfall, which may increase soil moisture. Wetter soils saturate faster during precipitation events, resulting in increased runoff that can muddy the waters and lead to floods. Anthropogenic land use changes, such as urbanisation, deforestation, grasslands and agricultural extension, can also reduce the amount of water infiltrating the soil and leading to frequent flooding. Floods are expected to increase in Asia, the USA and Europe, particularly in areas dependent on glacier water where melting will lead to earlier spring floods. Additionally, fluvial floods are projected to be more frequent in some regions in central Africa and northern high latitudes and less frequent in the southern areas of North America, southern South America, the Mediterranean, parts of Australia and southern parts of Europe.

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In AR6 (Chapter 16 and cross-chapter papers), over 100 key risks have been identified across regions and sectors, which have the potential to manifest into severe impacts that are relevant to the interpretation of UNFCCC Article 2, specifically on the objective to avoid dangerous anthropogenic interference with the climate system. These risks are likely 2 to become more severe under higher warming scenarios and social-ecological conditions that yield high exposure and vulnerability to the associated climate-related hazards. In this report, these key risks have been grouped into categories represented by eight overarching risks (called Representative Key Risks, RKRs) relating to: (1) coastal socio-ecological systems; (2) terrestrial and ocean ecosystems; (3) critical physical infrastructure, networks and services; (4) living standards; (5) human health; (6) food security; (7) water security; and (8) peace and human mobility (Chapter 16). Decision-making options for managing these risks, such as selecting the relevant adaptation options to implement, require an assessment of the local context in which these impacts are likely to be experienced, as well as the local to global collective implications of those actions (Sections 17.2 and 17.5).

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Observations of past and current climate in Asia are assessed in IPCC WGI AR6 (IPCC, 2021). Examples of observed impacts in Asia with attributed CIDs are shown in Figure 10.2. Surface temperature has increased in the past century all over Asia (very high confidence). Elevation-dependent warming (i.e., the warming rate is different across elevation bands is observed in HMA) (medium confidence) (Hock et al., 2019; Krishnan et al., 2019). While there is an overall trend of decreasing glacier mass in HMA, there are some regional differences and even areas with a positive mass balance due to increased precipitation (Wester et al., 2019). Rising temperatures have resulted in an increasing trend of growing-season length. The number of hot days and warm nights continues to increase in all of Asia (high confidence), while cold days and nights are decreasing except in the southern part of Siberia (Gutiérrez et al., 2021). Large increases in temperature extremes are observed in West and Central Asia (high confidence). Temperature increase is causing strong, more frequent and longer heatwaves in South and East Asia. The 2013 East China heatwaves case is such an example (Xia et al., 2016). In 2016 and 2018, extreme warmth was observed in Asia for which an event-attribution study revealed that this would not have been possible without anthropogenic global warming (medium confidence) (Imada et al., 2018; Imada et al., 2019).

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According to Bindoff et al. (2019), the oceans have warmed unabatedly since 2004, continuing the multi-decadal ocean-warming trends. Their report also summarised that there is increased agreement between coupled model simulations of anthropogenic climate change and observations of changes in ocean heat content (high confidence). Observed SLR around Asia over 1900–2018 is similar to the global mean sea level change of 1.7 mm yr –1, but for the period 1993–2018, the SLR rate increased to 3.65 mm yr –1 in the Indo-Pacific region and 3.53 m yr –1 in the Northwest Pacific, compared with the global value of 3.25 mm yr –1 (Ranasinghe et al., 2021). The extreme SLR has occurred since the 1980s along the coast of China (Feng et al., 2018b).

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Monsoon land precipitation likely will increase in East, Southeast and South Asia mainly due to increasing moisture convergence by elevated temperature (high confidence); however, there is low confidence in the magnitude and detailed spatial patterns of precipitation changes at the sub-regional scale in East Asia (Doblas-Reyes et al., 2021). Increasing land–sea thermal contrast and resultant lower tropospheric circulation changes, together with increasing moisture, are projected to intensify the South Asian summer monsoon precipitation (medium confidence). Anthropogenic aerosols greatly modify sub-regional precipitation changes, and their spatio-temporal changes are uncertain (Douville et al., 2021). Monsoonal winds will generally become weaker in a future warming world with different magnitudes across regions (medium confidence). Future changes in sand and dust storms are uncertain.

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In Central Kazakh Steppe, in line with warming, in 2018 there were more ‘southern’ sub-arid species in the communities and fewer relatively ‘northern’ boreal and polyzonal species of ground beetles (Carabidae) and black beetles (Tenebrionidae) than in 1976–1978 (Mordkovich et al., 2020). The present distribution of Asian black birch (Betula davuricaPall.) in East and North Asia was formed as a result of northward expansion during post-Last Glacial Maximum global warming (Shitara et al., 2018). Both upper and lower limits of avifauna of two New Guinean mountains, Mt. Karimui and Karkar Island, have been shifting upslope since 1965 (Freeman and Freeman, 2014). In Republic of Korea, for the past 60 years, the northern boundary line of 63 southern butterfly species has moved further north (Bae et al., 2020). The change in the butterflies’ occurrence in this period has been influenced mostly by large-scale reforestation, not by climate change (Kwon et al., 2021). Warming-driven geographic range shift was recorded in 87% of 124 endemic plant species studied in the Sikkim Himalaya in the periods 1849–1850 and 2007–2010 (Telwala et al., 2013). In Darjeeling, India, significant change in lichen community structure was shown in response to climate change and anthropogenic pollution (Bajpai et al., 2016).

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Climate change, human activity and lightning determine increases in wildfire severity and area burned in North Asia (high detection with medium-to-low attribution to climate change). In North Asia, the extent of fire-affected areas in boreal forest can be millions of hectares in a single extreme fire year (Duane et al., 2021) and nearly doubled between 1970 and 1990 (Brazhnik et al., 2017). During recent decades, the number, area and frequency of forest fires increased in Putorana Plateau (north of Central Siberia), in larch-dominated forests of Central Siberia and in Siberian forests as a whole. This increase is in line with an increase in the average annual air temperature, air temperature anomalies, droughts and the length of fire season (Ponomarev et al., 2016; Kharuk and Ponomarev, 2017; Pospelova et al., 2017). The number of forest fires and damaged areas in Gangwon Province and the Yeongdong area in the 2000s increased by factors of 1.7 and 5.6, respectively, compared with the 1990s (Bae et al., 2020). Climate change is not the sole cause of the increase in forest fire severity (Wu et al., 2014; Wu et al., 2018d). Ignition is often facilitated by lightning (Canadell et al., 2021), and over 80% of fires in Siberia are likely anthropogenic in origin (e.g., (Brazhnik et al., 2017). Gas field development and Indigenous tundra burning practices that may get out of control contribute to fire frequency in the forest–tundra of West Siberia (Adaev, 2018; Moskovchenko et al., 2020). Climate change in combination with socioeconomic changes has resulted in an increase in fire severity and area burned in South Siberia, and illegal logging increases fire danger in forest–steppe Scots pine stands (Ivanova et al., 2010; Schaphoff et al., 2016).

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In North Asia, in Central Siberia and south of West Siberia, the growth index of Siberian larch based on tree-ring width increased with the onset of warming and changed in antiphase with aridity in the 1980s (Kharuk et al., 2018). In Mongolia and Kazakhstan, the temperature increase over the previous decade promoted radial stem increment of the Siberian larch. However, the simultaneous influence of increased temperature, decreased precipitation and increased anthropogenic pressure resulted in widespread declines in forest productivity and reduced forest regeneration, and increased tree mortality (Dulamsuren et al., 2013; Lkhagvadorj et al., 2013a; Lkhagvadorj et al., 2013b; Dulamsuren et al., 2014; Khansaritoreh et al., 2017). In Eastern Taimyr, growing season, the number of flowering shoots, annual increment, success of seed ripening and vegetation biomass have increased considerably in recent decades (Pospelova et al., 2017). In Vishera Nature Reserve, northern Ural Mountains, annual temperature has increased in recent decades in parallel with a summer temperature drop and an increase in summer frost numbers. As a result, trends in vegetation change are mostly unreliable (Prokosheva, 2017).

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Modelling of the interactions between climate-induced vegetation shifts, wildfire and human activities can provide keys to how people in Asia may be able to adapt to climate change (Kicklighter et al., 2014; Tian et al., 2020). Conservation and sustainable development would benefit from being tailored and modified considering the changing climatic conditions and shifting biomes, mountain belts and species ranges (Pörtner et al., 2021). Expanding the nature reserves would help species conservation; to facilitate species movements across climatic gradients, an increase in landscape connectivity can be elaborated by setting up habitat corridors between nature reserves and along elevational and other climatic gradients (Brito-Morales et al., 2018; D’Aloia et al., 2019; United Nations Climate Change Secretariat, 2019). Assisted migration of species should be considered for isolated habitats as mountain summits or where movements are constrained by poor dispersal ability. Introducing seeds of the species to new regions will help to protect them from the extinction risk caused by climate change (Mazangi et al., 2016). In Asian boreal forests, a strategy and integrated programmes should be developed for adaptation of the forests to global climate change, including sustainable forest management, firefighting infrastructure and forest fuel management, afforestation, as well as institutional, social and other measures in line with Sustainable Development Goal (SDG) 15 ‘Life on Land’ (Isaev and Korovin, 2013; Kattsov and Semenov, 2014; Bae et al., 2020). Improvements in forest habitat quality can reduce the negative impacts of climate change on biodiversity and ecosystem services (Choi et al., 2021). Adaptation options for freshwater ecosystems in Asia include increasing connectivity in river networks, expanding protected areas, restoring hydrological processes of wetlands and rivers, creating shade to lower temperatures for vulnerable species, assisted translocation and migration of species (Hassan et al., 2020; Chapter 2). Reduction of non-climate anthropogenic impacts can enhance the adaptive capacity of ecosystems (Tchebakova et al., 2016).

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Coastal habitats of Asia are diverse, and the impacts of climate change, including rising temperatures, ocean acidification and SLR, are known to affect the services and livelihoods of the people depending on them. The risk of irreversible loss of many marine and coastal ecosystems increases with global warming, especially at 2°C or more (high confidence) (IPCC, 2018b). In the South China Sea, coral growth and sea surface temperature (SST) have shown regional long-term trends and inter-decadal variations, while coral growth is predicted to decline by the end of this century (Yan et al., 2019). Increasing human impacts have also been found to reduce coral growth (Yan et al., 2019). In the South China Sea, nearly 571 coral species have been severely impacted by global climate changes and anthropogenic activities (Huang et al., 2015a).

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An increase in host susceptibility, pathogen abundance or virulence has led to higher prevalence and severity of coral diseases and to decline and changes in coral reef community composition (Maynard et al., 2015). Relative risk has been found to be high in the province of Papua in Indonesia, the Philippines, Japan, India, northern Maldives, the Persian Gulf and the Red Sea. For the combined disease-risk metric, relative risk was considered lower for locations where anthropogenic stress was low or medium, a condition found for some locations in Thailand (Maynard et al., 2015).

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Degradation and loss of coral reefs can affect about 4.5 million people in Southeast Asia and the Indian Ocean (Lam et al., 2019). In the coral reef fisheries sector, there are about 3.35 million fishers in Southeast Asia and 1.5 million fishers in the Indian Ocean (Teh et al., 2013). The economic loss under different climate-change scenarios and fishing efforts were estimated to range from 27.78 to 31.72 million USD annually in Nha rang Bay, Vietnam. A survey conducted in Taiwan, Province of China, showed that the average annual amount that people were personally willing to pay was 35.75 USD and the total amount was 0.43 billion USD. These high values indicate the need to preserve these coral reef ecosystems (Tseng et al., 2015). In Bangladesh, the coral reef of St. Martin’s Island contributes 33.6 million USD yr –1 to the local economy, but climate change, along with other anthropogenic activities, has been identified as a threat these habitats (Rani et al., 2020 a).

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In the Southeast Asia region threats from both warming and acidification has indicated that by 2030, 99% of reefs will be affected, and by 2050, 95% are expected to be in the highest levels of the ‘threatened’ category (Burke et al., 2011), similar to global corals (Frieler et al., 2013; Bruno and Valdivia, 2016). Modelling results indicate that even under RCP scenarios, the functional traits of coral reefs can be affected (van der Zande et al., 2020) and coral communities will mainly consist of small numbers of temperature-tolerant and fast-growing species (Kubicek et al., 2019). Increases in temperature (+3°C) and pCO2 (+400 matm) projected for this century can reduce the sperm availability for fertilisation, which along with adult population decline either due to climate change or anthropogenic impacts (Hughes et al., 2017) can affect coral reproductive success thereby reducing the recovery of populations and their adaptation potential (Albright and Mason, 2013; Hughes et al., 2018; Jamodiong et al., 2018). In the southern Persian Gulf, increased disturbance frequency and severity has caused progressive reduction in coral size, cover and population fecundity (Riegl et al., 2018), and this can lead to functional extinction. Connectivity required to avoid extinctions has increased exponentially with disturbance frequency and correlation of disturbances across the metapopulation. In the Philippines experiments have also proved that scleractinian corals, such as A. tenuis, A. millepora and F. colemani, which spawn their gametes directly into the water column, may experience limitations from sperm dilution and delays in initial sperm–egg encounters that can impact successful fertilisation (de la Cruz and Harrison, 2020).

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Adequate water supply for various uses is crucial for millions of people living in the mountains of Asia. Particularly in the HKH region, mountain springs play an important role in generating stream flow for non-glaciated catchments and in maintaining dry-season flows across many watersheds (Scott et al., 2019; Stott and Huq, 2014). There is a good deal of evidence that the springs are drying up or yielding less discharge (Tambe et al., 2012; Tiwari and Joshi, 2014; Sharma et al., 2016), threatening local communities who depend on spring water for their lives and livelihoods. Some of the main reasons for drying springs include anthropogenic impacts (deforestation, exploitative land use), infrastructure (road construction), socioeconomic changes (increasing demand and modernisation of facilities) and climatic changes (changes in rainfall regime and higher temperature) (Stott and Huq, 2014; Tiwari and Joshi, 2014; Sharma et al., 2016).

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Significantly, climate change will add to already existing vulnerabilities. In the case of the Yellow River basin in China, underlining the interface between future water scarcity and hydroclimatic and anthropogenic drivers, a recent study expects moderate-to-severe water scarcity over six Yellow River sub-catchments under the RCP4.5 scenario, and anticipates that human influences on water scarcity will be worse than that of climate change, with water availability in the downstream being impacted by concurrent changes in land use and high temperature (Omer et al., 2020). Nearly 8% of internationally shared or transboundary aquifers (TBAs), ensuring livelihood security for millions of people through sustaining drinking water supply and food production, are currently overstressed due to human overexploitation (Wada and Heinrich, 2013). The Asia Pacific region has the highest annual water withdrawal due to its geographic size, growing population and irrigation practices, and water for agriculture continues to consume 80% of the region’s resources (Taniguchi et al., 2017b; Visvanathan, 2018).

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There is high agreement in the literature that Asian fisheries and aquaculture, including the local communities depending on them for livelihoods, are highly vulnerable to the impacts of climate change. Asia has been impacted by SLR (Panpeng and Ahmad, 2017), a decrease in precipitation in some parts (Salik et al., 2015) and an increase in temperature (Vivekanandan et al., 2016), all of which have drastic effects on fisheries and aquaculture (FAO, 2018c). Its coastal fishing communities is exposed to disasters, which are predicted to increase (Esham et al., 2018). Fisheries in most of South Asia and Southeast Asia involve small-scale fishers who are more vulnerable to climate-change impacts compared with commercial fishers (Sönke Kreft et al., 2016; Blasiak et al., 2017), although there is a general decreasing trend in the number of small units (Fernandez-Llamazares et al., 2015; ILO, 2015). A regional study of South Asia forecast large decreases in potential catch of two key commercial fish species (hilsa shad and Bombay duck) in the Bay of Bengal (Fernandes et al., 2016), which forms a major fishery and food source for coastal communities. About 69% of the commercially important species of the Indian marine fisheries were found to be impacted by climate change and other anthropogenic factors (Dineshbabu et al., 2020). Likewise, water salinisation brought about by SLR is expected to impact the availability of freshwater fish in southwest coastal Bangladesh with adverse implications to poor communities (Dasgupta et al., 2017a). Analysis of fishery has indicated that there will be a continued decrease in catch impacting the seafood sector in the Philippines, Thailand, Malaysia and Indonesia (Nong, 2019). Climate change is predicted to decrease total productive fisheries potential in South and Southeast Asia, driven by a temperature increase of approximately 2°C by 2050 (Barange et al., 2014).

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Case studies on climate risk management and integrated CCA and DRR actions highlight some key lessons including: an integrated and transformative approach to CCA, which focuses on long-term changes in addressing climate impacts (Filho et al., 2019); adoption of an adaptive flood risk management framework incorporating both risk observation and public perceptions (Al-Amin et al., 2019); a holistic approach and non-structural and technological measures in flood control management (Chan, 2014); monitoring of changes in urban surface water in relation to changes in seasons, land covers, anthropogenic activities and topographic characteristics for managing watersheds and urban planning (Faridatul et al., 2019); removing ‘gender blindness’ in agrobiodiversity conservation and adaptation policies (Ravera et al., 2019); understanding uncertainties in CCA and DRR at the local level (van der Keur et al., 2016; Djalante and Lassa, 2019 ); promoting the use of IKLK alongside scientific knowledge (Hiwasaki et al., 2014); and increasing information, education and communication activities, and capacity development on DRR at the local level (Tuladhar et al., 2015a).

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The food–water–energy nexus can be evaluated in the two-way interactions between water–food, water–energy and food–energy (Taniguchi et al., 2017a). The water–energy nexus includes water for energy and energy for water (Rothausen and Conway, 2011; Hussey and Pittock, 2012; Byers et al., 2014), the water–food nexus includes water for food and the impact of food production on water (Hoekstra and Mekonnen, 2012) and the energy–food nexus includes energy consumption for food production and food crops for biofuel production (Tilman et al., 2009). The food–water–energy–land nexus has diverse implications at the sub-regional level in Asia. The increase in the water-supply gap raises questions about the sustainability of the main mode of electricity generation in South Asia. Thermal power generation and hydropower generation are both threatened by water shortages in South Asia (Luo, 2018b; Mitra et al., 2021). Furthermore, policy-mismatch-driven anthropogenic causes lead to unsustainable water use for food production in India. For example, subsidised electricity supply for watering agriculture plays a key role in losing groundwater’s buffer capacity against the various changes including climate variabilities (Badiani et al., 2012; Mitra, 2017). In the Mekong River basin of Southeast Asia, massive and rapid export-oriented hydropower development will have direct implications on regional food security and livelihoods through a major negative effect on the aquatic ecosystem (Baran and Myschowoda, 2009; Dugan et al., 2010; Arias et al., 2014). Similarly, in Central Asia, the shifting of water storage for irrigation to power development has increased risks on reliable water supply and quality of water (Granit et al., 2012). Deforestation-driven agro-environmental changes have led to a decreased forest water supply, an increased irrigation water demand and a negative effect on cropland stability and productivity (Lim et al., 2017a; Lim et al., 2019b).

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Best, J., 2018: Anthropogenic stresses on the world’s big rivers. Nat. Geosci. , 1.

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Jenssen, B.M., et al., 2015: Anthropogenic flank attack on polar bears: interacting consequences of climate warming and pollutant exposure. Front. Ecol. Evol. , 3 (16), doi:10.3389/fevo.2015.00016.

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Smith, M.R. and S.S. Myers, 2018: Impact of anthropogenic CO2 emissions on global human nutrition. Nat. Clim, Change, 8 (9), 834–839, doi:10.1038/s41558-018-0253-3.

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Past, present and future concentrations of greenhouse gases in the atmosphere are the direct result of both natural and anthropogenic greenhouse gas emissions which are, in turn, a function of past and current patterns of human and economic development (very high confidence, WGI SPM [IPCC, 2021b ]). This includes development processes that drive land use change, extractive industries, manufacturing and trade, energy production, food production, infrastructure development and transportation. These patterns of development are therefore drivers of current and future climate risk to specific sectors, regions and populations (Byers et al., 2018), as well as the demand for both mitigation and adaptation as a means of preventing climate change from undermining development goals. The SDGs represent targets for supporting human and ecological well-being in a sustainable manner. Yet, while progress is being made towards a number of the SDGs, success in achieving all of the SDGs by 2030 across all global regions remains uncertain (high agreement , medium evidence) (United Nations, 2021 ). Moreover, current commitments to reduce greenhouse gas emissions are not yet consistent with limiting changes in global mean temperature elevation to well-below 2°C or 1.5°C (very high confidence) (IPCC, 2018a) (see also Section 18.2).

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Energy systems have been a historical driver of climate change, but are also adversely affected by climate change impacts, including short-term shocks and stressors from extreme weather as well as long-term shifts in climatic conditions (very high confidence). The potential for such factors is often incorporated into local system designs, operations and response strategies. There have been changes in observed weather and extreme event hazards for the energy system, but to date, many are not attributable solely to anthropogenic climate change (USGCRP, 2017; IPCC, 2021a). Nevertheless, with observed extremes shifting outside of what has been observed historically, existing design criteria and operations may not be optimal for future climate conditions and contingencies (Chapters 2 to 16). Overall, there is limited historical evidence on the efficacy of adaptation responses in reducing vulnerability of energy systems (high agreement , limited evidence). However, sustainable development trends, such as improving incomes, reducing poverty, and improving health and education have reduced vulnerability (Chapter 16), and improvements in system resiliency to extreme weather events and more efficient water management have occurred that have synergies with adaptation and sustainable development in general.

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Land, oceans and terrestrial ecosystems are in transition globally, with anthropogenic factors including climate change being a major driving force (very high confidence) (IPBES, 2019) (Box 6). Seventy-five percent of the land surface has been significantly altered, 66% of the ocean area is experiencing increasing cumulative impacts and over 85% of wetland areas have been lost (IPBES, 2019). Since 1970, only four out of eighteen recognised ecosystem services assessed have improved in their functioning: agricultural production, fish harvest, bioenergy production and material harvests. The other 14 ecosystem services have declined (IPBES, 2019), raising concerns about the capacity of ecosystems and their services to support sustainable and CRD.

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Climate change connects to ecosystem services through two links: climate change and its influence on ecosystems as well as its influence on services (Section 2.2). The key climatic drivers are changes in temperature, precipitation and extreme events, which are unprecedented over millennia and highly variable by regions (Sections 2.3, 3.2; Cross-Chapter Box EXTREMES in Chapter 2). These climatic drivers influence physical and chemical conditions of the environment and worsen the impacts of non-climate anthropogenic drivers including eutrophication, hypoxia and sedimentation (Section 3.4). Such changes have led to changes in terrestrial, freshwater, oceanic and coastal ecosystems at all different levels, from species shifts and extinctions, to biome migration, and to ecosystem structure and processes changes (Sections 2.4, 2.5, 3.4, Cross-Chapter Box MOVING PLATE in Chapter 5). Changes in ecosystems leads to changes in ecosystem services including food and limber prevision, air and water quality regulation, biodiversity and habitat conservation, and cultural and mental support (Sections 2.4, 3.5). Table Box 18.5.2 presents examples of climate change’s impact on ecosystems and their services from other chapters in the WGII report. The degradation of ecosystem services is felt disproportionately by people who are already vulnerable because of historical and systemic injustices, including women and children in low-income households, Indigenous or other minority groups, small-scale producers and fishing communities, and low-income countries (Sections 3.5, 4.3, 5.13).

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Some of the observed trends and events can be partly attributed to anthropogenic climate change, as documented in Chapter 16. Examples include regional warming trends and sea level rise (SLR), terrestrial and marine heatwaves, declining rainfall and increasing fire weather in southern Australia and extreme rainfall and severe droughts in New Zealand.

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Managing climate change risks to ecosystems is primarily based on reducing the impact of other anthropogenic pressures, including invasive species, and facilitating natural adaptation (high confidence). This approach is most feasible within protected areas on public, private and Indigenous land and sea (Bellard et al., 2014; Liu et al., 2020) but is also applicable elsewhere (Barnes et al., 2015). Effective strategies promote ecosystem resilience by changing unsustainable land uses and management practices, increasing habitat connectivity, controlling introduced species, restoring habitats, implementing appropriate fire management, integrated risk assessment and adaptation planning (B. Frame et al., 2018; Lindenmayer et al., 2020; Macinnis-Ng et al., 2021). Complementary approaches include ex situ seed banks (Morrison and Pickering, 2013; Christie et al., 2020).

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Tourist motivations for visiting the GBR are changing, with a recent survey finding that two-thirds of tourists were visiting ‘before it was gone’ and a similar number were reporting damage to the reef—an example of ‘last chance tourism’ (Piggott-McKellar and McNamara, 2016). The Australian government is investing AUD$1.9 billion to support the GBR through science and practical environmental outcomes, including reducing other anthropogenic pressures, which can suppress natural adaptive capacity (CoA, 2019b; GBRMPA, 2019). However, adaptation efforts on the GBR aimed specifically at climate impacts, for example coral restoration following marine heatwave impacts (Boström-Einarsson et al., 2020), may slow the impacts of climate change in small discrete regions of the reef or reduce short-term socioeconomic ramifications, but they will not prevent widespread bleaching (Condie et al. 2021).

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In New Zealand, precipitation has generally decreased in the north and increased in the southwest (Figure 11.2) (Harrington et al., 2014), but it is difficult to ascertain trends in the relatively short streamflow records. Glaciers in New Zealand’s southern alps have lost one third of their mass since 1977 (Mackintosh et al., 2017; Salinger et al., 2019b), and glacier mass loss in 2018 was at least 10 times more likely to occur with anthropogenic forcing than without (Vargo et al., 2020).

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Pluvial (flash flood from high intensity rainfall) and fluvial (river) flooding are the most costly natural disasters in Australia, averaging AUD$8.8 billion per year (Deloitte, 2017b). In New Zealand, insured damages for the 12 costliest flood events from 2007 to 2017 exceeded NZD$472 million, of which NZD$140 million has been attributed to anthropogenic climate change (Frame et al., 2020). Extreme rainfall intensity in northern Australia and New Zealand has been increasing, particularly for shorter (sub-daily) duration and more extreme high rainfall (high confidence) (Westra and Sisson, 2011; Griffiths, 2013; Laz et al., 2014; Rosier et al., 2015) (Table 11.2b). Changes are also occurring in spatial and temporal patterns and seasonality (Wasko and Sharma, 2015; Zheng et al., 2015; Wasko et al., 2016).

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There is ample evidence of health loss due to extreme weather in Australia and New Zealand, and rising temperatures, changing rainfall patterns and increasing fire weather have been attributed to anthropogenic climate change (11.2.1). Extreme heat leads to excess deaths and increased rates of many illnesses (Hales et al., 2000; Nitschke et al., 2011; Lu et al., 2020). Between 1991 and 2011 it is estimated that 35–36% of heat-related mortality in Brisbane, Sydney and Melbourne was attributable to climate change, amounting to about 106 deaths a year on average over the study period (Vicedo-Cabrera et al., 2021). Exposure to high temperatures at work is common in Australia, and the health consequences may include more accidents, acute heat stroke and chronic disease (Kjellstrom et al., 2016). Long-term rise in temperatures is changing the balance of summer and winter mortality in Australia (Hanigan et al., 2021). The Black Summer wildfires in Australia in 2019/2020 (Box 11.1) caused 33 deaths directly (Davey and Sarre, 2020) and exposed millions of people to heavy particulate pollution (Vardoulakis et al., 2020). In the Australian states most heavily affected by the fires, 417 deaths, 3151 hospital admissions for cardiovascular or respiratory conditions and about 1300 emergency department presentations for asthma are attributed to wildfire smoke exposure (Borchers Arriagada et al., 2020). Immediate smoke-related health costs from the 2019–2020 fires are estimated at AUD$1.95 billion (Johnston et al., 2020).

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Aggregated insured losses from weather-related hazard events from 2013 to 2020 were almost AUD$15 billion for Australia (1.2% of GDP) and almost NZD$1 billion for New Zealand (0.4% of GDP) (NIWA, 2020; ICA, 2021) (ICA, 2020a; NIWA, 2020). However, there is no trend in normalised losses because the rising insurance costs are being driven by more people living in vulnerable locations with more to lose (McAneney et al., 2019). In New Zealand, two major hailstorms during 2014–2020 and three major floods during 2019–2021 caused significant insurance losses (ICNZ, 2021). Insured losses exceeded NZD$472 million for the 12 costliest floods from 2007 to 2017, of which NZD$140 million could be attributed to anthropogenic climate change (Frame et al., 2020). In Australia, insured damage was almost AUD$1.0 billion for the Queensland hailstorm in 2020, AUD$1.7 billion for east coast flooding in 2020, AUD$2.3 billion for the 2019–2020 fires, AUD$2.3 billion for the Queensland hailstorm in 2019, AUD$1.2 billion for the North Queensland floods in 2019, AUD$1.4 billion for the NSW hailstorm in 2018, AUD$1.8 billion for Cyclone Debbie in 2017 and AUD$1.5 billion for the Brisbane hailstorm in 2014 (ICA, 2020b). The insured loss from the seven costliest hailstorms in Australia from 2014 to 2021 totalled AUD$7.6 billion (ICA, 2021).

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Detection and attribution of observed climate trends and events is called ‘climate attribution’. This has been assessed by IPCC WGI (Gutiérrez et al., 2021; Ranasinghe et al., 2021; Seneviratne et al., 2021) and summarised in Chapter 16. Trends that have been formally attributed in part to anthropogenic climate change include regional warming trends and sea level rise (SLR), decreasing rainfall and increasing fire risk in southern Australia. Events include extreme rainfall in New Zealand during 2007–2017, the 2007/2008 and 2012/2013 droughts in New Zealand, high temperatures in Australia during 2013–2020, the 2016 northern Australian marine heatwave, the 2016/2017 and 2017/18 Tasman Sea marine heatwaves and 2019/2020 fires in Australia.

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Impact attribution is considered in Sections 11.3.1–11.3.10 and summarised in Table 11.9. More literature is available for natural systems than human systems, which represents a knowledge gap rather than an absence of impacts that are attributable to anthropogenic climate change. Fundamental shifts in the structure and composition of some ecosystems are partly due to anthropogenic climate change (high confidence). In human systems, the costs of droughts and floods in New Zealand, and heat-related mortality and fire damage in Australia, are partly attributed to anthropogenic climate change (medium confidence).

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In New Zealand, the annual cost of rural fire to the economy has been estimated at NZD$67 million, with indirect ‘costs’ potentially two to three times the direct costs (Scion, 2018). Insured losses from weather-related disasters cost almost NZD$1 billion during 2015–2021 (ICNZ, 2021). Floods cost the New Zealand economy at least NZD$120 million for privately insured damages between 2007 and 2017 (D. Frame et al., 2018). The 2007/2008 drought cost NZD$3.2 billion and the 2012/13 drought cost NZD$1.6 billion, of which about 20% could be attributed to anthropogenic climate change (Frame et al., 2020) (11.3.11).

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Delworth, T. L. and F. Zeng, 2014: Regional rainfall decline in Australia attributed to anthropogenic greenhouse gases and ozone levels. Nature Geoscience, 7 (8), 583–587, doi:10.1038/ngeo2201.

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Rauniyar, S. P. and S. B. Power, 2020: The Impact of Anthropogenic Forcing and Natural Processes on Past, Present, and Future Rainfall over Victoria, Australia. Journal of Climate, 33 (18), 8087–8106, doi:10.1175/jcli-d-19-0759.1.

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Rosier, S. et al., 2015: Extreme Rainfall in Early July 2014 in Northland, New Zealand—Was There an Anthropogenic Influence?Bulletin of the American Meteorological Society, 96 (12), S136-S140, doi:10.1175/bams-d-15-00105.1.

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van Oldenborgh, G. J. et al., 2021: Attribution of the Australian bushfire risk to anthropogenic climate change. Natural Hazards and Earth System Sciences, 21 (3), 941–960, doi:10.5194/nhess-21-941-2021.

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Vargo, L. J. et al., 2020: Anthropogenic warming forces extreme annual glacier mass loss. Nat. Clim. Chang. , 10 (9), 856–861, doi:10.1038/s41558-020-0849-2.

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Anthropogenic climate change poses risks to many human and ecological systems. These risks are increasingly visible in our day-to-day lives, including a growing number of disasters that already bear a fingerprint of climate change. There is increasing concern about how these risks will shape the future of our planet—our ecosystems, our well-being and development opportunities. Policymakers are asking what is known about the risks, and what can be done about them. Many people, especially youth, around the world are calling for urgency, ambition and action. Companies are wondering how to manage new threats to their bottom line, or how to grasp new opportunities. On top of this growing concern about climate change, the coronavirus disease 2019 (COVID-19) pandemic has exposed vulnerabilities to shocks, significantly aggravated climate-related risks, and posed new questions about how to achieve a green, resilient and inclusive recovery (see Cross-Chapter Box COVID in Chapter 7).

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Section 16.5.1 presents a full discussion of ‘key risks’, synthesised from across all chapters, defined as those risks that are potentially severe and therefore especially relevant to the interpretation of ‘dangerous anthropogenic interference with the climate system’ in the terminology of UNFCCC Article 2.

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In line with the AR5 definition, ‘changes in climate-related systems’ here refer to any long-term trend, irrespective of the underlying causes; thus, an observed impact is not necessarily an observed impact of anthropogenic climate forcing. For example, in this section, sea level rise is defined as relative sea level rise measured against a land-based reference frame (tide gauge measurements), meaning that it is driven not only by thermal expansion and loss of land ice influenced by anthropogenic climate forcing, but also by vertical land movements. As attribution of coastal damages to sea level rise does not distinguish between these components, it does not imply attribution to anthropogenic forcing. Where the literature does allow attribution of changes in natural, human or managed systems to anthropogenic climate forcing (‘joint attribution’, Rosenzweig et al., 2007), this is highlighted in the assessment. Often the attribution of changes in the natural, human or managed systems to anthropogenic forcing can be done in a two-step approach where (i) an observed change in a climate-related system is attributed to anthropogenic climate forcing (‘climate attribution’) and (ii) changes in natural, human or managed systems are attributed to this change in the climate-related system (‘impact attribution’).

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In this chapter, we explicitly distinguish between assessment statements related to ‘climate attribution’ (listed in Table SM16.21), ‘impact attribution’ (listed in Table SM16.22) and ‘identification of weather sensitivity’ (listed in Table SM16.23). The identification of ‘weather sensitivity’ does not necessarily imply that there also is an impact of long-term changes in the climate-related systems on the considered system. However, if the probability or intensity of an extreme weather event has increased due to anthropogenic forcing (‘climate attribution’) (NASEM, 2016; WGI AR6 Chapter 11 Seneviratne et al., 2021) and the event is also identified as an important driver of an observed fluctuation in a natural, human or managed system (‘identification of weather sensitivity’), then the observed fluctuation is considered (partly) attributed to long-term climate change (‘impact attribution’) and even to anthropogenic forcing.

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By definition, the counterfactual baseline required for impact attribution cannot be observed. However, it may be approximated by impact model simulations forced by a stationary climate, for example derived by de-trending the observed climate (Diffenbaugh et al., 2017; Mengel et al., 2021), while other relevant drivers (e.g., land use changes or application of pesticides) of changes in the system of interest (e.g., a bird population) evolve according to historical conditions. To attribute to anthropogenic climate forcing, the anthropogenic trends in climate are estimated from a range of different climate models and subtracted from the observed climate (e.g., Abatzoglou and Williams, 2016, for changes in the extent of forest fires or Diffenbaugh and Burke, 2019, for effects on economic inequality) or the ‘no anthropogenic climate forcing’ baseline is directly derived from a large ensemble of climate model simulations not accounting for anthropogenic forcings (e.g., Kirchmeier-Young et al.., 2019b, for the extent of forest fires). In any case, it has to be demonstrated that the applied impact models are able to explain the observed changes in natural, human or managed systems by, for example, reproducing the observations when forced by observed changes in climate-related systems and other relevant drivers.

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In this section, we synthesise observed impacts of changes in climate-related systems across a range of ecosystems, sectors and regions. Figure 16.2 summarises the attribution of observed (regional) changes in natural, human or managed systems (orange symbols and confidence ratings), the quantification of weather sensitivity of those systems (blue symbols and confidence ratings) and the attribution of underlying changes in the climate-related systems to anthropogenic forcing (grey symbols and confidence ratings). The figure can be read as a summary and table of content for the underlying Tables SM16.21 on climate attribution, SM16.22 on impact attribution and SM16.23 on identification of weather sensitivity that provide the more detailed explanations behind each regional or global assessment, including all references. The synthesis was generated in collaboration with ‘detection and attribution contact persons’ from the individual chapters that each includes its own assessment of observed impacts, and contributing authors on individual topics. The synthesis of ‘climate attribution’ studies in Table SM16.21 was particularly informed by the WGI assessment.

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The collapse or transformation of ecosystems is one of the most abrupt potential tipping points associated with climate change. Climate change has started to induce such tipping points, with the first examples including mass mortality in coral reef ecosystems (e.g., Donner et al., 2017; Hughes et al., 2018; Hughes et al., 2019) (high confidence), and changes in vegetation cover triggered by wildfires with climate change suppressing the recovery of the former cover (Tepley et al., 2017; Davis et al., 2019) (low confidence because of the still limited number of studies). Another example of an abrupt change in an ecosystem triggered by a climate extreme is the shift from kelp- to urchin-dominated communities along parts of the Western North America coast (Rogers-Bennett and Catton, 2019; McPherson et al., 2021, see ‘Marine ecosystems—Kelp forest’, Table SM16.22). The loss of kelp forests was induced by a marine heatwave where anthropogenic climate forcing has been shown to have increased the probability for an event of that duration by at least a factor of 33 (Laufkötter et al., 2020). Many terrestrial ecosystems on all continents show evidence of significant structural transformation, including woody thickening and ‘greening’ in more water-limited ecosystems, with a significant role played by rising atmospheric CO2 fertilisation in these trends (high confidence) (Fang et al., 2017; Stevens et al., 2017; Burrell et al., 2020). Climate change is identified as a major driver of increases in burned areas in the Western USA (high confidence, see ‘Terrestrial ecosystems—Burned areas’, Table SM16.22).

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Observed trends in high river flows strongly vary across regions but also with the considered time period (Gudmundsson et al., 2019; Gudmundsson et al., 2021) as influenced by climate oscillations such as the El Niño-Southern Oscillation (Ward et al., 2014). On the global scale, the spatial pattern of observed trends is largely explained by observed changes in climate conditions as demonstrated by multi-model hydrological simulations forced by observed weather, while the considered direct human influences play only a minor role on global scale (Gudmundsson et al., 2021, see ‘Water distribution—Flood induced economic damages’, Table SM16.22). The annual total number of reported fatalities from flooding shows a positive trend (1.5% yr −1 from 1960 to 2013, Tanoue et al., 2016) which appears to be primarily driven by changes in exposure dampened by a reduction in vulnerability, while climate-induced increases in affected areas show only a weak positive trend on the global scale. However, the signal of climate change in flood-induced fatalities may be lost in the regional aggregation, where effects of increasing and decreasing hazards may cancel out. Thus, a climate-driven increase in flood-induced damages becomes detectable in continental subregions with increasing discharge, while the signal of climate change may not be detectable without disaggregation (Sauer et al., 2021, see ‘Water distribution—Flood induced economic damages’, Table SM16.22). Compared with river floods, the analysis of impacts of long-term changes in the climate-related systems on the reduction in water availability is much more fragmented and reduced to individual case studies regarding associated societal impacts (see ‘Water distribution—Reductions in water availability + induced damages and fatalities’, Table SM16.22). At the same time, weather fluctuations have led to reductions in water availability with severe societal consequences and high numbers of drought-induced fatalities and damages in particular in Africa and Asia (see ‘Water distribution—Reductions in water availability + induced damages and fatalities’, Table SM16.23) and impacts on malnutrition (see ‘Food system—Malnutrition’, Table SM16.23). Although anthropogenic climate forcing has increased droughts’ intensity or probability in many regions of the world (medium confidence), (see ‘Atmosphere—Droughts’, Table SM16.21) the existing knowledge has not yet been systematically linked to attribute long-term trends in malnutrition, fatalities and damages induced by reduced water availability to anthropogenic climate forcing or long-term climate change. For impacts of individual attributable drought events, see Table 4.5 and ‘Water distribution—Reductions in water availability + induced damages and fatalities’, Table SM16.23.

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With their enormous destructive power, tropical cyclones represent a major risk for coastal systems (see ‘Coastal systems—Damages’, Table SM16.23). Despite its relevance, confidence in the influence of anthropogenic climate forcing on the strength and occurrence probability of tropical storms themselves is still low (see ‘Coastal systems—Tropical cyclone activity’, Table SM16.21). However, anthropogenic climate forcing has become the dominant driver of sea level rise (high confidence) (see ‘Coastal systems—Mean and extreme sea levels’, Table SM16.21) and has increased the risk of coastal flooding, including inundation induced by tropical cyclones. In addition, anthropogenic climate forcing has increased the amount of rainfall associated with tropical cyclones (high confidence) (Risser and Wehner, 2017; Van Oldenborgh et al., 2017; Wang et al., 2018, for Hurricane Harvey in 2017; Patricola and Wehner, 2018, for hurricane Katrina in 2005, Irma in 2017 and Maria in 2017, see ‘Atmosphere—Heavy precipitation’, Table SM16.21). Assuming that the extreme rainfall is a major driver of the total damages induced by the tropical cyclone, the contribution of anthropogenic climate forcing to the occurrence probability of the observed rainfall (fraction of attributable risk) can also be considered the fraction of attributable risk of the hurricane-induced damages or fatalities (Frame et al., 2020; Clarke et al., 2021, see ‘Coastal systems—Damages’, Table SM16.22). However, first studies do not only quantify the change in occurrence probabilities but translate the actual change in climate-related systems into the additional area affected by flooding in a process-based way (Strauss et al., 2021 for the contribution of anthropogenic sea level rise (SLR) to damages induced by Hurricane Sandy; Wehner and Sampson, 2021 for the contribution increased precipitation to damages induced by Hurricane Harvey) and attribute a considerable part of the observed damage to anthropogenic climate forcing. In addition, disruption of local economic activity in Annapolis, Maryland and loss of areas and settlements in Micronesia and Solomon Islands have been attributed to relative SLR (Nunn et al., 2017; Albert et al., 2018; Hino et al., 2019), while permafrost thawing and sea ice retreat are additional drivers of observed coastal damages in Alaska (Albert et al., 2016; Smith and Sattineni, 2016; Fang et al., 2017).

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There is nearly universal evidence that non-optimal ambient temperatures increase mortality (high confidence), with notable heterogeneity only in the shape of the temperature–mortality relationship across geographical regions but often sharply growing relative risks at the outer 5% of the local historical temperature distributions (Gasparrini et al., 2015; Guo et al., 2018; Carleton et al., 2020; Zhao et al., 2021; see ‘Other societal impacts—Heat-related mortality’, Table SM16.23). Significant advances have been made since AR5 regarding the analysis of temperature-related excess mortality in previously under-researched regions, such as developing countries and (sub)tropical climates (e.g South-East Asia: Dang et al., 2016; Ingole et al., 2017; Mazdiyasni et al., 2017; South Africa: Wichmann, 2017, Scovronick et al., 2018; the Middle East: Alahmad et al., 2019, Gholampour et al., 2019; and Latin America: Péres et al., 2020). Progress has also been made with regard to temporal changes in temperature-related excess mortality and underlying population vulnerability over time. Heat-attributable mortality fractions have declined over time in most countries owing to general improvements in health care systems, increasing prevalence of residential air conditioning, and behavioural changes. These factors, which determine the susceptibility of the population to heat, have predominated over the influence of temperature change (see ‘Other societal impacts—Heat-related mortality’, Table SM16.22, De’Donato et al., 2015; Arbuthnott et al., 2016; Vicedo-Cabrera et al., 2018a). Important exceptions exist, for example, where unprecedented heatwaves have occurred recently. No conclusive evidence emerges regarding recent temporal trends in excess mortality attributable to cold exposure (Vicedo-Cabrera et al., 2018b). Quantitative detection and attribution studies of temperature-related mortality are still rare. One study (Vicedo-Cabrera et al. 2021), using data from 43 countries, found that 37% (range 20.5–76.3%) of average warm-season heat-related mortality during recent decades can be attributed to anthropogenic climate change (medium confidence, see ‘Other societal impacts—Heat-related mortality’, Table SM16.22). Studying excess mortality associated with past heatwaves, such as the 2003 or 2018 events in Europe, even higher proportions of deaths attributable to anthropogenic climate change have been reported for France and the UK (Mitchell et al., 2016; Clarke et al., 2021). Formal attribution studies encompassing cold-related mortality are quasi non-existent. The very few studies from Europe and Australia (Christidis et al., 2010; Åström et al., 2013; Bennett et al., 2014) find weak impacts of climate change on cold-associated excess mortality, with contradictory outcomes both towards higher and lower risks (low confidence, see ‘Other societal impacts—Heat-related mortality’, Table SM16.22).

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So far, there are few individual studies attributing observed economic damages to long-term climate change except for damages induced by river flooding, droughts and tropical cyclones (see ‘Coastal systems—Damages’, ‘Water distribution—Flood-induced damages’, and ‘Water distribution—Reduction in water availability + induced damages and fatalities’, Table SM16.22). In addition, the empirical findings on the sensitivity of macroeconomic development to weather fluctuations and extreme weather events have been used to estimate the cumulative effect of historical warming on long-term economic development (see ‘Other societal impacts—Macroeconomic output’, Table SM16.22): anthropogenic climate change is estimated to have reduced gross domestic product (GDP) growth over the last 50 years, with substantially larger negative effects on developing countries and in some cases positive effects on colder industrialised countries (low confidence) (Diffenbaugh and Burke, 2019). Globally, between-country inequality has decreased over the last 50 years. Climate change is estimated to have substantially slowed down this trend, that is, increased inequality compared with a counterfactual no-climate-change baseline (low confidence) (Diffenbaugh and Burke, 2019). On a regional level, decreasing rainfall trends in Sub-Saharan Africa may have increased the GDP per capita gap between Sub-Saharan Africa and other developing countries (low confidence) (Barrios et al., 2010). Overall, more research is needed on the impact channels through which extreme weather events and weather variability can hinder economic development, especially in the long term.

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Given the complexity of human migration processes and decisions (e.g., Boas et al., 2019, Cattaneo et al., 2019) and the paucity of long-term, reliable and internally consistent observational data on displacement (IDMC, 2019; IDMC, 2020) and migration (Laczko, 2016), the contribution of long-term changes in climate-related systems to observed human displacement or migration patterns has not been quantified so far, except for individual examples of displacement induced by inland flooding where the heavy precipitation has been attributed to anthropogenic climate forcing and coastal flooding (see ‘Other societal impacts—Displacement and migration’, Table SM16.22; Section CCP2).

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A key risk is defined as a potentially severe risk and therefore especially relevant to the interpretation of dangerous anthropogenic interference (DAI) with the climate system, the prevention of which is the ultimate objective of the UNFCCC as stated in its Article 2 (Oppenheimer et al., 2014). Key risks are therefore a relevant lens for the interpretation of this policy framing. The severity of a risk is a context-specific judgement based on a number of criteria discussed below. KRs are ‘potentially’ severe because, while some could already reflect dangerous interference now, more typically they may become severe over time due to changes in the nature of hazards (or, more broadly, climatic impact drivers; IPCC, 2021) and/or of the exposure/vulnerability of societies or ecosystems to those hazards. They also may become severe due to the adverse consequences of adaptation or mitigation responses to the risk (on the former, see Section 17.5.1; the latter is not assessed separately here, except as it contributes to risks from climate hazards). Dangerous interferences in this chapter are considered over the course of the 21st century.

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Last, there is growing concern that even ambitious adaptation efforts will not eliminate residual risks from climate change (Section 16.4.2). A synthesis of risk assessments in the recent IPCC Special Reports (Magnan et al., 2021) concludes that high societal adaptation is expected to reduce the aggregated score—the proxy used in the study—of global risk from anthropogenic climate change by about 40% under all RCPs by the end of the century, compared with risk levels projected without adaptation. It, however, also shows that, even for the lowest warming scenario, a residual risk one-third greater than today’s risk level would still remain (with a doubling of today’s aggregated score under the high-emissions scenario).

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Deltas face multiple interacting hazards, many of which over the past decades have been intensified by local and regional anthropogenic developments (e.g., the construction of dams, groundwater extraction, or agricultural irrigation practices) and most of which are expected to be exacerbated by climate change (high confidence) (Giosan et al., 2014; Tessler et al., 2015; Tessler et al., 2016; Arto et al., 2019; Oppenheimer et al., 2019). The most important hazards include SLR, inundation, salinity intrusion, cyclones, storms and erosion, many of which occur in combination. The potential for flooding and inundation depends on the relative sea level rise (RSLR) which results from global and regional SLR as well as local subsidence within the deltas. Subsidence caused by natural and human drivers (mainly compaction and groundwater extraction) is currently the most important cause for RSLR in many deltas and can exceed the rate of climate-induced SLR by an order of magnitude (Oppenheimer et al., 2019). But in higher warming scenarios the relative importance of climate-driven SLR is expected to increase over time (Oppenheimer et al., 2019). In a global study covering 47 major deltas and assessing future trends of sediment delivery across four RCPs, three SSPs (1,2,3) and a projection of future dam construction, Dunn et al. (2019) find most deltas (33 out of the 47) will experience a mean decline of 38% in sediment flux by the end of the century when considering the average of the scenarios. Nienhuis et al. (2020) find in a global assessment that some deltas have gained land through increased sediment load (e.g., through deforestation), but recent land gains are unlikely to be sustained if SLR continues to accelerate. According to the latest assessments, it is virtually certain that global mean sea level will continue to rise over the 21st century, with SLR by 2100likely to reach 0.28–0.55 m in a an SSP1–1.9 and 0.63–1.01 m in an SSP5–8.5 scenario relative to 1995–2014 (IPCC, 2021). The combined effects of local subsidence and GMSL rise result in a significant increase in the potential for inundation of low-lying deltas across all RCPs, with some variation according to regional sea level change rates, without significant further adaptation measures (very high confidence).

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In this AR6 assessment, the transition from moderate to high is based on the high level of observed impacts, and the areas projected to begin undergoing major transformations by 1.5°C (see Cross-Chapter Paper 1, Chapter 2 and SR15 (IPCC, 2018a)). A substantial number of unique and threatened systems are assessed to be in a high risk state owing to the influence of anthropogenic climate change by the 2000–2010 period, when global warming had reached approximately 0.85°C (range 0.7–1°C) (see WGI AR6 Cross-Chapter Box 2.3, Gulev et al., 2021) using the 1995–2014 figure as a proxy for 2000–2010).

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Identification of the transition to very high risk is associated by definition with the reaching of limits to natural and/or societal adaptation. Adaptation which occurs naturally is already included in the risk assessment, but experts also discussed the effect of additional human-planned adaptation in reducing risk levels in RFC1. This additional adaptation could help species to survive in situ despite a changing climate (for example, by reducing current anthropogenic stresses such as over-harvesting), or facilitate the ability of species to shift geographic range in response to changes in climate, and the potential benefits of nature-based solutions and restoration (see Cross-Chapter Box NATURAL, Section 2.6.5.1).

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Assessment of a high level of risk requires a higher level of magnitude, severity and spatial extent of the risks. Events prior to that already had substantial impacts, such as the 2003 European heatwave (IPCC SREX Chapter 9). Examples of impactful events in the early 2010s (at ca. 0.95°C of global warming; WGI AR6 Chapter 2, Gulev et al., 2021) include the 2010 Russian heatwave (Barriopedro et al., 2011) and the 2010 Amazon drought (Lewis et al., 2011). Later impactful events include, among others, the 2013 heatwave in eastern China (Sun et al., 2014), the 2017 tropical cyclone Harvey (Risser and Wehner, 2017; Van Oldenborgh et al., 2017) and the 2018 concurrent North Hemisphere heatwaves in Europe, North America and Asia (Vogel et al., 2019). Very recent events with severe and unprecedented impacts attributed to anthropogenic climate change indicate that thresholds to high risks may already have been crossed at recent levels of global warming (ca. 1.1–1.2°C), including the Siberian fires and the 2019 Australian bushfires that were linked to extreme heat and drought conditions (Van Oldenborgh et al., 2017) and extreme precipitation linked to increased storm activity in the USA (Van Oldenborgh et al., 2017). Severe and unprecedented impacts occurred with current low levels of adaptation (Section 16.2.3.4). The global-scale risk of wildfire considerably degrading ecosystems and increasing illnesses and death of people has been assessed to transition from undetectable to moderate over the range 0.6–0.9°C with high confidence (Chapter 2, Table SM2.5, Figure 2.11).

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The transition from high to very high risk for the RFC2 was not assessed in the AR5 or in SR15. Some new evidence suggests, however, that very high risks associated with weather and climate extremes would be reached at higher levels of global warming. In particular, changes in several hazards would be more widespread and pronounced at 2°C compared with 1.5°C global warming, including increases in multiple and concurrent extremes (IPCC WGI AR6 SPM; IPCC WGI AR6 Chapter 11, IPCC WGI AR6 Chapter 12). On average over land, high temperature events that would have occurred once in 50 years in the absence of anthropogenic climate change are projected to become 13.9 times more likely with 2°C warming, and 39.2 times more likely with 4°C warming (IPCC AR6 WGI SPM Figure SPM.6, IPCC, 2021), indicating a nonlinear increase with warming. Chapter 2 assessed that risk of wildfire transitions from moderate to high over the range 1.5°C to 2.5°C warming (medium confidence, Table SM2.5 , Figure 2.11). The intensity of heavy precipitation events increases overall by about 7% for each additional degree of global warming (IPCC AR6 WGI SPM), while their frequency increases nonlinearly. Events that would have occurred once every 10 years in a climate without human influence are projected to become 1.7 times more likely with 2°C warming, and 2.7 times more likely with 4°C warming (IPCC AR6 WGI SPM Figure SPM.6). Several AR6 regions are projected to be affected by increases in agricultural and ecological droughts at 2°C of global warming, including western North America, central North America, northern Central America, southern Central America, the Caribbean, northern South America, northeastern South America, South American Monsoon, southwestern South America, southern South America, West and Central Europe, the Mediterranean, western Southern Africa, eastern Southern Africa, Madagascar, eastern Australia and southern Australia (IPCC WGI AR6, Chapter 11, Seneviratne et al., 2021). This is a substantially larger number compared with projections at 1.5°C (IPCC WGI AR6, Chapter 11, Seneviratne et al., 2021). In these drying regions, events that would have occurred once every 10 years in a climate without human influence are projected to happen 2.4 times more frequently at 2°C of global warming (IPCC WGI AR6 SPM Figure SPM.6). Urban land exposed to floods and droughts is very likely to have more than doubled between 2000 and 2030, and the risk of flooding accelerates after 2050 (Chapter 4). At 2°C of global warming, there are also significant projected increases in fluvial flood frequency and resultant risks associated with higher populations exposed to these flood risks (Alfieri et al., 2017; Dottori et al., 2018).

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In the Amazon Forest, increases in tree mortality and a decline in the carbon sink are already reported (Brienen et al., 2015; Hubau et al., 2020), and old-growth Amazon Rainforest may have become a net carbon source for the period 2010–2019 (Qin et al., 2021). Estimates which include land use emissions indicate the region may have become a net carbon source (Gatti et al., 2021). Fire activity is an important driver, and both bigger fires (Lizundia-Loiola et al., 2020) and longer fire season (Jolly et al., 2015) have been reported in South America, although this is strongly linked to land use and land use change as well as climate (Kelley et al., 2021), and indeed land use change may be a stronger driver of potential loss of the Amazon Forest than climate change. The risk of climate-change-related loss of the Amazon Forest is assessed already above ‘undetectable’, but has only emerged over the last few years, when global warming had reached 1°C, and is linked to land use as well as GSAT levels. Chapter 2 has assessed ecosystem carbon loss from tipping points in tropical forest and loss of Arctic permafrost, and finds a transition to moderate risk over the range 0.6–0.9°C (medium confidence). Specifically, WGII AR6 Table SM2.5 finds that ‘Primary tropical forest comprised a net source of carbon to the atmosphere, 2001–2019 (emissions 0.6 Gt y −1, net 0.1 Gt y −1) (Harris et al., 2021). Anthropogenic climate change has thawed Arctic permafrost (Guo et al., 2020), carbon emissions 1.7 ± 0.8 Gt y −1, 2003–2017 (Natali et al., 2019)’. This also supports the upper limit for this transition lying at 1°C.

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Finally, it is important to note that ‘attribution to climate change’ does not necessarily mean ‘attribution to anthropogenic climate change’. Instead, according to the IPCC definition, climate change means any long-term change in the climate system, no matter where it comes from.

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Abatzoglou, J.T. and A.P. Williams, 2016: Impact of anthropogenic climate change on wildfire across western US forests. Proc. Natl. Acad. Sci. , 113 (42), 11770–11775.

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Burrell, A., J. Evans and M. De Kauwe, 2020: Anthropogenic climate change has driven over 5 million km 2 of drylands towards desertification. Nat. Commun. , 11 (1), 1–11.

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Dottori, F., et al., 2018: Increased human and economic losses from river flooding with anthropogenic warming. Nat. Clim. Change, 8 (9), 781–786, doi:10.1038/s41558-018-0257-z.

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Dunn, F.E., et al., 2019: Projections of declining fluvial sediment delivery to major deltas worldwide in response to climate change and anthropogenic stress. Environ. Res. Lett. , 14 (8), 84034, doi:10.1088/1748-9326/ab304e.

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Fischer, E.M. and R. Knutti, 2015: Anthropogenic contribution to global occurrence of heavy-precipitation and high-temperature extremes. Nature Clim Change, 5 (6), 560–564.

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Guo, D., et al., 2020: Attribution of historical near-surface permafrost degradation to anthropogenic greenhouse gas warming. Environ. Res. Lett. , 15 (8), 84040, doi:10.1088/1748-9326/ab926 f.

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Mitchell, D., et al., 2016: Attributing human mortality during extreme heat waves to anthropogenic climate change. Environ. Res. Lett. , 11 (7), 74006.

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Patricola, C.M. and M.F. Wehner, 2018: Anthropogenic influences on major tropical cyclone events. Nature, 563 (7731), 339–346.

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Samaniego, L., et al., 2018: Anthropogenic warming exacerbates European soil moisture droughts. Nature Clim Change, 8 (5), 421–426.

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Strauss, B., et al., 2021: Economic damages from hurricane Sandy attributable to sea level rise caused by anthropogenic climate change. Nat Commun, 12 (1), 15.

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Takakura, J., 2019: Dependence of economic impacts of climate change on anthropogenically directed pathways. Nat. Clim. Chang. , 9 (10), 737–741, doi:10.1038/s41558-019-0578-6.

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Tessler, Z.D., et al., 2016: A global empirical typology of anthropogenic drivers of environmental change in deltas. Sustain Sci, 11 (4), 525–537, doi:10.1007/s11625-016-0357-5.

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Zwiers, F.W., X. Zhang and Y. Feng, 2011: Anthropogenic influence on long return period daily temperature extremes at regional scales. J. Climate, 24 (3), 881–892.

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Climate justice questions arise about the observed differential L&Ds due to climatic hazards to affected populations in close connection with their vulnerability (Wrathall et al., 2015). Individual extreme weather events attributable to climate change result in L&Ds in communities and societies, which allow a quantification of the differential impacts of such events on different groups (Hoegh-Guldberg et al., 2019a). Considering the disproportionately adverse impacts of climatic hazard on most vulnerable groups and regions and their relatively minor contribution to anthropogenic climate change (Mora et al., 2018; Robinson and Shine, 2018), it is evident that vulnerability reduction and adaptation to climate change have also to be seen as an issue of climate justice and climate just development (Byers et al., 2018).

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Across all geographical regions, there is evidence that anthropogenic climate change is hindering poverty alleviation and thereby constraining responses to climate change in five main ways:

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Between 2015 and 2017, the Western Cape region experienced an unprecedented three consecutive years of below average rainfall, leading to acute water shortages, most prominently in the city of Cape Town (Sousa et al., 2018). Anthropogenic climate change made the drought five to six times more likely (Pascale et al., 2020; see also AR6 WGI Chapter 10, Section 10.6.2). The severity of the drought presented new challenges to the existing management and governance capacity to ensure equitable and sustainable water service delivery. The city’s water supply infrastructure and demand management practice were unprepared for the ‘rare and severe’ event of three consecutive years of below average rainfall (Wolski, 2018; Muller, 2019). Despite a potential total storage volume of about 900,000 Ml of water (enough water for around a year and a half of normal usage, after taking evaporation into account), Cape Town’s reservoirs fell from 97% full in 2014 to less than 20% in May 2018 (Ouweneel et al., 2020; Cole et al., 2021). The drought saw residents queue for water as restrictions were imposed together with threats of closure of water provision to households (Sorensen, 2017; Scheba and Millington, 2018). Poor communication in the early stages of the drought (Hellberg, 2020) and a lack of trust in the administration contributed to a near-panic situation at the threat of ‘Day Zero’ as dams almost ran dry in the first half of 2018 (Enqvist and Ziervogel, 2019; Simpson et al., 2020c). ‘Day Zero’ was avoided largely through public response, water demand management and the 2018 winter rains (Sorensen, 2017; Booysen et al., 2019a; Muller, 2019; Rodina, 2019b; Matikinca et al., 2020). At a household level, responses to the drought showed everyday residents can display unprecedented degrees of resilience (Sorensen, 2017), including behavioural and attitudinal shifts and technological innovation across the full socioeconomic spectrum (Ouweneel et al., 2020). But the private nature of some of these responses extended existing inequality in water access through privileged forms of ‘gated adaptation’ by elites which conventional water governance arrangements were unprepared for (Simpson et al., 2019b; Simpson et al., 2020a).

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As the consumption of animal protein and products rises along with global standards of living, CRD will require transformations in livestock-centred livelihoods. Livestock are a key contributor to global food security, especially in marginal lands where animal products are a unique source of energy, protein and micronutrients (FAO, 2017; IPCC, 2019a). However, they also contribute disproportionately to total annual anthropogenic GHG emissions globally and influence climate through land use change, processing and transport through emitting CO2, animal production by increasing methane emissions, and feed and manure production by emitting CO2, nitrous oxide, and methane, (Rojas-Downing et al., 2017). Mitigation of livestock emissions can be achieved by implementation of various technologies and practices such as improving diets to reduce enteric fermentation, improving manure management and improving animal nutrition and genetics (Rojas-Downing et al., 2017); altering land use for grazing and feed production, altering feeding practices, improving manure treatment and reducing herd size (Zhang et al., 2017). Adaptation strategies in the livestock sector include changes in animal feeding, genetic manipulation, alterations in species and/or breeds (Zhang et al., 2017), shifting to mixed crop–livestock systems (Rojas-Downing et al., 2017), production and management system modifications, breeding strategies, institutional and policy changes, science and technology advances, and changing farmers’ perceptions and adaptive capacity (USDA, 2013).

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Otto, F. E. et al., 2018: Anthropogenic influence on the drivers of the Western Cape drought 2015–2017. Environmental Research Letters, 13 (12), 124010, doi: https://doi.org/10.1088/1748-9326/aae9 f9.

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van Oldenborgh, G. J. et al., 2021a: Attribution of the Australian bushfire risk to anthropogenic climate change. Natural Hazards and Earth System Sciences, 21 (3), 941–960, doi: https://doi.org/10.5194/nhess-21-941-2021.

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This report evaluates key risks—potentially severe risks—meriting society’s full attention globally and regionally across sectors, in order to inform judgements about dangerous anthropogenic interference with the climate system (Oppenheimer et al., 2014; Mach et al., 2016; see also Sections 16.1.2; 16.4; WGI Section 1.2.4.1). As described detail in Chapter 16, evaluation of key risks is based on expert judgement applied to all relevant lines of evidence, with a focus on the role of societal values in determining the importance of a risk. Specific criteria considered relate to the magnitude of adverse consequences, including the potential for irreversibility, thresholds, or cascading effects; the likelihood of adverse consequences; the timing of the risk; and the ability to respond to the risk (Section 16.5.1).

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Anthropogenic climate change is unequivocal and ongoing. The detection of specific changes in the climate and their diverse impacts on people and nature is advancing, with robust attribution of climate change to GHG emissions as well as to other contributing factors (e.g., socioeconomic development, land use change). In the AR6, advances include an increasing ability to link individual extreme weather and climate events to emissions of GHGs, increasing identification of impacts for societies and economies and strong linkages in the attribution methods across Working Groups (Cross-Working Group Box: ATTRIBUTION in Chapter 1).

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Third, CCPs are dedicated to typological regions, defined in the Annex II: Glossary as regions that share one or more specific features (known as ‘typologies’), such as geographic location (e.g., coastal), physical processes (e.g., monsoons), biological (e.g., coral reefs, tropical forests, deserts), geological (e.g., mountains) or anthropogenic (e.g., megacities), and for which it is useful to consider the common climate features. Typological regions are generally discontinuous (such as monsoon areas, mountains, deserts and megacities) and are specifically used to integrate across similar climatological, geological and human domains.

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The concept of Loss and Damage (with capitalised letters, L&D) refers to the discussion point under the UNFCCC, which is to ‘address loss and damage associated with impacts of climate change, including extreme events and slow onset events, in developing countries that are particularly vulnerable to the adverse effects of climate change’. Lowercase letters of losses and damages refer broadly to harm from (observed) impacts and (projected) risks (IPCC, 2018a). The IPCC report uses the latter for its assessment on loss and damage which may provide useful information for the former. L&D associated with climate change has gained importance supported by the robust scientific evidence on anthropogenic climate change amplifying the frequency, intensity and duration of climate-related hazards (Mechler et al., 2019). Loss and damage associated with those residual losses and damages that are felt beyond the adaptation actions taken imply a sense of limits to adaptation at a given time and within a spatial context (Tschakert et al., 2017). IPCC’s SRCCL also underlined the unavoidable loss and damage due to changes in tropical and extratropical cyclones and marine heatwaves, where adaptation and resilience limits are being exceeded for the people and ecosystems (Cross-Chapter Box LOSS in Chapter 17; IPCC, 2019a).

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Loss and damage has emerged as an important topic in international climate policy (Surminski and Lopez, 2015; Roberts and Pelling, 2016; Boyd et al., 2017). It originated in assessing compensation for SIDS, related to sea level rise impacts. It has since become formalised under the UNFCCC, through the establishment of the Warsaw International Mechanism (UNFCCC, 2013) and Article 8 of the Paris Agreement (UNFCCC, 2015b). The Warsaw International Mechanism promotes the implementation of comprehensive risk management approaches, improves understanding of slow onset events, non-economic losses and human mobility (migration, displacement), and enhances action and support, including finance, technology and capacity building to avert, minimise and address loss and damage associated with climate change impacts, particularly on vulnerable and developing countries (UNFCCC, 2021). Different actors have defined loss and damage differently in reference to climate change impacts and responses (Surminski and Lopez, 2015; Roberts and Pelling, 2016; Boyd et al., 2017; McNamara and Jackson, 2019). These understandings include the following: (a) an adaptation and mitigation perspective linking all human-induced climate change impacts to potential loss and damage and a mandate to avoid dangerous anthropogenic interference; (b) a risk management perspective emphasising interconnections among disaster risk reduction, climate change adaptation and humanitarian efforts; (c) a limits to adaptation perspective focused on residual loss and damage beyond adaptation and mitigation; and (d) an existential perspective highlighting inevitable harm and unavoidable transformation for some people and systems. This report assesses the growing literature on loss and damage across sectors and regions linking with adaptation constraints and limits, GWL and incremental and or transformational adaptation to climate risks (Section 8.3.4; Cross-Chapter Box LOSS in Chapter 17; Box 10.7).

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Hegerl, G.C., O. Hoegh-Guldberg, G. Casassa, M.P. Hoerling, R. Kovats, C. Parmesan, D.W. Pierce and P.A. Stott, 2010: Good practice guidance paper on detection and attribution related to anthropogenic climate change. In: Meeting Report of the Intergovernmental Panel on Climate Change Expert Meeting on Detection and Attribution of Anthropogenic Climate Change. IPCC Working Group I Technical Support Unit, University of Bern, Bern.

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Undorf, S., et al., 2018: Detectable impact of local and remote Anthropogenic aerosols on the 20th century changes of West African and South Asian monsoon precipitation. J. Geophys. Res. Atmos. , 123 (10), 4871–4889.

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Locally, the urban heat island also elevates temperatures within cities relative to their surroundings. It is caused by physical changes to the surface energy balance of the pre-urban site from urbanisation, resulting from the thermal characteristics and spatial arrangement of the built environment, and anthropogenic heat release (Oke et al., 2017; Chow et al., 2014; Susca and Pomponi, 2020; Doblas-Reyes et al., 2021 FAQ10.1). A considerable body of evidence exists on how the multi-scale impacts and consequent risks arise when local elevated temperatures within settlements are enhanced by climate change, with specific elements of this affecting megacities (Darmanto et al., 2019). The urban heat island itself is amplified during heatwaves (Founda and Santamouris, 2017), but the extent to which varies regionally and by time of day (Ward et al., 2016a; Zhao et al., 2018b; Eunice Lo et al., 2020). When combined with warming induced by urban growth, extreme heat risks are expected to affect half of the future urban population, with a particular impact in the tropical Global South and in coastal cities and settlements (Huang et al., 2019; Section CCP2.2.2; Table CCP2.A.1).

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For wildfires at the WUI, anthropogenic climate change, natural weather variability, expansion of human settlement and a legacy of fire suppression are key factors in determining fire risk (Abatzoglou and Williams, 2016; Knorr, Arneth and Jiang, 2016; van Oldenborgh et al., 2020). Recent wildfires in Australia and California both occurred under hot and dry weather conditions exacerbated by climate change, and resulted in substantial property damage along the WUI, ecosystem destruction and lives lost (Brown et al., 2020; Lewis et al., 2020; Yu et al., 2020). Future climate risk of fires at the WUI are likely (medium confidence), and are compounded by projected urban development along the WUI within several regions, such as in the Western USA (Syphard et al., 2019), Australia (Dowdy et al., 2019) and the Bolivian Chiquitania (Devisscher et al., 2016).

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Air Pollution. Despite recent observed improvements in air quality arising from COVID-19 restrictions (Krecl et al., 2020; Naik et al., 2021 Cross-Chapter Box 6.1), significant risks to human health in cities leading to premature mortality very likely arise from exposure to decreased outdoor air quality from a combination of biogenic (e.g., wildfires at the WUI that advect into the urban atmosphere [Reddington et al., 2014; Naik et al., 2021 Chapter 12 Box 12.1]) and anthropogenic sources that are influenced by climate change (e.g., fine particulate matter such as PM2.5, tropospheric ozone, oxides of nitrogen and volatile organic compounds [Burnett et al., 2018; Knight et al., 2016; Turner et al., 2016; West et al., 2016; Chang et al., 2019b; Li et al., 2019a; Alexader, Luisa and Molina, 2016; Naik et al., 2021 Sections 6.5.1, 6.7.1.1, 6.7.1.2]). Risks of premature mortality from indoor air pollution in cities, arising from biomass burning for heating in winter or cooking, indoor pesticide use or exposure to volatile organic compounds from poor thermal insulation in buildings, are also likely to occur (Leung, 2015; Peduzzi et al., 2020; Cross-Chapter Box HEALTH in Chapter 7).

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The mortality risk for several pollutants, for example PM2.5, is considerable (high confidence). Current estimates indicate that 95% of global population live in areas where ambient PM2.5 exceeds the WHO guideline of annual average exposure of 10 µg m −3 (Shaddick et al., 2018a; Shaddick et al., 2018b; Chang et al., 2019b). Among the 250 most populous urban areas, estimated PM2.5 concentrations are generally highest in cities in Africa, South Asia, the Middle East and East Asia; PM2.5 in many cities in North Africa and the Middle East is likely due mainly to wind-blown dust, whereas that in South Asia and East Asia are mainly anthropogenic in origin (Anenberg et al., 2019). However, data on PM2.5 concentrations are unavailable in many cities in low- and middle-income countries owing to a lack of measurements (Martin et al., 2019).

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For some air pollutants, for example concentrations of PM2.5 in several US, Western European and Chinese cities have recently decreased as a result of clean air regulations that have controlled emissions from sources such as motor vehicles, fossil fuel power plants and major industries (Zheng et al., 2018a; Fleming et al., 2018). These decreases have brought substantial improvements in public health in settlements within these regions (Ciarelli et al., 2019; Zhang et al., 2018). In South Asia, Southeast Asia and Africa, however, concentrations of other air pollutants, for example tropospheric ozone, oxides of nitrogen and volatile organic compounds are likely to continue to grow and peak by mid-century before they subside due to global urbanisation assumptions embedded in the SSPs (Naik et al., 2021 Sections 6.2.1; 6.7.1.2). Broadly, future air pollutant emissions are projected to decline globally by 2050 as societies become wealthier and more willing to invest in air pollution controls, but the trajectories vary among pollutants, world regions and scenarios (Silva et al., 2016b; Rao et al., 2017; Silva et al., 2016c). Whereas cities in East Asia and South Asia currently have large exposure to anthropogenic air pollution, African cities may emerge by 2050 as the most polluted because of growing populations and demand for energy, increased urbanisation and relatively weak regulations to control emissions (Liousse et al., 2014).

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In general, the most promising adaptation measures are a combination of solar shading with increased levels of insulation and ample possibilities to apply natural ventilation to cool down a building (e.g., van Hooff et al., 2014; Makantasi and Mavrogianni, 2016; Fosas et al., 2018; Barbosa, Vicente and Santos, 2015; Taylor et al., 2018; Triana, Lamberts and Sassi, 2018; Dodoo and Gustavsson, 2016). However, it must be noted that the cooling potential of natural ventilation will decrease in the future because of increasing outdoor air temperatures (Gilani and O’Brien, 2020). Increased insulation (including through green solutions) without shading and ventilation can also lead to adverse impacts through the lowering of nighttime cooling (Reder et al., 2018). Similarly, air conditioning performance also decreases with increasing outdoor temperatures, in addition to being maladaptive where use increases anthropogenic heat emissions into the urban area, and global greenhouse gas emissions if powered by carbon intensive energy systems (Wang et al., 2018c).

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Abatzoglou, J.T. and A.P. Williams, 2016: Impact of anthropogenic climate change on wildfire across western US forests. Proc. Natl. Acad. Sci. , 113 (42), 11770–11775.

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Chow, W.T.L., et al., 2014: A multi-method and multi-scale approach for estimating city-wide anthropogenic heat fluxes. Atmospheric Environ. , 99, 64–76.

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Li, K., et al., 2019a: Anthropogenic drivers of 2013–2017 trends in summer surface ozone in China. Proc. Natl. Acad. Sci. , 116 (2), 422–427.

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Salamanca, F., M. Georgescu, A. Mahalov, M. Moustaoui and M. Wang, 2014: Anthropogenic heating of the urban environment due to air conditioning. J. Geophys. Res. Atmos. , 119 (10), 5949–5965.

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Silva, R.A., Z. Adelman, M.M. Fry and J.J. West, 2016b: The Impact of Individual Anthropogenic Emissions Sectors on the Global Burden of Human Mortality due to Ambient Air Pollution. Environ. Health Perspect. , 124 (11), 1776–1784.

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van Oldenborgh, G.J., et al., 2020: Attribution of the Australian bushfire risk to anthropogenic climate change. Nat. Hazards Earth Syst. Sci. Discuss. , 21 (3), 1–46.

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Wang, Y., Y. Li, S. Di Sabatino, A. Martilli and P. Chan, 2018c: Effects of anthropogenic heat due to air-conditioning systems on an extreme high temperature event in Hong Kong. Environ. Res. Lett. , 13 (3), 34015.

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Wu, W.-T., Y.-X. Zhou and B. Tian, 2017: Coastal wetlands facing climate change and anthropogenic activities: A remote sensing analysis and modelling application. Ocean. Coast. Manag. , 138, 1–10.

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Yang, S., K. Xu, J. Milliman, H. Yang and C. Wu, 2015: Decline of Yangtze River water and sediment discharge: Impact from natural and anthropogenic changes. Sci. Rep. , 5 (1), 1–14.

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Zheng, B., et al., 2018a: Trends in China’s anthropogenic emissions since 2010 as the consequence of clean air actions. Atmos. Chem. Phys. , 18 (19), 14095–14111.

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Eleven categories of diseases and health outcomes have been identified in this assessment as being climate-sensitive through direct pathways (e.g., heat and floods) and indirect pathways mediated through natural and human systems and economic and social disruptions (e.g., disease vectors, allergens, air and water pollution, and food system disruption) (high confidence). A key challenge in quantifying the specific relationship between climate and health outcomes is distinguishing the extent to which observed changes in prevalence of a climate-sensitive disease or condition are attributable directly or indirectly to climatic factors as opposed to other non-climatic causal factors (Ebi et al., 2020). A subsequent challenge is then determining the extent to which those observed changes in health outcomes associated with climate are attributable to events or conditions associated with natural climate variability compared to persistent human induced shifts in the mean and/or the variability characteristics of climate (i.e., anthropogenic climate change). The context within which the impacts of climate change affect health outcomes and health systems is described in this chapter as being a function of risk, which is in turn a product of interactions between hazard, exposure and vulnerability (Chapter 1), with the impacts in turn having the potential to reinforce vulnerability and/or exposure to risk (Figure 7.4).

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Wide ranging knowledge regarding the specific detection of heat- and cold-related mortality/morbidity and its attribution to observed climate change is lacking . Although there has been an observed increase in winter-season temperatures for a number of regions, to date there is variable evidence for a consequential reduction in winter mortality and susceptibility to cold over time due to milder winters; some countries demonstrate decreasing trends, while other countries show stable or even increasing trends in cold-attributable mortality fractions over time (e.g., Arbuthnott et al. (2020); Åström et al. (2013); Diaz et al. (2019); Hajat (2017); Hanigan et al. (2021); Lee et al. (2018b)). While there is a burgeoning literature on the attribution of extreme heat events to climate change (e.g., Vautard et al. (2020)), the number of studies that assess the extent to which observed changes in heat-related mortality may be attributable to climate change is small (Ebi et al., 2020). During the 2003 European heatwave, anthropogenic climate change increased the risk of heat-related mortality by approximately 70% and 20% for London and Paris, respectively (Mitchell et al., 2016). For the severe heat event across Egypt in 2015, the impact on human discomfort was 69% (±17%) more likely due to anthropogenic climate change (Mitchell, 2016), and for Stockholm, Sweden, it has been estimated that mortality due to temperature extremes for 1980 to 2009 was double what would have occurred without climate change (Åström et al., 2013). To date there has only been one multi-country attempt to quantify the heat-related human health impacts that have already occurred due to climate change. Based on an analysis of 732 locations spanning 43 countries for the 1991–2018 period, the study found that on average 37.0% (inter-quartile range 20.5–76.3%) of warm-season heat-related deaths can be attributed to anthropogenic climate change, equivalent to an average mortality rate of 2.2/100,000 (median: 1.67/100,000; interquartile range: 1.08–2.34/100,000). Regions with a high attributed percentage (> 50%) include southern and western Asia (Iran and Kuwait), Southeast Asia (Philippines and Thailand) and several countries in Central and South America. Those with lower values (< 35%) include Western Europe (the Netherlands, Germany and Switzerland), eastern Europe (Moldova, the Czech Republic and Romania), southern Europe (Greece, Italy, Portugal and Spain), North America (USA) and eastern Asia (China, Japan and South Korea) (Vicedo-Cabrera et al., 2021). Due to data restrictions, some of the poorest and most susceptible regions to climate change and increases in heat exposure, such as west and east Africa (Asefi-Najafabady et al., 2018; Sylla et al., 2018) and south Asia, could not be included in the analysis (Mitchell, 2021).

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Weather events and climate conditions can act as direct drivers of migration and displacement (e.g., destruction of homes by tropical cyclones) and as indirect drivers (e.g., rural income losses and/or food insecurity due to heat- or drought-related crop failures that in turn generate new population movements) (high confidence). Extreme storms, floods and wildfires are strongly associated with high levels of short- and long-term displacement, while droughts, extreme heat and precipitation anomalies are more likely to stimulate longer-term changes in migration patterns (Kaczan and Orgill-Meyer, 2020; Hoffmann et al., 2020). Longer-term environmental changes attributable to anthropogenic climate change—such as higher average temperatures, desertification, land degradation, biodiversity loss and sea level rise—have had observed effects on migration and displacement in a limited number of locations in recent decades but are projected to have wider-scale impacts on future population patterns and migration, and are therefore assessed in Section 7.3.2 (Projected Risks).

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Climate change may alter regional and local exposures to anthropogenic chemical contaminants (medium agreement, low evidence). Changes in future occurrences of wildfires could lead to a 14% increase in global emissions of mercury by 2050, depending on the scenarios used (Kumar et al., 2018a). Mercury exposure via consumption of fish may be affected by warming waters. Warming trends in the Gulf of Maine could increase the methyl mercury levels in resident tuna by 30% between 2015 and 2030 (Schartup et al., 2019). An observed annual 3.5% increase in mercury levels was attributed to fish having higher metabolism in warmer waters, leading them to consume more prey. The combined impacts of climate change and the presence of arsenic in paddy fields are projected to potentially double the toxic heavy metal content of rice in some regions, potentially leading to a 39% reduction in overall production by 2100 under some models (Muehe et al., 2019).

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As outlined in 7.2, the most common drivers of observed climate-related migration and displacement are extreme storms (particularly tropical cyclones), floods and droughts (high confidence). The future frequency and/or severity of such events due to anthropogenic climate change are expected to vary by region according to future GHG emission pathways (Naik et al 2021; Regional Chapters, this report), with there being an increased potential for compound effects of successive or multiple hazards (e.g., tropical storms accompanied by extreme heat events (Matthews et al., 2019)). Table 7.2 summarises anticipated changes in future migration and displacement risks due to sudden-onset climate events by region (and by sub-regions for Africa and Asia, where climatic risks vary within the region).

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Mitchell, D., et al., 2016: Attributing human mortality during extreme heat waves to anthropogenic climate change. Environ. Res. Lett. , 11, doi:10.1088/1748-9326/11/7/074006.

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Paerl, H.W., et al., 2016: Mitigating cyanobacterial harmful algal blooms in aquatic ecosystems impacted by climate change and anthropogenic nutrients. Harmful Algae, 54, 213–222, doi:10.1016/j.hal.2015.09.009.

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Smith, M.R. and S.S. Myers, 2018: Impact of anthropogenic CO2 emissions on global human nutrition. Nat. Clim. Change, 8 (9), 834–839, doi:10.1038/s41558-018-0253-3.

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A key risk is defined as a potentially severe risk. In line with AR5, ‘severity’ relates to dangerous anthropogenic interference with the climate system, the prevention of which is the ultimate objective of the United Nations Framework Convention on Climate Change (UNFCCC) as stated in its Article 2 (Oppenheimer et al., 2014). The process for identifying key risks for Africa included reviewing risks from Niang et al. (2014) and assessing new evidence on observed impacts and projected risks in this chapter.

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Anthropogenic climate change is already negatively impacting Africa’s marine biodiversity, ecosystem functioning and services by changing physical and chemical properties of seawater (increased temperature, salinity and acidification, and changes in oxygen concentration, ocean currents and vertical stratification) (high confidence) (Hoegh-Guldberg et al., 2014; 2018). Coastal ecosystems in west Africa are among the most vulnerable because of extensive low-lying deltas exposed to sea level rise, erosion, saltwater intrusion and flooding (Belhabib et al., 2016; UNEP, 2016b; Kifani et al., 2018). In southern Africa, shifting distributions of anchovy, sardine, hake, rock lobster and seabirds have been partly attributed to climate change (Crawford et al., 2015; van der Lingen and Hampton, 2018; Vizy et al., 2018), including southern shifts of 30 estuarine and marine fish species attributed to increased temperature and changes in water circulation from decreased river inflow (Augustyn et al., 2018). Warming sea surface temperatures inhibiting nutrient mixing have reduced phytoplankton biomass in the western Indian Ocean by 20% since the 1960s, potentially reducing tuna catches (Roxy et al., 2016).

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EbA can mitigate flooding and increase the resilience of freshwater ecosystems (Table 9.6). Adaptation in African freshwater ecosystems is heavily influenced by non-climate anthropogenic factors, including land use change, water abstraction and diversion, damming and overfishing (Dodds et al., 2013; Kimirei et al., 2020; UNESCO and UN-Water, 2020). Wetlands and riparian areas support biodiversity, act as natural filtration systems and serve as buffers to changes in the hydrological cycle, thereby increasing the resilience of freshwater ecosystems and the people that rely on them (Ndebele-Murisa, 2014; Musinguzi et al., 2015; Lowe et al., 2019). However, national adaptation programmes of action, NAPs and national communications rarely consider the ecological stability of ecosystems safeguarding the very water resources they seek to preserve (Kolding et al., 2016). Some countries have mandated the protection of riparian zones, but implementation is low (Musinguzi et al., 2015; Muchuru and Nhamo, 2018). Protecting terrestrial areas surrounding Lake Tanganyika benefited fish diversity (Britton et al., 2017). Afforestation reduces water availability but forest restoration and removing invasive plant species can increase water flows in regions facing water insecurity from climate change (Chausson et al., 2020; Le Maitre et al., 2020). Regular, long-term monitoring of African freshwaters would improve understanding of responses to climate change. General principles for this type of monitoring were developed for Lake Tanganyika (Plisnier et al., 2018) and could be applied to develop harmonised, regional monitoring of African lakes, rivers and wetlands (Tamatamah and Mwedzi, 2020)

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Future climate warming is projected to have a substantial adverse impact on food security in Africa and is anticipated to coincide with low adaptive capacity as climate change intensifies other anthropogenic stressors, as 85% of Africa’s poor live in rural areas and mostly depend on agriculture for their livelihoods (Adams, 2018; Mahmood et al., 2019). This highlights the need to prioritise innovative measures for reducing vulnerabilities in African food systems (Fuller et al., 2018; Mahmood et al., 2019).

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Dust events in west Africa have severe health impacts (cardiorespiratory and infectious diseases, including meningitis) (Ayanlade et al., 2020) given the proximity of the Sahara, which produces about half of the yearly global mineral dust (de Longueville et al., 2013). Wildfires are projected to become the main source of particulate matter in west, central and southern Africa under both the lowest and highest future emissions scenarios, whereas, under intermediate scenarios (i.e., SSP3/RCP4.5), anthropogenic sources of particulate matter are projected to exceed that produced by wildfires (Knorr et al., 2017).

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Abatzoglou, J. T., A. P. Williams and R. Barbero, 2019: Global Emergence of Anthropogenic Climate Change in Fire Weather Indices. Geophysical Research Letters, 46 (1), 326–336, doi:10.1029/2018GL080959.

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Bond, W. and N. P. Zaloumis, 2016: The deforestation story: testing for anthropogenic origins of Africa’s flammable grassy biomes. Philosophical Transactions of the Royal Society B: Biological Sciences, 371 (1696), 20150170, doi:10.1098/rstb.2015.0170.

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Dottori, F. et al., 2018: Increased human and economic losses from river flooding with anthropogenic warming. Nature Climate Change, 8 (9), 781–786, doi:10.1038/s41558-018-0257-z.

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Du, L. et al., 2021: Effects of anthropogenic revegetation on the water and carbon cycles of a desert steppe ecosystem. Agricultural and Forest Meteorology, 300, 108339, doi: https://doi.org/10.1016/j.agrformet.2021.108339.

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Funk, C. et al., 2018a: 18. Anthropogenic enhancement of moderate-to-strong El Niño events likely contributed to drought and poor harvests in southern Africa during 2016. Bull. Am. Meteorol. Soc, 99, S91-S96, doi:10.1175/bams-d-17-0112.1.

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Gu, X. et al., 2020: Impacts of anthropogenic warming and uneven regional socio-economic development on global river flood risk. Journal of Hydrology, 590, 125262, doi: https://doi.org/10.1016/j.jhydrol.2020.125262.

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Harrington, L. J. et al., 2016: Poorest countries experience earlier anthropogenic emergence of daily temperature extremes. Environmental Research Letters, 11 (5), 055007, doi:10.1088/1748-9326/11/5/055007.

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Harrod, C., A. Ramirez, J. Valbo-Jorgensen and S. F. Smith, 2018b: Current anthropogenic stress and projected effect of climate change on global inland fisheries. [Barange, M., T. Bahri, M. Beveridge, K. Cochrane and S. Funge-Smith (eds.)]. Food and Agriculture organization of the United Nations, Rome, pp. 393–448. ISBN 9789251306079.

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Iizumi, T. et al., 2018: Crop production losses associated with anthropogenic climate change for 1981–2010 compared with preindustrial levels. International Journal of Climatology, 38 (14), 5405–5417, doi: https://doi.org/10.1002/joc.5818.

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Malherbe, J., F. A. Engelbrecht and W. A. Landman, 2013: Projected changes in tropical cyclone climatology and landfall in the Southwest Indian Ocean region under enhanced anthropogenic forcing. Clim Dyn, 40 (11), 2867–2886, doi: https://doi.org/10.1007/s00382-012-1635-2.

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Midgley, G. F. and W. J. Bond, 2015: Future of African terrestrial biodiversity and ecosystems under anthropogenic climate change. Nature Climate Change, 5 (9), 823–829, doi:10.1038/nclimate2753.

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Muhati, G. L., D. Olago and L. Olaka, 2018: Participatory scenario development process in addressing potential impacts of anthropogenic activities on the ecosystem services of Mt. Marsabit forest, Kenya. Global Ecology and Conservation, 14, doi:10.1016/j.gecco.2018.e00402.

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Olago, D. O. et al., 2021: Lentic-Lotic Water System Response to Anthropogenic and Climatic Factors in Kenya and Their Sustainable Management. In: Climate Change and Water Resources in Africa, pp. 193–218. ISBN 978-3-030-61224-5; 978-3-030-61225-2.

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Onywere, S. M., J. M. Mironga and I. Simiyu, 2012: Use of Remote Sensing Data in Evaluating the Extent of Anthropogenic

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Ortiz-Bobea, A. et al., 2021: Anthropogenic climate change has slowed global agricultural productivity growth. Nature Climate Change, 11 (4), 306–312, doi:10.1038/s41558-021-01000-1.

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Otto, F. E. et al., 2018: Anthropogenic influence on the drivers of the Western Cape drought 2015–2017. Environmental Research Letters, 13 (12), 124010, doi: https://doi.org/10.1088/1748-9326/aae9 f9.

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Plisnier, P.-D., M. Nshombo, H. Mgana and G. Ntakimazi, 2018: Monitoring climate change and anthropogenic pressure at Lake Tanganyika. Journal of Great Lakes Research, 44 (6), 1194–1208, doi:10.1016/j.jglr.2018.05.019.

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Probert, J. R. et al., 2019: Anthropogenic modifications to fire regimes in the wider Serengeti-Mara ecosystem. Global Change Biology, 25 (10), 3406–3423, doi:10.1111/gcb.14711.

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Quesada, B., A. Arneth, E. Robertson and N. d. Noblet-Ducoudré, 2018: Potential strong contribution of future anthropogenic land-use and land-cover change to the terrestrial carbon cycle. Environmental Research Letters, 13 (6), 064023, doi:10.1088/1748-9326/aac4c3.

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Sylla, M. B., N. Elguindi, F. Giorgi and D. Wisser, 2015a: Projected robust shift of climate zones over West Africa in response to anthropogenic climate change for the late 21st century. Climatic Change, 134 (1-2), 241–253, doi:10.1007/s10584-015-1522-z.

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Takakura, J. y. et al., 2019: Dependence of economic impacts of climate change on anthropogenically directed pathways. Nature Climate Change, 9 (10), 737–741, doi:10.1038/s41558-019-0578-6.

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Wang, H. et al., 2016: Detecting cross-equatorial wind change as a fingerprint of climate response to anthropogenic aerosol forcing. Geophysical Research Letters, 43 (7), 3444–3450, doi: https://doi.org/10.1002/2016GL068521.

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Yuan, X., L. Wang and E. F. Wood, 2018: Anthropogenic intensification of southern African flash droughts as exemplified by the 2015/16 season. Bulletin of the American Meteorological Society, 99 (1), S86-S90, doi:10.1175/BAMS-D-17-0077.1.

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European land and freshwater ecosystems (Figure 13.7) are already strongly impacted by a range of anthropogenic drivers (very high confidence), particularly habitats at the southern and northern margins, along the coasts, up mountains and in freshwater systems (Cross-Chapter Paper 1). Interacting with climate change are non-climatic hazards, such as habitat loss and fragmentation, overexploitation, water abstraction, nutrient enrichment and pollution, all of which reduce resilience of biotas and ecosystems (very high confidence). Peatlands in NEU and EEU and other historically important cultural landscapes in Europe are overexploited for forestry, agriculture and peat mining (Page and Baird, 2016; Tanneberger et al., 2017; Ojanen and Minkkinen, 2020). Inland wetland RAMSAR convention sites in Europe, which constitute 47% of the global sites have lost area in WCE and gained in SEU from 1980 to 2014 (Xi et al., 2021). Forests in WCE were impacted by the extreme heat and drought event of 2018, with effects lasting into 2019 (Schuldt et al., 2020) and losses in conifer timber sales in Europe (Hlásny et al., 2021).

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Autonomous species adaptation, via range shifts towards higher latitudes and altitudes and changes in phenology, but also extirpation, have been documented in all European regions (very high confidence) (Figure 13.8). Lowering vulnerability by reducing other anthropogenic impacts (Gillingham et al., 2015), such as land-use change, habitat fragmentation (Eigenbrod et al., 2015; Oliver et al., 2017; Wessely et al., 2017), pollution and deforestation (Chapter 2), enhances adaptation capacity and biodiversity conservation (high confidence) (Ockendon et al., 2018). Protected areas, such as the EU Natura 2000 network, have contributed to biodiversity protection (medium confidence) (Gaüzère et al., 2016; Sanderson et al., 2016; Santini et al., 2016; Hermoso et al., 2018), but 60% of terrestrial species at these sites could lose suitable climate niches at 4°C GWL (Figure Box 13.1.1; EEA, 2017a).

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Since AR5, scientific documentation of observed changes attributed to global warming have proliferated (high confidence). These include ecosystem changes detected in previous assessments, such as earlier annual greening and onset of faunal reproduction processes, relocation of species towards higher latitudes and altitudes (high confidence), and impacts of heat on human health and productivity (high confidence) (Figure 13.27; Table SM13.22; Vicedo-Cabrera et al., 2021). Formal attribution of impacts of compound events to anthropogenic climate change is just emerging, for example, in the recent crop failures due to heat and drought (Toreti et al., 2019a). Also, there is high agreement and medium evidence that particular events attributed to climate change have induced cascading impacts and other impact interactions (Smale et al., 2019; Vogel et al., 2019). In recent decades (2000–2015), economic losses intensified in SEU (high confidence) and were detected for parts of WCE and NEU (medium confidence). (The methodology for detection and attribution is presented in Section 16.2.)

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Bevacqua, E., et al., 2019: Higher probability of compound flooding from precipitation and storm surge in Europe under anthropogenic climate change. Sci. Adv. , 5 (9), eaaw5531, doi:10.1126/sciadv.aaw5531.

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Collet, L., et al., 2015: Water supply sustainability and adaptation strategies under anthropogenic and climatic changes of a meso-scale Mediterranean catchment. Sci. Total Environ. , 536, 589–602, doi:10.1016/j.scitotenv.2015.07.093.

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Dottori, F., et al., 2018: Increased human and economic losses from river flooding with anthropogenic warming. Nat. Clim. Change, 8 (9), 781–786, doi:10.1038/s41558-018-0257-z.

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Geels, C., et al., 2015: Future premature mortality due to O-3, secondary inorganic aerosols and primary PM in Europe – sensitivity to changes in climate, Anthropogenic emissions, population and building stock. Int. J. Environ. Res. Public Health, 12 (3), 2837–2869, doi:10.3390/ijerph120302837.

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Naumann, G., C. Cammalleri, L. Mentaschi and L. Feyen, 2021: Increased economic drought impacts in Europe with anthropogenic warming. Nat. Clim. Change, 11 (June), doi:10.1038/s41558-021-01044-3.

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Samaniego, L., et al., 2018: Anthropogenic warming exacerbates European soil moisture droughts. Nat. Clim. Change, 8 (5), 421–426, doi:10.1038/s41558-018-0138-5.

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Solidoro, C., et al., 2010: Response of the Venice Lagoon ecosystem to natural and anthropogenic pressures over the last 50 years. In: Coastal Lagoons: Critican Habitats and Environmental Change. [Kennish, M. J. and H.W. Paerl (eds.)]. Boca Raton, CRC Press, pp. 483–511.

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Teatini, P., et al., 2011: A new hydrogeologic model to predict anthropogenic uplift of Venice. Water Resour. Res. , 47 (12), W12507, doi:10.1029/2011WR010900.

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Turco, M., et al., 2018: Exacerbated fires in Mediterranean Europe due to anthropogenic warming projected with non-stationary climate-fire models. Nat. Commun. , 9 (1), 3821, doi:10.1038/s41467-018-06358-z.

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Wakelin, S.L., et al., 2015: Modelling the combined impacts of climate change and direct anthropogenic drivers on the ecosystem of the northwest European continental shelf. J. Mar. Syst. , 152, 51–63, doi:10.1016/j.jmarsys.2015.07.006.

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Wang, J., et al., 2020: Anthropogenically-driven increases in the risks of summertime compound hot extremes. Nat. Commun. , 11 (1), 528, doi:10.1038/s41467-019-14233-8.

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The proportion of hurricanes in stronger categories has likely increased globally over the past 40 years, with medium confidence that the onshore propagation speed of hurricanes making landfall in the USA has slowed detectably since 1900 (Seneviratne et al., 2021; Kossin, 2018), contributing to detectable increases in local rainfall and coastal flooding associated with these storms. There is high confidence (Seneviratne et al., 2021) that anthropogenic climate change has contributed to extreme precipitation associated with recent intense hurricanes, such as Harvey in 2017.

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The majority of the climate science community has reached consensus that mean global temperature has increased and human activity is a major cause (Oreskes, 2004; Anderegg et al., 2010; Cook et al., 2013; Cook et al., 2016; IPCC, 2021), setting the context for public policy action. Despite expert scientific consensus on anthropogenic climate change, there is polarisation and an ongoing debate over the reality of anthropogenic climate change in the public and policy domains, with attendant risks to society (high confidence) (Doran and Zimmerman, 2009; Ballew et al., 2019; Druckman and McGrath, 2019; Hornsey and Fielding, 2020; Wong-Parodi and Feygina, 2020). Public perception of consensus regarding anthropogenic climate change can be an important gateway belief, which establishes a crucial precondition for public policy action (van der Linden et al., 2015; van der Linden et al., 2019) by influencing the assessment of climate-change risks and opportunities, and formulation of appropriate mitigation and adaptation responses (Ding et al., 2011; Bolsen et al., 2015; Drews and Van den Bergh, 2016; Doll et al., 2017; Mase et al., 2017; Morton et al., 2017). Trust in experts, institutions and environmental groups is also important (Cologna and Siegrist, 2020; Termini and Kalafatis, 2021).

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In a 2018 survey across 26 nations, people in Canada and Mexico ranked climate change as the top global threat, whereas in the USA climate change ranked third (Poushter and Huang, 2019). The public’s responses to the causes of climate change and risk perceptions in Canada (Mildenberger et al., 2016) and the USA (Howe et al., 2015) have revealed variations among regions (Figure 14.3) and less acceptance of climate change in rural regions than in urban areas. Canadian regions have higher acceptance of climate change (e.g., recognise it is happening and attributable to human activity) than the most liberal areas in the USA (Lachapelle et al., 2012; Mildenberger et al., 2016). Western Canadian regions with high carbon intensity economies had lower acceptance of climate change than the rest of Canada, whereas in the USA perceptions were more stable across regions (Lachapelle et al., 2012). A recent survey in Mexico found that for 73% of respondents climate change represents a major economic, environmental and social threat, and in the most vulnerable states (MX-SE), the perception is that climate-change impacts and extreme events have considerable implications for the way of life in communities (Zamora Saenz, 2018). In a 2017 survey, Azócar et al. (2021) found that 85% of respondents from Mexico acknowledged anthropogenic climate change. Peoples’ experience with extreme events (e.g., hurricanes, high temperatures), socio-demographic characteristics, level of marginalisation and economic and social exclusion, as well as education levels, were important factors influencing perception of climate change in Mexico (Corona-Jimenez, 2018; Alfie and Cruz-Bello, 2021; Azócar et al., 2021). Drawing upon Indigenous knowledge (see Box 14.1) as well as lived experience of recent changes in ice, weather patterns, and species’ phenology and distribution, Indigenous Peoples recognise that change is occurring in their communities and have effective solutions that are grounded in Indigenous world views (Harrington, 2006; Turner and Clifton, 2009; Norton-Smith et al., 2016a ; Savo et al., 2016; Maldonado et al., 2017; Chisholm Hatfield et al., 2018).

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Recent climate-related changes represent cultural threats similar to the ones that occurred when European settlement began in the Americas over 500 years ago (Whyte, 2016; Whyte, 2017). Thus, for Indigenous Peoples, who often disproportionately bear the impacts of climate change, such changes are not novel, but seen as déjà vu (Whyte, 2016). Since livelihoods and subsistence are often directly dependent on the land and water, Indigenous Peoples have direct insights into the localised impacts of global environmental change. Indeed, Indigenous Peoples consider themselves stewards of the land (and water), and have a spiritual duty to care for the land and its flora, fauna and aquatic community, or ‘Circle’ of beings. Indigenous knowledge (IK) has gained recognition for its potential to bolster Western scientific research about climate change. Many recent examples demonstrate the scientific value of IK for resource management in climate-change adaptation and mitigation (e.g., Kronik and Verner, 2010; Maldonado et al., 2013; Wildcat, 2013; Etchart, 2017; Nursey-Bray et al., 2019). For example, Indigenous practices have not only contributed to the present understanding of North American forest fires, but also that the practice of frequent small-scale anthropogenic fires, also called cultural burns, is a key method to prevent large-scale destructive fires (Section 14.7.1). The growing interest and recognised value in these practices, particularly in California, has led to formal agreements with state and federal agencies (Long et al., 2020a; Lake, 2021).

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Anthropogenic climate change has led to warmer and drier conditions (i.e., fire weather) that favour wildland fires in North America (high confidence) (see AR6, WGI, Chapter 12, Ranasinghe et al., 2021). In response, increased burned area in recent decades in western North America has been facilitated by anthropogenic climate change (medium confidence). Annual numbers of large wildland fires and area burned have risen in the past several decades in the western USA (USGCRP, 2017; USGCRP, 2018), and area burned has increased in Canada (although the number of large fires has declined slightly recently) (Gauthier et al., 2014; Natural Resources Canada, 2018; Hanes et al., 2019). Attribution studies have reported that climate change increased burned area in Canada (1959–1999) (Gillett et al., 2004) as well as the western USA (1984–2015) (Abatzoglou and Williams, 2016) and California (1972–2018) (Williams et al., 2019a). Decreased precipitation was the primary climate-change cause of increased burned area in the western USA, with warming a secondary influence (Holden et al. 2018), whereas warming (through aridity) was most important in a California study (Williams et al., 2019a). A drier atmosphere (including reduced precipitation) has been linked to climate change through altered large-scale atmospheric circulation, which then facilitated greater burned area in the western USA (Zhang et al., 2019c). Through anomalous warm and dry conditions, anthropogenic climate change contributed to the extreme fires of 2016 (Kirchmeier-Young et al., 2019 ; Tan et al., 2019) in western Canada and the extreme fire season in 2015 in Alaska (Partain et al., 2017). These studies did not include human activities that influence fire–climate relationships (Syphard et al., 2017).

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Increased fire activity, partly attributable to anthropogenic climate change, has had direct and indirect effects on mortality and morbidity, economic losses and costs, key infrastructure, cultural resources and water resources (medium confidence), although other factors, such as increasing populations in the wildland–urban interface, have also contributed. During 2000–2018, significant fire events claimed 315 lives in the USA (NOAA, 2019); the economic impacts (e.g., capital, health, indirect losses from economic disruption) from the 2018 California fires were 149 billion USD (Wang et al., 2021). Poor air quality from fires caused increased respiratory distress (very high confidence); exposure extends long distances from the fire source (Section 14.5.6.3). In addition to public and private property damage and loss, fires have caused irretrievable losses from archaeological and historical sites (Ryan et al., 2012). Post-fire conditions have created unanticipated challenges for communities’ water supply operations (Bladon et al., 2014; Návar, 2015; Martin, 2016) by altering water quality and availability (Smith et al., 2011; Bladon et al., 2014; Robinne et al., 2020) or public safety by increasing exposure to mass wasting events after extreme rainfall events (Cui et al., 2019; Kean et al., 2019). California utilities have proactively shut down parts of their electricity grid to reduce risk of fire during extreme weather, and substantial numbers of people will be increasingly vulnerable to this action in the coming decades (Abatzoglou et al., 2020).

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North America’s dams, levees, wastewater-management and water conveyance facilities have improved water supply safety and have reduced flood and drought risks, but a substantial portion of that infrastructure is ageing and inadequate for modern conditions (Ho et al., 2017; Tellman et al., 2018; Carlisle et al., 2019; FEMA, 2019; ASCE, 2021). Increasingly heavy precipitation from a variety of storm types has affected parts of North America (Feng et al., 2016; Prein et al., 2017a; Kunkel and Champion, 2019; Kunkel et al., 2020), contributing to contamination from combined sewer overflows (Olds et al., 2018) and increased flood damages that are partially attributed to anthropogenic climate change (van der Wiel et al., 2017; Davenport, 2021). Extreme precipitation events have overwhelmed water control infrastructure, imperilling public safety and contributing to extensive damages in parts of North America (Kytomaa et al., 2019; Vano et al., 2019; White et al., 2019). Damages stem from extremity of the event and prior land-use and infrastructure decisions (high confidence).

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In South Carolina, 5 days of heavy rainfall in October 2015 caused the failure of more than 50 dams and some levees, significantly magnifying destruction from the floodwaters (FEMA, 2016). Slow-moving, destructive storms like hurricanes Harvey (2017) and Florence (2018) have caused significant flooding (van Oldenborgh et al., 2017; Paul et al., 2019b). In those cases, urban sprawl may have altered storm dynamics (Zhang et al., 2018b), while increased asset exposure to the flood hazard amplified the multi-billion-dollar losses (Klotzbach et al., 2018; Trenberth et al., 2018). A substantial fraction of the damage from hurricane Harvey’s extreme rainfall has been attributed to anthropogenic climate change (see Box 14.5; Emanuel, 2017; Risser and Wehner, 2017). A near disaster at California’s Oroville dam in 2017 was caused by inadequate infrastructure design and maintenance together with an unusually large number of atmospheric river (AR) storms. The event required emergency reservoir spills while the state was beginning recovery from the extreme 2012–2016 drought (Vano et al., 2019; White et al., 2019).

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Extreme weather events, including hurricanes, droughts and flooding, and wildfires, have been partly attributed to anthropogenic climate change (high confidence) (Table SM 16.21; e.g., Rupp et al., 2015; Emanuel, 2017). Direct, indirect and non-market economic damages from extreme events have increased in some parts of North America (high confidence). The number of extreme events with inflation-adjusted damages totalling more than 1 billion USD has risen in the USA over the past decades (NOAA, 2020; Smith, 2020), and similar increases have been observed in Canada (Boyd and Markandya, 2021). Factors other than climate change, including increases in exposure and the value of the assets at risk, also explain increasing damage amounts (Freeman and Ashley, 2017; Vano et al., 2018). Climate change explains a portion of long-term increases in economic damages of hurricanes (limited evidence, low agreement ). Studies of US hurricanes since 1900 have found increasing economic losses that are consistent with an influence from climate change (Estrada et al., 2015; Grinsted et al., 2019), although another study found no increase (Weinkle et al., 2018).

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Formal attribution of economic damages from individual extreme events to anthropogenic climate change has been limited, but climate change could account for a substantial fraction of the damages (limited evidence, medium agreement ). Two recent studies have shown approaches for how damages may be attributed for individual events in the USA. Assuming a direct proportionality between attributable risk of the event to the attributable economic damages, one study suggested that 30–75% of the direct damages from Hurricane Harvey was caused by climate change, with a best estimate of 67 billion USD out of an estimated 90 billion USD total of attributable damages (Frame et al., 2020). Another study modelled the component of the flooding from Hurricane Sandy due to rising SLR and mapped that to coastal damages. That study estimated that 8.1 billion USD (13% of the total) was attributable to the climate influence on SLR (Strauss et al., 2021).

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Abatzoglou, J.T. and A.P. Williams, 2016: Impact of anthropogenic climate change on wildfire across western US forests. Proc. Natl. Acad. Sci. U. S. A. , 113 (42), 11770–11775, doi:10.1073/pnas.1607171113.

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Abrahms, B., et al., 2019b: Dynamic ensemble models to predict distributions and anthropogenic risk exposure for highly mobile species. Divers. Distrib. , 116, 5582, doi:10.1111/ddi.12940.

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AghaKouchak, A., et al., 2015: Water and climate: recognize anthropogenic drought. Nature, 524 (7566), 409–411, doi:10.1038/524409a.

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Cadieux, P., et al., 2020: Projected effects of climate change on boreal bird community accentuated by anthropogenic disturbances in western boreal forest, Canada. Divers. Distributions, 26 (6), 668–682, doi:10.1111/ddi.13057.

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Carroll, C., S.A. Parks, S.Z. Dobrowski and D.R. Roberts, 2018: Climatic, topographic, and anthropogenic factors determine connectivity between current and future climate analogs in North America. Glob. Chang Biol. , 24 (11), 5318–5331, doi:10.1111/gcb.14373.

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Cook, J., et al., 2013: Quantifying the consensus on anthropogenic global warming in the scientific literature. Environ. Res. Lett. , 8 (2), 24024, doi:10.1088/1748-9326/8/2/024024.

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Doney, S.C., et al., 2007: Impact of anthropogenic atmospheric nitrogen and sulfur deposition on ocean acidification and the inorganic carbon system. Proc. Natl. Acad. Sci. U. S. A. , 104 (37), 14580–14585, doi:10.1073/pnas.0702218104.

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Gattuso, J.P., et al., 2015: OCEANOGRAPHY. Contrasting futures for ocean and society from different anthropogenic CO2 emissions scenarios. Science, 349 (6243), aac4722, doi:10.1126/science.aac4722.

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Liñan-Cabello, M. A., A.L. Quintanilla-Montoya, C. Sepúlveda-Quiroz and O.D. Cervantes-Rosas, 2016: AnthropogenicSusceptibility to environmental variability of the aquaculture sector in the State of Colima, Mexico: case study. Lat. Am. J. Aquat. Res. , 44 (3), 649–656, doi:10.3856/vol44-issue3-fulltext-24.

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Molina-Martínez, A., et al., 2016: Changes in butterfly distributions and species assemblages on a neotropical mountain range in response to global warming and anthropogenic land use. Divers. Distrib. , 22 (11), 1085–1098, doi:10.1111/ddi.12473.

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Ortiz-Bobea, A., et al., 2021: Anthropogenic climate change has slowed global agricultural productivity growth. Nat. Clim. Chang. , 11 (4), 306–312, doi:10.1038/s41558-021-01000-1.

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Partain, Jr., J.L., et al., 2017: An Assessment of the Role of Anthropogenic Climate Change in the Alaska Fire Season of 2015. Bull. Am. Meteorol. Soc. , 97 (12), S14–S18, doi:10.1175/bams-d-16-0149.1.

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Patricola, C.M. and M.F. Wehner, 2018: Anthropogenic influences on major tropical cyclone events. Nature, 563 (7731), 339–346, doi:10.1038/s41586-018-0673-2.

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Rupp, D.E., et al., 2015: Anthropogenic influence on the changing likelihood of an exceptionally warm summer in Texas, 2011. Geophys. Res. Lett. , 42 (7), 2392–2400.

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Strauss, B.H., et al., 2021: Economic damages from Hurricane Sandy attributable to sea level rise caused by anthropogenic climate change. Nat. Commun. , 12 (1), 2720, doi:10.1038/s41467-021-22838-1.

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Tan, X., et al., 2019: Dynamic and thermodynamic changes conducive to the increased occurrence of extreme spring fire weather over western Canada under possible anthropogenic climate change. Agric. For. Meteorol. , 265, 269–279, doi:10.1016/j.agrformet.2018.11.026.

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Williams, A.P., et al., 2019a: Observed impacts of anthropogenic climate change on wildfire in California. Earths Future, doi:10.1029/2019ef001210.

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Williams, A.P., et al., 2020: Large contribution from anthropogenic warming to an emerging North American megadrought. Science, 368 (6488), 314, doi:10.1126/science.aaz9600.

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Despite their vital social and ecological value, substantial declines in seagrass communities have been documented in many small islands (Section 3.4.2.5; Arias-Ortiz et al., 2018; Kendrick et al., 2019; Brodie et al., 2020), including Fiji (Joseph et al., 2019), Reunion Island (Cuvillier et al., 2017), Bermuda, Cayman Islands, US Virgin Islands (Waycott et al., 2009), Kiribati (Brodie et al., 2020), Federated States of Micronesia, and Palau (Short et al., 2016), but attribution of such declines to climatic influences remains weak (low confidence). The impact of climate change on seagrasses goes beyond the loss of seagrass but includes acceleration of seagrass decomposition (Kelaher et al., 2018), palatability (Jimenez-Ramos et al., 2017) and the cumulative effect of warming and eutrophication (Ontoria et al., 2019). Seagrasses face a multitude of threats including physical disturbance and direct damage caused by rapidly growing human populations, declines in water quality, and coastal erosion (Short et al., 2016). Experimental studies have shown increased mortality, leaf necrosis, and respiration when seagrasses are exposed to higher-than-normal temperatures (Hernan et al., 2017). As such, seagrass meadows growing near the edge of their thermal tolerance are at risk from rising temperatures (Pedersen et al., 2016). In the Mediterranean, seagrass meadows are already showing signs of regression, which may have been aggravated by climate change (high confidence). Some studies suggest seagrasses have potential for acclimation and adaptation (Duarte et al., 2018; Ruiz et al., 2018; Beca-Carretero et al., 2020). Chefaoui et al. (2018) attempted to forecast the distribution of two seagrasses in the future, including around the islands of Cyprus, Malta, Sicily and the Balearic Islands. Under the worst-case scenario, Posidonia oceanica was projected to lose 75% of suitable habitat by 2050. Conversely, it has been suggested that seagrasses could actually benefit from an increase in anthropogenic CO2 because of increased growth and photosynthesis (Hopley et al., 2007; Waycott et al., 2011; Sunday et al., 2016; Repolho et al., 2017). However, Collier et al. (2017) argued that when faced with increased heat waves, thermal stress will rarely be offset by the benefit of elevated CO2 and therefore that the widespread belief that seagrasses will be a ‘winner’ under future climate change conditions seems unlikely (low confidence).

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Since 2011, the Caribbean region has been experiencing unprecedented influxes of the pelagic seaweed Sargassum. These extraordinary sargassum ‘blooms’ have resulted in mass strandings of sargassum throughout the Lesser Antilles, with significant damage to coastal habitats, mortality of seagrass beds and associated corals (van Tussenbroek et al., 2017), as well as consequences for fisheries and tourism. Whether or not such events are related to long-term climate change remains unclear; however, it has been suggested that the influx may be related to strong Amazon discharge, enhanced West African upwelling, together with rising seawater temperatures in the Atlantic (low confidence) (Oviatt et al., 2019; Wang et al., 2019). Since 2011, the Pacific atoll nation of Tuvalu has also been affected by algal blooms, the most recent being a large growth of Sargassum on the main atoll of Funafuti, and this phenomenon has been related to anthropogenic eutrophication and high seawater temperatures (De Ramon N’Yeurt and Iese, 2014).

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Despite encompassing approximately 2% of the Earth’s terrestrial surface, oceanic and other high-endemicity islands are estimated to harbour substantial proportions of existing species including ~25% extant global flora, ~12% birds and ~10% mammals (Alcover et al., 1998; Wetzel et al., 2013; Kumar and Tehrany, 2017). Islands also have higher densities of critically endangered species, hosting just under half of all species currently considered to be at risk of extinction (Spatz et al., 2017a; 2017b), hence making the loss of terrestrial biodiversity and related ecosystem services a KR (KR3) for small islands (Figure 15.5). Impacts from developing synergies between changing climate, natural and anthropogenic stressors on islands (Cross-Chapter Box DEEP in Chapter 17) could lead to disproportionate changes in global biodiversity. The most prominent drivers include: SLR, increasing intensities of extreme events (human activities—especially continuing/accelerating habitat destruction/degradation) and the introduction of invasive alien species (IAS) (Tershy et al., 2015). When coupled with characteristic small island traits such as spatial and other resource limitations, these synergies play a critical role towards increasing the vulnerability of these insular ecosystems (Box CCP1.1). This is likely to hinder the adaptation response of terrestrial biota–increasing the risk of biodiversity loss and, in turn, impairing the resilience capacity of ecosystem functioning and services (high confidence) (Heller and Zavaleta, 2009; Ferreira et al., 2016; Vogiatzakis et al., 2016).

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Marine flooding is expected to destroy habitats of coastal species, particularly range-restricted coastal and/or single-island endemics (many already listed as at least ‘threatened’ by the International Union for Conservation of Nature) within the limited terrain on atoll islands. These species have limited opportunities to accommodate such direct impacts of climate change apart from shifting further inland or to other neighbouring atolls which might have favourable habitat. However, fragmentation of habitat due to anthropogenic activity may hinder migration further inland, while shifting to neighbouring islands is not viable due to the water barrier between islands (high confidence) (Bellard et al., 2013b; Wetzel et al., 2013; Kumar and Tehrany, 2017). Additionally, migratory birds, which use small islands (e.g., atolls) for stopovers or breeding/nesting sites, are projected to become impacted. Within the Mediterranean and Caribbean, significant losses to coastal wetlands—critical habitat for migratory birds—has already been observed, with further significant habitat losses, redistribution and changes in quality being projected across island systems such as the Bahamas (Caribbean) and Sardinia (Mediterranean) (Vogiatzakis et al., 2016; Wolcott et al., 2018).

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Studies do not provide sufficiently robust evidence to attribute the various forms of migration to anthropogenic climate change directly on small islands or to accurately estimate the current number of climate-related migrants (see Chapter 7). Climate events and conditions strongly interact with other environmental stressors and economic, social, political and cultural reasons for migrating (robust evidence, high agreement ) (Birk and Rasmussen, 2014; Campbell and Warrick, 2014; Laczko and Piguet, 2014; Marino and Lazrus, 2015; Connell, 2016; Weber, 2016b; Stojanov et al., 2017; Cashman and Yawson, 2019).

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The 1997–1998 ENSO event was severe in the Maldives and as a result the living coral cover dropped to <10% (Bianchi et al., 2003). Recovery was still in progress in 2004 when the tsunami caused further (although not quantitatively assessed (Gischler and Kikinger, 2006)) damage to the reef ecosystem. Post-1998 recovery ultimately took 15 years, (i.e., longer than following the 1987 ENSO event, after which recovery had only taken a few years) and also longer than in the neighbouring undisturbed Chagos atolls, thereby suggesting the alteration of the recovery capacity of the reef ecosystem by human-induced reef degradation and climate change (Morri et al., 2015; Pisapia et al., 2017). Mid-2016, a new ENSO event occurred, which reduced living coral cover by 75% (Perry and Morgan, 2017). Future recovery of the reef ecosystem, which is critical to both current livelihoods and economic activities (especially diving-oriented tourism and fishing) and to long-term island persistence, will mainly depend first on the frequency and magnitude of future bleaching events, which are expected to increase due to ocean warming, and second on the highly variable effects of anthropogenic disturbances locally (Perry and Morgan, 2017; Pisapia et al., 2017; Duvat and Magnan, 2019b).

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Gattuso, J., et al., 2015: Contrasting futures for ocean and society from different anthropogenic CO2 emissions scenarios. Science, 349 (6243), aac4722, doi:10.1126/science.aac4722.

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Kane, H.H. and C.H. Fletcher, 2020: Rethinking Reef Island Stability in Relation to Anthropogenic Sea Level Rise. Earths Future, 8 (10), doi:10.1029/2020ef001525.

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Saha, C., 2017: Dynamics of climatic and anthropogenic stressors in risking island-char livelihoods: a case of northwestern Bangladesh. Asian. Geogr. , 34 (2), 107–129, doi:10.1080/10225706.2017.1354770.

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The combination of continued anthropogenic disturbance, particularly deforestation, with global warming may result in dieback of forest in the region (medium confidence) (SR15 Chapter 3, Hoegh-Guldberg et al., 2018). Losses of biomass as high as 40% are projected in CA with a warming of 3°C–4°C, and the Amazon may experience a significant dieback at similar warming levels (SR15 Chapter 3, Hoegh-Guldberg et al., 2018). Advances in second-generation bioethanol from sugarcane and other feedstock will be important for mitigation. However, agricultural expansion results in large conversions in tropical dry woodlands and savannahs in SA (Brazilian Cerrado, Caatinga and Chaco) (high confidence) (SRCCL Chapter 1, Arneth et al., 2019). The expansion of soybean plantations in the Amazonian state of Mato Grosso in Brazil reached 16.8% yr −1 from 2000 to 2005; and oil palm, a significant biofuel crop, is also linked to recent deforestation in tropical CA (Costa Rica and Honduras) and SA (Colombia and Ecuador), although lower in magnitude compared to deforestation from soybean and cattle ranching (WGII AR5 Chapter 27, Magrin et al., 2014).

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Fire has been found to promote or halt biological invasions (medium confidence: medium evidence, high agreement ). For example, an analysis of Pinus spread following wildfires in Patagonia revealed a high risk that pines will become invasive if ignition frequency increases as a result of climate change (Raffaele et al., 2016). According to Inostroza et al. (2016), the Magellan Region is one of the most fragile regions in Patagonia, and despite its low population densities, it is undergoing a silent process of anthropogenic alteration where between 53.1% and 68.1% of the area needs to be considered to be influenced by humans who are occupying pristine ecosystems, even some with extensive conservation designations (Inostroza et al., 2016). Fire exposure can result in several health problems for human populations; Table 12.5 shows that SSA is the region with the highest exposure to wildfire danger.

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Amazonian dark earths (ADEs), also known as Terras Pretas de Índio, are anthropogenic soils derived from the activities associated with the settlements and agricultural practices of pre-Hispanic societies in the Amazon (Woods and McCann, 1999; Lehmann et al., 2003; Sombroek et al., 2003). Most of the ADEs identified so far are 500 to 2500 years old (de Souza et al., 2019). According to Maezumi et al. (2018a), polyculture agroforestry allowed for the development of complex societies in the eastern Amazon around 4500 years ago. Agroforestry was combined with the cultivation of multiple crops and the active and progressive increase in the proportion of edible plant species in the forest, along with hunting and fishing. The formation of ADEs as a result of these activities served as the basis for a food production system that supported a growing human population in the area (Maezumi et al., 2018a).

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There is a robust and growing body of research from various disciplines that assigns a high relevance to ADEs in the region. It has been shown through archaeological and palaeoclimatic data that Amazonian societies that based their agricultural management on Terras Pretas de Índio were more resilient to the changing climate due to increased soil fertility and water retention capacity (de Souza et al., 2019). Additionally, low organic carbon degradability over long time periods, associated with high contents of charcoal or pyrogenic carbon, makes these soils an important C sink (medium confidence: robust evidence, medium agreement ) (Lehmann et al., 2003; Guo, 2016; Trujillo et al., 2020), which is particularly relevant in an area like the Amazon, that could change from a net carbon sink to a net carbon source as a consequence of anthropogenic climate change (Maezumi et al., 2018b).

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At present, ADEs are estimated to cover up to 3.2% of the Amazon basin and are highly valued for their persistent fertility, and they have become a key resource for sustainable agriculture for Amazon communities in a climate-change context (Altieri and Nicholls, 2013; Maezumi et al., 2018a; de Souza et al., 2019). Based on the lessons learned from the Terras Pretas de Índio, some researchers have proposed the development of technologies to promote a new generation of anthropogenic soils (e.g., Kern et al. 2009; Lehmann 2009; Schmidt et al. 2014; Bezerra et al. 2016; Kern et al. 2019). Among the technologies based on ADE findings, biochar, obtained by the slow pyrolysis of agricultural residues, is the most explored application found in the literature (Mohan et al., 2018; Matoso et al., 2019; Amoah-Antwi et al., 2020). The dual purpose of increased soil fertility and carbon sequestration is considered an important goal in connection with developing sustainable agriculture in a climate-change context (Kern et al., 2019).

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Aguirre, C., et al., 2018: Insight into anthropogenic forcing on coastal upwelling off south-central Chile. Elem. Sci. Anthropocene, 6 (1), 59–72, doi:10.1525/elementa.314.

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Arnan, X., et al., 2018: Increased anthropogenic disturbance and aridity reduce phylogenetic and functional diversity of ant communities in Caatinga dry forest. Sci. Total Environ. , 631-632, 429–438, doi:10.1016/j.scitotenv.2018.03.037.

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Boisier, J.P., et al., 2018: Anthropogenic drying in central-southern Chile evidenced by long-term observations and climate model simulations. Elem. Sci. Anthropocene, 6 (1), 74, doi:10.1525/elementa.328.

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Boisier, J.P., R. Rondanelli, R.D. Garreaud and F. Muñoz, 2016: Anthropogenic and natural contributions to the Southeast Pacific precipitation decline and recent megadrought in central Chile. Geophys. Res. Lett. , 43 (1), 413–421, doi:10.1002/2015GL067265.

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Herrera, D.A., et al., 2018a: Exacerbation of the 2013–2016 pan-Caribbean drought by anthropogenic warming. Geophys. Res. Lett. , 45 (19), 10,619–610,626, doi:10.1029/2018GL079408.

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Huneeus, N., et al., 2020: Evaluation of anthropogenic air pollutant emission inventories for South America at national and city scale. Atmos. Environ. , 235, 117606, doi:10.1016/j.atmosenv.2020.117606.

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Klein Goldewijk, K., A. Beusen, J. Doelman and E. Stehfest, 2017: Anthropogenic land use estimates for the Holocene – HYDE 3.2. Earth Syst. Sci. Data, 9 (2), 927–953, doi:10.5194/essd-9-927-2017.

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Scordo, F., et al., 2017: Evolution of water resources in the “Bajo de Sarmiento” (Extraandean Patagonia): natural and anthropogenic impacts. Anu. Inst. Geoci. UFRJ, 40 (2), 106–117, doi:10.11137/2017_2_106_117.

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Vera, C.S. and L. Díaz, 2015: Anthropogenic influence on summer precipitation trends over South America in CMIP5 models. Int. J. Climatol. , 35 (10), 3172–3177, doi:10.1002/joc.4153.

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Woods, W. and J. McCann (eds.), 1999: The anthropogenic origin and persistence of Amazonian dark earths. In: Conference of Latin Americanist Geographers[Woods, W.I. and J.M. McCann(eds.)]. University of Texas Press, Austin, Texas, USA. pp. 7–14.

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12.6.2 Anthropogenic Soils, an Option for Mitigation and Adaptation to Climate Change in Central and South America. Learning from the “Terras Pretas de Índio” in the Amazon 1757

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Opportunities for avoiding waste associated with the provision of services, or avoiding overprovision of or excess demand for services, exist across multiple service categories. ‘Avoid’ options are relevant in all end-use sectors, namely, teleworking and avoiding long-haul flights, adjusting dwelling size to household size, and avoiding short-lifespan products and food waste. Cities and built environments can play an additional role. For example, more compact designs and higher accessibility reduce travel demand and translate into lower average floor space and corresponding heating/cooling and lighting demand, and thus reductions of between 5% to 20% of GHG emissions of end-use sectors (Creutzig et al. 2021b). Avoidance of food loss and wastage – which equalled 8–10% of total anthropogenic GHG emissions from 2010–2016 (Mbow et al. 2019), while millions suffer from hunger and malnutrition – is a prime example (Chapter 12). A key challenge in meeting global nutrition services is therefore to avoid food loss and waste while simultaneously raising nutrition levels to equitable standards globally. Literature results indicate that in developed economies, consumers are the largest source of food waste, and that behavioural changes such as meal planning, use of leftovers, and avoidance of over-preparation can be important service-oriented solutions (Gunders et al. 2017; Schanes et al. 2018), while improvements to expiration labels by regulators would reduce unnecessary disposal of unexpired items (Wilson et al. 2017) and improved preservation in supply chains would reduce spoilage (Duncan and Gulbahar 2019). Around 931 million tonnes of food waste was generated in 2019 globally, 61% of which came from households, 26% from food service and 13% from retail.

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Reduced food waste and dietary shifts have highly relevant repercussions in the land-use sector that underpin the high GHG emission reduction potential. Demand-side measures lead to changes in consumption of land-based resources and can save GHG emissions by reducing or improving management of residues or making land areas available for other uses such as afforestation or bioenergy production (Smith et al. 2013; Hoegh-Guldberg et al. 2019). Deforestation is the second-largest source of anthropogenic greenhouse gas emissions, caused mainly by expanding forestry and agriculture, and in many cases this agricultural expansion is driven by trade demand for food. For example, across the tropics, cattle and oilseed products account for half the deforestation carbon emissions, embodied in international trade to China and Europe (Creutzig et al. 2019a; Pendrill et al. 2019). Benefits from shifts in diets and resulting lowered land pressure are also reflected in reductions of land degradation and emissions.

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Jylhä, K.M., C. Cantal, N. Akrami, and T.L. Milfont, 2016: Denial of anthropogenic climate change: Social dominance orientation helps explain the conservative male effect in Brazil and Sweden. Pers. Individ. Dif. , 98, 184–187, doi:10.1016/j.paid.2016.04.020.

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Global net anthropogenic greenhouse gas (GHG) emissions during the last decade (2010–2019) were higher than at any previous time in human history (high confidence). Since 2010, GHG emissions have continued to grow, reaching 59 ± 6.6 GtCO2-eq in 2019, 1 but the average annual growth in the last decade (1.3%, 2010–2019) was lower than in the previous decade (2.1%, 2000–2009) ( high confidence). Average annual GHG emissions were 56 ± 6.0 GtCO2-eq yr –1 for the decade 2010–2019 growing by about 9.1 GtCO2-eq yr –1 from the previous decade (2000–2009) – the highest decadal average on record ( high confidence). {2.2.2, Table 2.1, Figure 2.2, Figure 2.5}

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As demonstrated by the contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (AR6 WGI) (IPCC 2021a), greenhouse gas 4 (GHG) concentrations in the atmosphere and annual anthropogenic GHG emissions continue to grow and have reached a historic high, driven mainly by continued fossil fuels use (Jackson et al. 2019; Friedlingstein et al. 2020; Peters et al. 2020). Unsurprisingly, a large volume of new literature has emerged since AR5 on the trends and underlying drivers of anthropogenic GHG emissions. This chapter provides a structured assessment of this new literature and establishes the most important thematic links to other chapters in this report.

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Total anthropogenic greenhouse gas (GHG) emissions as discussed in this chapter comprise CO2 emissions from fossil fuel combustion and industrial (FFI) processes, 5 net CO2 emissions from land use, land-use change, and forestry (CO2-LULUCF) (often named FOLU – forestry and other land-use – in previous IPCC reports), methane (CH4), nitrous oxide (N2O) and fluorinated gases (F-gases) comprising hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), sulphur hexafluoride (SF6) as well as nitrogen trifluoride (NF3). There are other major sources of F-gas emissions that are regulated under the Montreal Protocol such as chlorofluorocarbons (CFCs) and hydrochlorofluorocarbons (HCFCs) that also have considerable warming impacts (Figure 2.4), however they are not considered here. Other substances, including ozone and aerosols, that further contribute climate forcing are only treated very briefly, but a full chapter is devoted to this subject in the Working Group I contribution to AR6 (Szopa et al. 2021a; 2021b).

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Across this report, version 6 of EDGAR (Crippa et al. 2021) provided by the Joint Research Centre of the European Commission, is used for a consistent assessment of GHG emissions trends and drivers. It covers anthropogenic releases of CO2-FFI, CH4, N2O, and F-gas (HFCs, PFCs, SF6, NF3) emissions by 228 countries and territories and across five sectors and 27 subsectors. EDGAR is chosen because it provides the most comprehensive global dataset in its coverage of sources, sectors and gases. For transparency, and as part of the uncertainty assessment, EDGAR is compared to other global datasets in Section 2.2.1 as well as in the Chapter 2 Supplementary Material (2.SM.1). For individual country estimates of GHG emissions, it may be more appropriate to use inventory data submitted to the United Nations Framework Convention on Climate Change (UNFCCC) under the common reporting format (CRF) (UNFCCC 2021). However, these inventories are only up to date for Annex I countries and cannot be used to estimate global or regional totals. As part of the regional analysis, a comparison of EDGAR and CRF estimates at the country-level is provided, where the latter is available (Figure 2.9).

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Net CO2-LULUCF estimates are added to the dataset as the average of estimates from three bookkeeping models of land-use emissions (Hansis et al. 2015; Houghton and Nassikas 2017; Gasser et al. 2020) following the Global Carbon Project (Friedlingstein et al. 2020). This is different to AR5, where land-based CO2 emissions from forest fires, peat fires, and peat decay, were used as an approximation of the net-flux of CO2-LULUCF (Blanco et al. 2014). Note that the definition of CO2-LULUCF emissions by global carbon cycle models, as used here, differs from IPCC definitions (IPCC 2006) applied in national greenhouse gas inventories (NGHGI) for reporting under the climate convention (Grassi et al. 2018, 2021) and, similarly, from estimates by the Food and Agriculture Organization of the United Nations (FAO) for carbon fluxes on forest land (Tubiello et al. 2021). The conceptual difference in approaches reflects different scopes. We use the global carbon cycle models’ approach for consistency with Working Group I (Canadell et al. 2021) and to comprehensively distinguish natural from anthropogenic drivers, while NGHGI generally report as anthropogenic all CO2 fluxes from lands considered managed (Section 7.2.2). Finally, note that the CO2-LULUCF estimate from bookkeeping models as provided in this chapter is indistinguishable from the CO2 from agriculture, forestry and other land use (AFOLU) as reported in Chapter 7, because the CO2 emissions component from agriculture is negligible.

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Comprehensive mitigation policy relies on consideration of all anthropogenic forcing agents, which differ widely in their atmospheric lifetimes and impacts on the climate system. GHG emission metrics 6 provide simplified information about the effects that emissions of different GHGs have on global temperature or other aspects of climate, usually expressed relative to the effect of emitting CO2 (see emission metrics in Annex I: Glossary). This information can inform prioritisation and management of trade-offs in mitigation policies and emission targets for non-CO2 gases relative to CO2, as well as for baskets of gases expressed in CO2-eq. This assessment builds on the evaluation of GHG emission metrics from a physical science perspective by WGI (Forster et al. 2021b). For additional details and supporting references, see Chapter 2 Supplementary Material (2.SM.3) and Annex II.8.

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A key insight from WGI is that, for a given emissions scenario, different metric choices can alter the time at which net zero GHG emissions are calculated to be reached, or whether net zero GHG emissions are reached at all (2.SM.3). From a mitigation perspective, this implies that changing GHG emission metrics but retaining the same numerical CO2-equivalent emissions targets would result in different climate outcomes. For example, achieving a balance of global anthropogenic GHG emissions and removals, as stated in Article 4.1 of the Paris Agreement could, depending on the GHG emission metric used, result in different peak temperatures and in either stable, or slowly or rapidly declining temperature after the peak (Allen et al. 2018; Fuglestvedt et al. 2018; Tanaka and O’Neill 2018; Schleussner et al. 2019). A fundamental change in GHG emission metrics used to monitor achievement of existing emission targets could therefore inadvertently change their intended climate outcomes or ambition, unless existing emission targets are re-evaluated at the same time (very high confidence).

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Table 2.1 | Total anthropogenic GHG emissions (GtCO2-eq yr–1) 1990–2019. CO2 from fossil fuel combustion and industrial processes (FFI); CO2 from Land Use, Land Use Change and Forestry (LULUCF); methane (CH4); nitrous oxide (N2O); fluorinated gases (F-gases: HFCs, PFCs, SF6, NF3). Aggregate GHG emissions trends by groups of gases reported in GtCO2- eq converted based on global warming potentials with a 100-year time horizon (GWP100) from the IPCC Sixth Assessment Report (AR6). Uncertainties are reported for a 90% confidence interval. Source: Minx et al. (2021).

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The only exception to these patterns of GHG emissions growth is net anthropogenic CO2-LULUCF emissions, where there is no statistically significant trend due to high uncertainties in estimates (Figures 2.2 and 2.5; Chapter 2 Supplementary Material). While the average estimate from the bookkeeping models report a slightly increasing trend in emissions, NGHGI and FAOSTAT estimates show a slightly decreasing trend, which diverges in recent years (Figure 2.2). Similarly, trends in CO2-LULUCF estimates from individual bookkeeping models differ: while two models (BLUE and OSCAR) show a sustained increase in emissions levels since the mid-1990s, emissions from the third model (Houghton and Nassikas (HN)) declined (Figure 2.2 in this chapter; Friedlingstein et al. 2020). Differences in accounting approaches and their impacts CO2 emissions estimates from land use is covered in Chapter 7 and in the Chapter 2 Supplementary Material (2.SM.2). Note that anthropogenic net emissions from bioenergy are covered by the CO2-LULUCF estimates presented here.

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From 1850 until around 1950, anthropogenic CO2 emissions were mainly (>50%) from land use, land-use change and forestry (Figure 2.7). Over the past half-century CO2 emissions from LULUCF have remained relatively constant around 5.1 ± 3.6 GtCO2 but with a large spread across estimates (Le Quéré et al. 2018 a; Friedlingstein et al. 2019, 2020). By contrast, global annual FFI-CO2 emissions have continuously grown since 1850, and since the 1960s from a decadal average of 11 ± 0.9 GtCO2 to 36 ± 2.9 GtCO2 during 2010–2019 (Table 2.1).

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Even when taking uncertainties into account, historical emissions between 1850 and 2019 constitute a large share of total carbon budgets from 2020 onwards for limiting warming to 1.5°C with a 50% probability as well as for limiting warming to 2°C with a 67% probability. Based on central estimates only, historical cumulative net CO2 emissions between 1850–2019 amount to about four fifths of the total carbon budget for a 50% probability of limiting global warming to 1.5°C (central estimate about 2900 GtCO2), and to about two thirds of the total carbon budget for a 67% probability to limit global warming to 2°C (central estimate about 3550 GtCO2). The carbon budget is the maximum amount of cumulative net global anthropogenic CO2 emissions that would result in limiting global warming to a given level with a given likelihood, taking into account the effect of other anthropogenic climate forcers. This is referred to as the total carbon budget when expressed starting from the pre-industrial period, and as the remaining carbon budget when expressed from a recent specified date. The total carbon budgets reported here are the sum of historical emissions from 1850 to 2019 and the remaining carbon budgets from 2020 onwards, which extend until global net zero CO2 emissions are reached. Uncertainties for total carbon budgets have not been assessed and could affect the specific calculated fractions (IPCC 2021 [Working Group 1 SPM], Canadell et al., 2021 [Working Group 1 Ch5]).

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GHG emissions from agriculture, forestry and other land use (AFOLU) reached 13 GtCO2-eq globally in 2019 (medium confidence) (Figure 2.21). AFOLU trends, particularly those for CO2-LULUCF, are subject to a high degree of uncertainty (Section 2.2.1). Overall, the AFOLU sector accounts for 22% of total global GHG emissions, and in several regions – Africa, Latin America, and South-East Asia – it is the single largest emitting sector, which is also significantly affected itself by climate change (AR6 WGI Chapters 8, 11, and 12; and AR6 WGII Chapter 5). Latin America has the highest absolute and per capita AFOLU GHG emissions of any world region (Figure 2.21). CO2 emissions from land-use change and CH4 emissions from enteric fermentation together account for 74% of sector-wide GHGs. Note that CO2-LULUCF estimates included in this chapter are not necessarily comparable with country GHG inventories, due to different approaches to estimating anthropogenic CO2 sinks (Grassi et al. 2018) (Chapter 7).

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Grassi, G. et al., 2018: Reconciling global-model estimates and country reporting of anthropogenic forest CO2 sinks. Nat. Clim. Change, 8(10) , 914–920, doi:10.1038/s41558-018-0283-x.

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Hoesly, R.M. et al., 2018: Historical (1750–2014) anthropogenic emissions of reactive gases and aerosols from the Community Emissions Data System (CEDS). Geosci. Model Dev. , 11(1) , 369–408, doi:10.5194/gmd-11-369-2018.

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Höglund-Isaksson, L., A. Gómez-Sanabria, Z. Klimont, P. Rafaj, and W. Schöpp, 2020: Technical potentials and costs for reducing global anthropogenic methane emissions in the 2050 timeframe –results from the GAINS model. Environ. Res. Commun. , 2(2) , 025004, doi:10.1088/2515-7620/ab7457.

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Lee, D.S. et al., 2021: The contribution of global aviation to anthropogenic climate forcing for 2000 to 2018. Atmos. Environ. , 244, 117834, doi:10.1016/j.atmosenv.2020.117834.

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Lelieveld, J. et al., 2019: Effects of fossil fuel and total anthropogenic emission removal on public health and climate. Proc. Natl. Acad. Sci. , 116(15) , 7192–7197, doi:10.1073/pnas.1819989116.

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McDuffie, E.E. et al., 2020: A global anthropogenic emission inventory of atmospheric pollutants from sector- and fuel-specific sources (1970–2017) : an application of the Community Emissions Data System (CEDS). Earth Syst. Sci. Data, 12(4) , 3413–3442, doi:10.5194/essd-12-3413-2020.

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Myhre, G., D. Shindell, F.-M. Bréon, W. Collins, J. Fuglestvedt, J. Huang, D. Koch, J.-F. Lamarque, D. Lee, B. Mendoza, T. Nakajima, A. Robock, G. Stephens, T. Takemura and H. Zhang, 2013: Anthropogenic and Natural Radiative Forcing. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change[Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, UK and New York, NY, USA.

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Petrescu, A.M.R. et al., 2020: European anthropogenic AFOLU greenhouse gas emissions: a review and benchmark data. Earth Syst. Sci. Data, 12(2) , 961–1001, doi:10.5194/essd-12-961-2020.

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Winiwarter, W., L. Höglund-Isaksson, Z. Klimont, W. Schöpp, and M. Amann, 2018: Technical opportunities to reduce global anthropogenic emissions of nitrous oxide. Environ. Res. Lett. , 13(1) , 014011, doi:10.1088/1748-9326/aa9ec9.

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Anthropogenic land CO2 emissions and removals in Integrated Assessment Model (IAM) pathways cannot be directly compared with those reported in national GHG inventories (high confidence). Methodologies enabling a more like-for-like comparison between models’ and countries’ approaches would support more accurate assessment of the collective progress achieved under the Paris Agreement. {3.4, 7.2.2.5}

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Stabilising global average-temperature change requires reducing CO2 emissions to net zero. Thus, a central cross-cutting topic within the chapter is the timing of reaching net zero CO2 emissions and how a ‘balance between anthropogenic emissions by sources and removals by sinks’ could be achieved across time and space. This includes particularly the increasing body of literature since the IPCC Special Report on Global Warming of 1.5°C (SR1.5) which focuses on net zero CO2 emissions pathways that avoid temperature overshoot and hence do not rely on net negative CO2 emissions. The chapter conducts a systematic assessment of the associated economic costs as well as the benefits of mitigation for other societal objectives, such as the Sustainable Development Goals (SDGs). In addition, the chapter builds on SR1.5 and introduces a new conceptual framing for the assessment of possible social, economic, technical, political, and geophysical ‘feasibility’ concerns of alternative pathways, including the enabling conditions that would need to fall into place so that stringent climate goals become attainable.

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Scenario and emission pathways are used to explore possible long-term trajectories, the effectiveness of possible mitigation strategies, and to help understand key uncertainties about the future. Ascenario is an integrated description of a possible future of the human–environment system (Clarke et al. 2014), and could be a qualitative narrative, quantitative projection, or both. Scenarios typically capture interactions and processes that change key driving forces such as population, GDP, technology, lifestyles, and policy, and the consequences on energy use, land use, and emissions. Scenarios are not predictions or forecasts. An emission pathway is a modelled trajectory of anthropogenic emissions (Rogelj et al. 2018 a) and, therefore, a part of a scenario.

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In addition to the constraints on change in global mean temperature, the Paris Agreement also calls for reaching a balance of sources and sinks of GHG emissions (Art. 4). Different interpretations of the concept related to balance have been published (Rogelj et al. 2015c; Fuglestvedt et al. 2018). Key concepts include that of net zero CO2 emissions (anthropogenic CO2 sources and sinks equal zero) and net zero greenhouse gas emissions (see Annex I: Glossary, and Box 3.3). The same notion can be used for all GHG emissions, but here ranges also depend on the use of equivalence metrics (Box 2.1). Moreover, it should be noted that while reaching net zero CO2 emissions typically coincides with the peak in temperature increase; net zero GHG emissions (based on GWP-100) imply a decrease in global temperature (Riahi et al. 2021) and net zero GHG emissions typically require negative CO2 emissions to compensate for the remaining emissions from other GHGs. Many countries have started to formulate climate policy in the year that net zero emissions (either CO2 or all greenhouse gases) are reached – although, at the moment, formulations are often still vague (Rogelj et al. 2021). There has been increased attention on the timing of net zero emissions in the scientific literature and ways to achieve it.

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The concept of a finite carbon budget means that the world needs to get to net zero CO2, no matter whether global warming is limited to 1.5°C or well below 2°C (or any other level). Moreover, exceeding the remaining carbon budget will have consequences by overshooting temperature levels. Still, the relationship between the timing of net zero and temperature targets is a flexible one, as discussed further in Cross-Chapter Box 3 in this chapter. It should be noted that the national-level inventory as used by UNFCCC for the land use, land-use change and forestry sector is different from the overall concept of anthropogenic emissions employed by IPCC AR6 WGI. For emissions estimates based on these inventories, the remaining carbon budgets must be correspondingly reduced by approximately 15%, depending on the scenarios (Grassi et al., 2021) (Chapter 7).

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The accounting of anthropogenic carbon dioxide removal on land matters for the evaluation of net zero CO2and net zero GHG strategies. Due to the use of different approaches between national inventories and global models, the current net CO2 emissions are lower by 5.5 GtCO2, and cumulative net CO2 emissions in modelled 1.5°C–2°C pathways would be lower by 104–170 GtCO2, if carbon dioxide removals on land are accounted based on national GHG inventories. National GHG inventories typically consider a much larger area of managed forest than global models, and on this area additionally consider the fluxes due to human-induced global environmental change (indirect effects) to be anthropogenic, while global models consider these fluxes to be natural. Both approaches capture the same land fluxes, only the accounting of anthropogenic vs natural emissions is different. Methods to convert estimates from global models to the accounting scheme of national GHG inventories will improve the use of emission pathways from global models as benchmarks against which collective progress is assessed. (Section 7.2.2.5).

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Net zero CO2and carbon neutrality have different meanings in this assessment, as is the case for net zero GHG and GHG neutrality. They apply to different boundaries in the emissions and removals being considered. Net zero (GHG or CO2) refers to emissions and removals under the direct control or territorial responsibility of the reporting entity. In contrast, (GHG or carbon) neutrality includes anthropogenic emissions and anthropogenic removals within and also those beyond the direct control or territorial responsibility of the reporting entity. At the global scale, net zero CO2 and carbon neutrality are equivalent, as is the case for net zero GHG and GHG neutrality. The term ‘climate neutrality’ is not used in this assessment because the concept of climate neutrality is diffuse, used differently by different communities, and not readily quantified.

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Anthropogenic land CO2 emissions and removals in IAM pathways cannot be directly compared with those reported in national GHG inventories ( high confidence) (Grassi et al. 2018, 2021) (Section 7.2). Due to differences in definitions for the area of managed forests and which emissions and removals are considered anthropogenic, the reported anthropogenic land CO2 emissions and removals differ by about 5.5 GtCO2 yr –1 between IAMs, which rely on bookkeeping approaches (e.g., Houghton and Nassikas 2017), and national GHG inventories (Grassi et al. 2021). Such differences in definitions can alter the reported time at which anthropogenic net zero CO2 emissions are reached for a given emission scenario. Using national inventories would lead to an earlier reported time of net zero (van Soest et al. 2021b) or to lower calculated cumulative emissions until the time of net zero (Grassi et al. 2021) as compared to IAM pathways. The numerical differences are purely due to differences in the conventions applied for reporting the anthropogenic emissions and do not have any implications for the underlying land-use changes or mitigation measures in the pathways. Grassi et al. (Grassi et al. 2021) offer a methodology for adjusting to reconcile these differences and enable a more accurate assessment of the collective progress achieved under the Paris Agreement (Chapter 7 and Cross-Chapter Box 6 in Chapter 7).

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Dottori, F. et al., 2018: Increased human and economic losses from river flooding with anthropogenic warming. Nat. Clim. Change, 8(9) , 781–786, doi:10.1038/s41558-018-0257-z.

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Grassi, G. et al., 2018: Reconciling global-model estimates and country reporting of anthropogenic forest CO2 sinks. Nat. Clim. Change, 8(10) , 914–920, doi:10.1038/s41558-018-0283-x.

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Höglund-Isaksson, L., A. Gómez-Sanabria, Z. Klimont, P. Rafaj, and W. Schöpp, 2020: Technical potentials and costs for reducing global anthropogenic methane emissions in the 2050 timeframe –results from the GAINS model. Environ. Res. Commun. , 2(2) , 25004, doi:10.1088/2515-7620/ab7457.

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Takakura, J. et al., 2019: Dependence of economic impacts of climate change on anthropogenically directed pathways. Nat. Clim. Change, 9(10) , 737–741, doi:10.1038/s41558-019-0578-6.

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Notes: aCountries are abbreviated by their ISO 3166-1 alpha-3 letter codes. EU denotes the European Union. b2018 Share of global Kyoto GHG emissions, excluding FOLU emissions, based on 2019 GHG emissions from Chapter 2 (Minx et al. 2021; Crippa et al. 2021). cType distinguishes between independent globally comprehensive studies (that also provide information at the country/region level), independent national studies and official communications via Biennial Reports, Biennial Update Reports or National Communications. dDifferent estimates from one study (e.g., data from multiple models or minimum and maximum estimates) are counted individually, if available. eGHG emissions expressed in CO2-eq emission using AR6 100-year GWPs (see Section 2.2.2 for a discussion of implications for historical emissions). GHG emissions from scenario data is recalculated from individual emission species using AR6 100-year GWPs. GHG emissions from studies that do provide aggregate GHG emissions using other GWPs are rescaled using 2019 GHG emissions from Chapter 2 (Minx et al. 2021; Crippa et al. 2021). f If more than one value is available, a median is provided and the full range of estimates (in parenthesis). To avoid a bias due to multiple estimates provided by the same model, only one estimate per model, typically the most recent update, is included in the median estimate. In the full range, multiple estimates from the same model might be included, in case these reflect specific sensitivity analyses of the ‘central estimate’ (e.g., Baumstark et al. 2021; Rogelj et al. 2017). gNote that AFOLU emissions from national GHG inventories and global/national land use models are generally different due to different approaches to estimate the anthropogenic CO2 sink (Grassi et al. 2018, 2021) (Section 7.2.3 and Cross-Chapter Box 6 in Chapter 7). hThe estimates for USA are based on the first NDC submitted prior to the withdrawal from the Paris Agreement, but not including the updated NDC submitted following its re-entry. iThe EU estimates are based on the 28 member states up until 31 January 2020, i.e., including UK.

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The aggregation of targets results in large uncertainty (Rogelj et al. 2017; Benveniste et al. 2018). In particular, clarity on the contributions from the land use sector to NDCs is needed ‘to prevent high LULUCF uncertainties from undermining the strength and clarity of mitigation in other sectors’ (Fyson and Jeffery 2019). Methodological differences in the accounting of the LULUCF anthropogenic CO2 sink between scientific studies and national GHG inventories (as submitted to UNFCCC) further complicate the comparison and aggregation of emissions of NDC implementation (Grassi et al. 2018, 2021) (Section 7.2.3 and Cross-Chapter Box 6 in Chapter 7). This uncertainty could be reduced with clearer guidelines for compiling future NDCs, in particular when it comes to mitigation efforts not expressed as absolute economy-wide targets (Doelle 2019), and explicit specification of technical details, including energy accounting methods, harmonised emission inventories (Rogelj et al. 2017) and finally, increased transparency and comparability (Pauw et al. 2018).

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The Paris Agreement (PA) sets a long-term goal of holding the increase of global average temperature to ‘well below 2°C above pre-industrial levels’ and pursuing efforts to limit the temperature increase to 1.5°C above pre-industrial levels. This is underpinned by the ‘aim to reach global peaking of greenhouse gas emissions as soon as possible’ and ‘achieve a balance between anthropogenic emissions by sources and removals by sinks of GHG in the second half of this century’ (UNFCCC 2015a). The PA adopts a bottom-up approach in which countries determine their contribution to reach the PA’s long-term goal. These national targets, plans and measures are called ‘nationally determined contributions’ or NDCs.

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The literature frequently refers to national mitigation pathways up to 2030 or 2050 using long-term temperature limits in the Paris Agreement (i.e., ‘2°C’ or ‘1.5°C scenario’). Without additional information, such denomination is incorrect. Working Group I reaffirmed ‘with high confidence the AR5 finding that there is a near-linear relationship between cumulative anthropogenic CO2 emissions and the global warming they cause’ (WGI SPM AR6). It is not the function of any single country’s mitigation efforts, nor any individual actor’s. Emission pathways of individual countries or sectors in the near to mid-term can only be linked to a long-term temperature with additional assumptions specifying (i) the GHG emissions and removals of other countries up the mid-term; and (ii) the GHG emissions and removals of all countries beyond the near and mid-term. For example, a national mitigation pathway can be labelled ‘2°C compatible’ if it derives from a global mitigation pathway consistent with 2°C via an explicit effort sharing scheme across countries (Sections 4.2.2.6 and 4.5).

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To achieve net zero GHG emissions implies consideration of targets for non-CO2 gases. While methane emissions have grown less rapidly than CO2 and F-gases since 1990 (Chapter 2), the literature urges action to bring methane back to a pathway more in line with the Paris goals (Nisbet et al. 2020). Measures to reduce methane emissions from anthropogenic sources are considered intractable – where they sustain livelihoods – but also becoming more feasible, as studies report the options for mitigation in agriculture without undermining food security (Wollenberg et al. 2016; Frank et al. 2017; Nisbet et al. 2020). The choice of emission metrics has implications for SLCF (Cain et al. 2019) (Cross-Chapter Box 2 in Chapter 2). Ambitious reductions of methane are complementary to, rather than substitutes for, reductions in CO2 (Nisbet et al. 2020).

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Bustamante, M.M.C. et al., 2018: Engagement of scientific community and transparency in C accounting: The Brazilian case for anthropogenic greenhouse gas emissions from land use, land-use change and forestry. Environ. Res. Lett. , 13(5) , 055005, doi:10.1088/1748-9326/aabb37.

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Grassi, G. et al., 2018: Reconciling global-model estimates and country reporting of anthropogenic forest CO2 sinks. Nat. Clim. Change, 8(10) , 914–920, doi:10.1038/s41558-018-0283-x.

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The extraction, distribution and wastewater processes of anthropogenic water-management systems similarly use vast amounts of energy, making the proper management of water essential to reduce energy usage and GHG emissions (Nair et al. 2014)Chapter 11). One study reports that the water sector accounts for 5% of total US GHG emissions (Rothausen and Conway 2011). Within the WEFN, there is an obvious trade-off between water availability and food production, competing demands that pose a risk to the supply of the basic commodities of food, energy and water in line with the SDGs (Bleischwitz et al. 2018; Gao et al. 2019), all of which have the potential for inter-sectorial or inter-regional conflicts (Froese and Schilling 2019). Currently, 24% of the global population live in regions with constant water-scarce food production, and 19% experience occasional water scarcities (Kummu et al. 2014). To counterbalance the demand for food and comestibles in regions that experience constant or intermittent supplies, transportation is needed, which in itself requires suitable infrastructure, energy supplies, a well-functioning trading environment and support policies. Of the 2.6 billion people who experience constant or occasional water scarcities in food production, 55% rely on international trade, 21% on domestic trade and the remainder on water stocks (Kummu et al. 2014).

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Aviation is widely recognised as a ‘hard-to-decarbonise’ sector (Gota et al. 2019) having a strong dependency on liquid fossil fuels and an infrastructure that has long ‘lock-in’ timescales, resulting in slow fleet turnover times. The principal GHG emitted is CO2 from the combustion of fossil fuel aviation kerosene (‘Jet-A’), although its non-CO­2 emissions can also affect climate (Section 10.5.2). International emissions of CO2 are about 65% of the total emissions from aviation (Fleming and de Lépinay 2019), which totalled approximately 1 Gt of CO2 in 2018. Emissions from this segment of the transport sector have been steadily increasing at rates of around 2.5% per year over the last two decades (Figure 10.10), although for the period 2010 to 2018 the rate increased to roughly 4% per year. The latest available data (2018) indicate that aviation is responsible for approximately 2.4% of total anthropogenic emissions of CO2 (including land-use change) on an annual basis (using IEA data, IATA data and global emissions data of Le Quéré et al. (2018b)).

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Maritime transport volume has increased by 250% over the past 40 years, reaching an all-time high of 11 billion tonnes of transported goods in 2018 (UNCTAD 2019). This growth in transport volumes has resulted in continued growth in GHG emissions from the shipping sector, despite an improvement in the carbon intensity of ship operations, especially since 2014. The estimated total emissions from maritime transport can vary depending on data set and calculation method, but range over 600–1100 MtCO2 yr –1 over the past decade (Figure 10.14), corresponding to 2–3% of total anthropogenic emissions. The legend in Figure 10.14 refers to the following data sources: Endresen et al. (2003), Eyring et al. (2005), Dalsøren et al. (2009), DNV GL (DNV GL 2019), CAMS-GLOB-SHIP (Jalkanen et al. 2014; Granier et al. 2019), EDGAR (Crippa et al. 2019), Hoesly et al. (2018), Johansson et al. (2017), ICCT (Olmer et al. 2017), the IMO GHG Studies; IMO 2nd (Buhaug et al. 2009), IMO 3rd (Smith et al. 2014), IMO 4th-vessel and IMO 4th-voyage (Faber et al. 2020), and Kramel et al. (2021).

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Crippa, M. et al., 2021: Global anthropogenic emissions in urban areas: patterns, trends, and challenges. Environ. Res. Lett. , 16(7) , 074033, doi:10.1088/1748-9326/ac00e2.

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Dubov, G.M., D.S. Trukhmanov, and S.A. Nokhrin, 2020: The Use of Alternative Fuel for Heavy-Duty Dump Trucks as a Way to Reduce the Anthropogenic Impact on the Environment. IOP Conf. Ser. Earth Environ. Sci. , 459, 42059, doi:10.1088/1755-1315/459/4/042059.

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Hoesly, R.M. et al., 2018: Historical (1750–2014) anthropogenic emissions of reactive gases and aerosols from the Community Emissions Data System (CEDS). Geosci. Model Dev. , 11(1) , 369–408, doi:10.5194/gmd-11-369-2018.

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Lee, D.S. et al., 2021: The contribution of global aviation to anthropogenic climate forcing for 2000 to 2018. Atmos. Environ. , 244, doi:10.1016/j.atmosenv.2020.117834.

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Penner, J.E., C. Zhou, A. Garnier, and D.L. Mitchell, 2018: Anthropogenic aerosol indirect effects in cirrus clouds. J. Geophys. Res. Atmos. , 123(20) , 11–652, doi:10.1029/2018JD029204.

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Roiger, A. et al., 2014: Quantifying Emerging Local Anthropogenic Emissions in the Arctic Region: The ACCESS Aircraft Campaign Experiment. Bull. Am. Meteorol. Soc. , 96(3) , 441–460, doi:10.1175/BAMS-D-13-00169.1.

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Whatever metric is used, industrial emissions have been growing faster since 2000 than emissions in any other sector, driven by increased basic materials extraction and production (high confidence). GHG emissions attributed to the industrial sector originate from fuel combustion, process emissions, product use and waste, which jointly accounted for 14.1 GtCO2-eq or 24% of all direct anthropogenic emissions in 2019, second behind the energy transformation sector. Industry is a leading GHG emitter – 20 GtCO2-eq or 34% of global emissions in 2019 – if indirect emissions from power and heat generation are included. The share of emissions originating from direct fuel combustion is decreasing and was 7 GtCO2-eq, 50% of direct industrial emissions in 2019. {11.2.2}

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USEPA, and ICF, 2012: Global Anthropogenic Non-CO2Greenhouse Gas Emissions: 1990–2030. Washington D.C., USA, 176 pp. https://www.epa.gov/sites/production/files/2016-08/documents/epa_global_nonco2_projections_dec2012.pdf . (Accessed December 15, 2019).

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In cities, carbon cycles through natural (e.g., vegetation and soils) and managed (e.g., reservoirs and anthropogenic – buildings, transportation) pools. The accumulation of carbon in urban pools, such as buildings or landfills, results from the local or global transfer of carbon-containing energy and raw materials used in the city (Churkina 2008; Pichler et al. 2017; Chen et al. 2020b). Quantitative understanding of these transfers and the resulting emissions and uptake within an urban area is essential for accurate urban carbon accounting (USGCRP 2018). Currently, urban areas are a net source of carbon because they emit more carbon than they uptake. Thus, urban mitigation strategies require a twofold strategy: reducing urban emissions of carbon into the atmosphere, and enhancing uptake of carbon in urban pools (Churkina 2012) (for a broader definition of ‘carbon cycle’ and related terms such as ‘carbon sink,’ ‘carbon stock,’ ‘carbon neutrality,’ ‘GHG neutrality,’ and others, see Glossary).

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Severe weather events, exacerbated by anthropogenic emissions, are already having devastating impacts on people who live in urban areas, on the infrastructure that supports these communities, as well as people living in distant places ( high confidence) (Cai et al. 2019; Folke et al. 2021). Between 2000 and 2015, the global population in locations that were affected by floods grew by 58–86 million (Tellman et al. 2021). The direct economic costs of all extreme events reached USD210–268 billion in 2020 (Aon 2021; Munich RE 2021; WMO 2021) or about USD0.7 billion per day; this figure does not include knock-on costs in supply chains (Kii 2020) or lost days of work, implying that the actual economic costs could be far higher. Depending on RCP, between half (RCP2.6) and three-quarters (RCP8.5) of the global population could be exposed to periods of life-threatening climatic conditions arising from coupled impacts of extreme heat and humidity by 2100 (Mora et al. 2017; Huang et al. 2019) (see WGII Section 6.2.2.1, WGII Figure 6.3, and WGIII Sections 8.2 and 8.3.4).

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Though preliminary, some studies suggest that urban areas saw larger overall declines in emissions because of lower commuter activity and associated emissions. For example, researchers have explored the COVID-19 impact in the cities of Los Angeles, Baltimore, Washington, DC, and San Francisco Bay Area in the United States. In the San Francisco region, a decline of 30% in anthropogenic CO2 was observed, which was primarily due to changes in on-road traffic (Turner et al. 2020). Declines in the Washington, DC/Baltimore region and in the Los Angeles urban area were 33% and 34%, respectively, in the month of April 2020 compared to previous years (Yadav et al. 2021).

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Quesada, B., A. Arneth, E. Robertson, and N. De Noblet-Ducoudré, 2018: Potential strong contribution of future anthropogenic land-use and land-cover change to the terrestrial carbon cycle. Environ. Res. Lett. , 13 (6) , 064023, doi:10.1088/1748-9326/aac4c3.

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Sargent, M. et al., 2018: Anthropogenic and biogenic CO2 fluxes in the Boston urban region. Proc. Natl. Acad. Sci. U.S.A. , 115(29) , 7491–7496, doi:10.1073/pnas.1803715115.

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Accelerating mitigation to prevent dangerous anthropogenic interference within the climate system will require the integration of broadened assessment frameworks and tools that combine multiple perspectives, applied in a context of multi-level governance (robust evidence, medium agreement). Analysing a challenge on the scale of fully decarbonising our economies entails integration of multiple analytic frameworks. Approaches to risk assessment and resilience, established across IPCC Working Groups, are complemented by frameworks for probing the challenges in implementing mitigation. Aggregate frameworks include cost-effectiveness analysis towards given objectives, and cost-benefit analysis, both of which have been developing to take fuller account of advances in understanding risks and innovation, the dynamics of emitting systems and of climate impacts, and welfare economic theory including growing consensus on long-term discounting. Ethical frameworks consider the fairness of processes and outcomes which can help ameliorate distributional impacts across income groups, countries and generations. Transition and transformation frameworks explain and evaluate the dynamics of transitions to low-carbon systems arising from interactions amongst levels, with inevitable resistance from established socio-technical structures. Psychological, behavioural and political frameworks outline the constraints (and opportunities) arising from human psychology and the power of incumbent interests. A comprehensive understanding of climate mitigation must combine these multiple frameworks. Together with established risk frameworks, collectively these help to explain potential synergies and trade-offs in mitigation, imply a need for a wide portfolio of policies attuned to different actors and levels of decision-making, and underpin Just Transition strategies in diverse contexts. {1.2.2, 1.7, 1.8}

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A key message from recent Special Reports is the urgency to mitigate GHG emissions in order to avoid rapid and potentially irreversible changes in natural and human systems (IPCC 2018b, 2019b, 2019c). Successive IPCC reports have drawn upon increasing sophistication of modelling tools to project emissions in the absence of ambitious decarbonisation action, as well as the emission pathways that meet long-term temperature targets. The SR1.5 examined pathways limiting warming to 1.5°C, compared to the historical baseline of 1850–1900, finding that ‘in pathways with no or limited overshoot of 1.5°C, global net anthropogenic CO2 emissions decline by about 45% from 2010 levels by 2030, reaching net zero around 2050’ (2045–2055 interquartile range); with ‘overshoot’ referring to higher temperatures, then brought down by 2100 through ‘net negative’ emissions. It found this would require rapid and far-reaching transitions in energy, land, urban and infrastructure (including transport and buildings), and industrial systems ( high confidence) (IPCC 2018b).

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In defining the objective of international climate negotiations as being to ‘prevent dangerous anthropogenic interference’ (UNFCCC 1992, Art. 2), the UNFCCC underlines the centrality of risk framing in considering the threats of climate change and potential response measures. Against the background of ‘unequivocal’ (AR4) evidence of human-induced climate change, and the growing experience of direct impacts, the IPCC has sought to systematise a robust approach to risk and risk management.

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In order to achieve the its long term temperature goal, the Paris Agreement aims ‘to achieve a balance between anthropogenic emissions by sources and removals by sinks of greenhouse gases in the second half of this century’ (PA Art. 4 para. 1). The PA provides for five-yearly stocktakes in which Parties have to take collective stock on progress towards achieving its purposes and its long-term goal in the light of equity and available best science (PA Art. 14). The first global stocktake is scheduled for 2023 (PA Art. 14, para. 3).

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The land-use component of CO2 emissions has different drivers and particularly large uncertainties (Figures 2.2 and 2.5), hence is shown separately. Also, compared to AR5, new evidence showed that the AFOLU CO2 estimates by the global models assessed in this report are not necessarily comparable with national GHG inventories, due to different approaches to estimate the ‘anthropogenic’ CO2 sink. Possible ways to reconcile these discrepancies are discussed in Chapter 7.

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Historically, energy-related GHG emissions were considered a by-product of the increasing scale of human activity, driven by population size, economic activity and technology. That simple notion has evolved greatly over time to become much more complex and diverse, with increasing focus on the provision of energy services (Cullen and Allwood 2010; Bardi et al. 2019; Brockway et al. 2019; Garrett et al. 2020). The demand for agricultural products has historically driven conversion of natural lands (land-use change). AFOLU along with food processing accounts for 21–37% of total net anthropogenic GHG emissions (SRCCL SPM A3). 5

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Tackling climate change is often mentioned as an important reason for strong international cooperation in the 21st century (Falkner 2016; Keohane and Victor 2016; Bodansky et al. 2017; Cramton et al. 2017b). Mitigation costs are borne by countries taking action, while the benefits of reduced climate change are not limited to them, being in economic terms ‘global and non-excludable’. Hence anthropogenic climate change is typically seen as a global commons problem (Falkner 2016; Wapner and Elver 2017). Moreover, the belief that mitigation will raise energy costs and may adversely affect competitiveness creates incentives for free riding, where states avoid taking their fair share of action (Barrett 2005; Keohane and Victor 2016). International cooperation has the potential to address these challenges through collective action (Tulkens 2019) and international institutions offer the opportunity for actors to engage in meaningful communication and exchange of ideas about potential solutions (Cole 2015). International cooperation is also vital for the creation and diffusion of norms and the framework for stabilising expectations among actors (Pettenger 2016).

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The ultimate goal of mitigation is to preserve a biosphere which can sustain human civilisation and the complex of ecosystem services which surround and support it. This means reducing anthropogenic GHG emissions towards net zero to limit the warming, with global goals agreed in the Paris Agreement. Effective mitigation strategies require an understanding of mechanisms that underpin release of emissions, and the technical, policy and societal options for influencing these.

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Anthropogenic GHGs such as carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), and fluorinated gases (e.g., hydrofluorocarbons, perfluorocarbons, sulphur hexafluoride) are released from various sources. CO2 makes the largest contribution to global GHG emissions, but some have extremely long atmospheric lifetimes extending to tens of thousands of years, such as F-gases (Chapter 2).

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Current energy sector emissions trends, if continued, will not limit global temperature change to well below 2°C ( high confidence). Global energy system fossil fuel CO2 emissions grew by 4.6% between 2015 and 2019 (1.1% yr –1), reaching 38 GtCO2 yr –1 and accounting for approximately two-thirds of annual global anthropogenic GHG emissions. In 2020, with the worldwide COVID-19 pandemic, energy sector CO2 emissions dropped by roughly 2 GtCO2 yr –1 (Figure 6.2). However global energy-related CO2 emissions are projected to rebound by nearly 5% in 2021, approaching the 2018–19 peak (IEA 2021d).

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This section synthesises current understanding of net-zero energy systems. Discussions surrounding efforts to limit warming are frequently communicated in terms of the point in time at which net anthropogenic CO2 emissions reach zero, accompanied by substantial reductions in non-CO2 emissions (IPCC 2018, Chapter 3). Net-zero GHG goals are also common, and they require net-negative CO2 emissions to compensate for residual non-CO2 emissions. Economy-wide CO2 and GHG goals appear in many government and corporate decarbonisation strategies, and they are used in a variety of ways. Most existing carbon-neutrality commitments from countries and sub-national jurisdictions aim for economies with very low emissions rather than zero emissions. Offsets, carbon dioxide removal (CDR) methods, and/or land sink assumptions are used to achieve net-zero goals (Kelly Levin et al. 2020).

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Hmiel, B. et al., 2020: Preindustrial 14CH4 indicates greater anthropogenic fossil CH4 emissions. Nature, 578(7795) , 409–412, doi:10.1038/s41586-020–1991-8.

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Höglund-Isaksson, L., A. Gómez-Sanabria, Z. Klimont, P. Rafaj, and W. Schöpp, 2020: Technical potentials and costs for reducing global anthropogenic methane emissions in the 2050 timeframe – results from the GAINS model. Environ. Res. Commun. , 2(2) , 025004, doi:10.1088/2515-7620/ab7457.

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The AFOLU (managed land) sector, on average, accounted for 13–21% of global total anthropogenic greenhouse gas (GHG) emissions in the period 2010–2019 (medium confidence). At the same time managed and natural terrestrial ecosystems were a carbon sink, absorbing around one third of anthropogenic CO2 emissions (medium confidence). Estimated anthropogenic net CO2 emissions from AFOLU (based on book-keeping models) result in a net source of +5.9 ± 4.1GtCO2 yr –1 between 2010 and 2019 with an unclear trend. Based on FAOSTAT or national GHG inventories, the net CO2 emissions from AFOLU were 0.0 to +0.8 GtCO2 yr –1 over the same period. There is a discrepancy in the reported CO2AFOLU emissions magnitude because alternative methodological approaches that incorporate different assumptions are used. If the managed and natural responses of all land to both anthropogenic environmental change and natural climate variability, estimated to be a gross sink of –12.5 ± 3.2 GtCO2 yr –1 for the period 2010–2019, are included with land use emissions, then land overall, constituted a net sink of –6.6 ± 5.2 GtCO2 yr –1 in terms of CO2 emissions (medium confidence). {7.2, 7.2.2.5, Table 7.1; IPCC AR6 WGI}

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There is a discrepancy, equating to 5.5GtCO2yr–1 between alternative methods of accounting for anthropogenic land CO2fluxes. Reconciling these methods greatly enhances the credibility of AFOLU-based emissions offsetting. It would also assist in assessing collective progress in a global stocktake (high confidence). The principal accounting approaches are national GHG inventories (NGHGI) and global modelling approaches. NGHGI, based on IPCC guidelines, consider a much larger area of forest to be under human management than global models. NGHGI consider the fluxes due to human-induced environmental change on this area to be anthropogenic and are thus reported. Global models, 2 in contrast, consider these fluxes to be natural and are excluded from the total reported anthropogenic land CO2 flux. To enable a like-with-like comparison, the remaining cumulative global CO2 emissions budget can be adjusted (medium confidence). In the absence of these adjustments, collective progress would appear better than it is. {Cross-Chapter Box 6 in this chapter, 7.2}

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Addressing the many knowledge gaps in the development and testing of AFOLU mitigation options can rapidly advance the likelihood of achieving sustained mitigation (high confidence). Research priorities include improved quantification of anthropogenic and natural GHG fluxes and emissions modelling, better understanding of the impacts of climate change on the mitigation potential, permanence and additionality of estimated mitigation actions, and improved (real time and cheap) measurement, reporting and verification. There is a need to include a greater suite of mitigation measures in IAMs, informed by more realistic assessments that take into account local circumstances and socio-economic factors and cross-sector synergies and trade-offs. Finally, there is a critical need for more targeted research to develop appropriate country-level, locally specific, policy and land management response options. These options could support more specific NDCs with AFOLU measures that enable mitigation while also contributing to biodiversity conservation, ecosystem functioning, livelihoods for millions of farmers and foresters, and many other Sustainable Development Goals (SDGs) ( high conf idence). {7.7}

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Agriculture, Forestry and Other Land Uses (AFOLU) is unique due to its capacity to mitigate climate change through greenhouse gas (GHG) emission reductions, as well as enhance removals (IPCC 2019). However, despite the attention on AFOLU since early 1990s it was reported in the IPCC Special Report on Climate Change and Land (SRCCL) as accounting for almost a quarter of anthropogenic emission (IPCC, 2019), with three main GHGs associated with AFOLU; carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O). Overall emission levels had remained similar since the publication of AR4 (Nabuurs et al. 2007). The diverse nature of the sector, its linkage with wider societal, ecological and environmental aspects and the required coordination of related policy, was suggested to make implementation of known and available supply- and demand-side mitigation measures particularly challenging (IPCC 2019). Despite such implementation barriers, the considerable mitigation potential of AFOLU as a sector on its own and its capacity to contribute to mitigation within other sectors was emphasised, with land-related measures, including bioenergy, estimated as capable of contributing between 20% and 60% of the total cumulative abatement to 2030 identified within transformation pathways (IPCC 2018). However, the vast mitigation potential from AFOLU initially portrayed in literature and in Integrated Assessment Models (IAMs), as explored in the IPCC Special Report on Climate Change of 1.5°C (SR1.5), is being questioned in terms of feasibility (Roe et al. 2021) and a more balanced perspective on the role of land in mitigation is developing, while at the same time, interest by private investors in land-based mitigation is increasing fast.

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Since the IPCC Fifth Assessment Report (AR5), the share of AFOLU to anthropogenic GHG emissions had remained largely unchanged at 13–21% of total GHG emissions (medium confidence), though uncertainty in estimates of both sources and sinks of CO2, exacerbated by difficulties in separating natural and anthropogenic fluxes, was emphasised. Models indicated land (including the natural sink) to have very likely provided a net removal of CO2 between 2007 and 2016. As in AR5, land cover change, notably deforestation, was identified as a major driver of anthropogenic CO2 emissions while agriculture was a major driver of the increasing anthropogenic CH4 and N2O emissions.

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In terms of mitigation, without reductions in overall anthropogenic emissions, increased reliance on large-scale land-based mitigation was predicted, which would add to the many already competing demands on land. However, some mitigation measures were suggested to not compete with other land uses, while also having multiple co-benefits, including adaptation capacity and potential synergies with some Sustainable Development Goals (SDGs). As in AR5, there was large uncertainty surrounding mitigation within AFOLU, in part because current carbon stocks and fluxes are unclear and subject to temporal variability. Additionally, the non-additive nature of individual measures that are often inter-linked and the highly context specific applicability of measures, causes further uncertainty. Many AFOLU measures were considered well-established and some achievable at low to moderate cost, yet contrasting economic drivers, insufficient policy, lack of incentivisation and institutional support to stimulate implementation among the many stakeholders involved, in regionally diverse contexts, was recognised as hampering realisation of potential.

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The biosphere on land and in wetlands is a source and sink of CO2 and CH4, and a source of N2O due to both natural and anthropogenic processes that happen simultaneously and are therefore difficult to disentangle (IPCC 2010; Angelo and Du Plesis 2017; IPCC 2019). AFOLU is the only GHG sector to currently include anthropogenic sinks. A range of methodological approaches and data have been applied to estimating AFOLU emissions and removals, each developed for their own purposes, with estimates varying accordingly. Since the SRCCL (Jia et al. 2019), emissions estimates have been updated (Sections 7.2.2 and 7.2.3), while the assessment of biophysical processes and short-lived climate forcers (Section 7.2.4) is largely unchanged. Further progress has been made on the implications of differences in AFOLU emissions estimates for assessing collective climate progress (Section 7.2.2.2 and Cross-Chapter Box 6 in this chapter).

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National greenhouse gas inventory (NGHGI) reporting following the IPCC 1996 guidelines (IPCC 1996), separates the total anthropogenic AFOLU flux into: (i) net anthropogenic flux from Land Use, Land-Use Change, and Forestry (LULUCF) due to both change in land cover and land management; and (ii) the net flux from Agriculture. While fluxes of CO2 (Section 7.2.2) are predominantly from LULUCF and fluxes of CH4 and N2O (Section 7.2.3) are predominantly from agriculture, fluxes of all three gases are associated with both sub-sectors. However, not all methods separate them consistently according to these sub-sectors, thus here we use the term AFOLU, separate by gas and implicitly include CO2 emissions that stem from the agriculture part of AFOLU, though these account for a relatively small portion.

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Total global net anthropogenic GHG emissions from AFOLU were 11.9 ± 4.4 GtCO2-eq yr –1 on average over the period 2010–2019, around 21% of total global net anthropogenic GHG emissions (Table 7.1 and Figure 7.3, using the sum of bookkeeping models for the CO2 component). When using FAOSTAT/NGHGIs CO2 flux data, then the contribution of AFOLU to total emissions amounts to 13% of global emissions.

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Table 7.1 | Net anthropogenic emissions (annual averages for2010–2019a) from Agriculture, Forestry and Other Land Use (AFOLU). For context, the net flux due to the natural response of land to climate and environmental change is also shown for CO2 in column E. Positive values represent emissions, negative values represent removals.

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bNet anthropogenic flux of CO2 are due to land-use change such as deforestation and afforestation and land management, including wood harvest and regrowth, peatland drainage and fires, cropland and grassland management. Average of three bookkeeping models (Hansis et al. 2015; Houghton and Nassikas 2017; Gasser et al. 2020), complemented by data on peatland drainage and fires from FAOSTAT (Prosperi et al. 2020) and GFED4s (van der Werf et al. 2017). Bookkeeping based CO2-LULUCF emissions (5.7±4.0) are consistent with AR6 WGI and Chapter 2 of this report. The value of 5.9(±4.1) includes CO2 emissions from urea application to managed soils and pasture. Comparisons with other estimates are discussed in 7.2.2. Based on NGHGIs and FAOSTAT, the range is 0 to 0.8 GtCO2 yr –1.

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eThe modelled CO2 estimates include natural processes in vegetation and soils and how they respond to both natural climate variability and to human-induced environmental changes, for example, the response of vegetation and soils to environmental changes such as increasing atmospheric CO2 concentration, nitrogen deposition, and climate change (indirect anthropogenic effects) on both managed and unmanaged lands. The estimate shown represents the average from 17 Dynamic Global Vegetation Models with 1SD uncertainty (Friedlingstein et al. 2020).

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f The NGHGIs take a different approach to calculating ‘anthropogenic’ CO2 fluxes than the models (Section 7.2.2). In particular the sinks due to environmental change (indirect anthropogenic fluxes) on managed lands are generally treated as anthropogenic in NGHGIs and non-anthropogenic in models such as bookkeeping and IAMs. A reconciliation of the results between IAMs and NGHGIs is presented in Cross-Chapter Box 6 in this chapter. If applied to this table, it would transfer approximately –5.5 GtCO2 yr –1 (a sink) from Column E (which would become –7.0 GtCO2 yr –1) to Column A (which would then be 0.4 GtCO2 yr –1).

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This AFOLU flux is the net of anthropogenic emissions of CO2, CH4 and N2O, and anthropogenic removals of CO2. The contribution of AFOLU to total emissions varies regionally with highest in Latin America and Caribbean with 58% and lowest in Europe and North America with each 7% (Chapter 2, Section 2.2.3). There is a discrepancy in the reported CO2AFOLU emissions magnitude because alternative methodological approaches that incorporate different assumptions are used (Section 7.2.2.2). While there is low agreement in the trend of global AFOLU CO2 emissions over the past few decades (Section 7.2.2), they have remained relatively constant (medium confidence) (Chapter 2). Average non-CO2 emission (aggregated using GWP100 IPCC AR6 values) from agriculture have risen from 5.2 ± 1.4 GtCO2-eq yr –1 for the period 1990 to 1999, to 6.0 ± 1.7 GtCO2-eq yr –1 for the period 2010 to 2019 (Crippa et al. 2021) (Section 7.2.3).

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To present a fuller understanding of land–atmosphere interactions, Table 7.1 includes an estimate of the natural sink of land to atmospheric CO2 (Jia et al. 2019) (IPCC AR6 WGI Chapter 5). Land fluxes respond naturally to human-induced environmental change (e.g., climate change, and the fertilising effects of increased atmospheric CO2 concentration and nitrogen deposition), known as ‘indirect anthropogenic effects’, and also to ‘natural effects’ such as climate variability (IPCC 2010) (Table 7.1 and Section 7.2.2). This showed a removal of –12.5 ± 3.2 GtCO2 yr –1 (medium confidence) from the atmosphere during 2010–2019 according to global dynamic global vegetation model (DGVM) models (Friedlingstein et al. 2020) 31% of total anthropogenic net emissions of CO2 from all sectors. It is likely that the NGHIs and FAOSTAT implicitly cover some part of this sink and thus provide a net CO2AFOLU balance with some 5 GtCO2 lower net emissions than according to bookkeeping models, with the overall net CO2 value close to being neutral. Model results and atmospheric observations concur that, when combining both anthropogenic (AFOLU) and natural processes on the entire land surface (the total ‘land–atmosphere flux’), the land was a global net sink for CO2 of –6.6 ± 4.6 GtCO2 yr –1 with a range for 2010 to 2019 from –4.4 to –8.4 GtCO2 yr –1. (Rödenbeck et al. 2003, 2018; Chevallier et al. 2005; Feng et al. 2016; van der Laan-Luijkx et al. 2017; Niwa et al. 2017; Patra et al. 2018). The natural land sink is highly likely to be affected by both future AFOLU activity and climate change (IPCC AR6 WGI Box 5.1 and Figure SPM. 7), whereby under more severe climate change, the amount of carbon stored on land would still increase although the relative share of the emissions that land takes up, declines.

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The processes responsible for fluxes from land have been divided into three categories (IPCC 2006, 2010): (i) the direct human-induced effects due to changing land cover and land management; (ii) the indirect human-induced effects due to anthropogenic environmental change, such as climate change, CO2 fertilisation, nitrogen deposition, and so on; and (iii)natural effects, including climate variability and a background natural disturbance regime (e.g., wildfires, windthrows, diseases or insect outbreaks).

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Global models estimate the anthropogenic land CO2 flux considering only the impact of direct effects, and only those areas that were subject to intense and direct management such as clear-cut harvest. It is important to note, that DGVMs also estimate the non-anthropogenic land CO2 flux (Land Sink) that results from indirect and natural effects (Table 7.1). In contrast, estimates of the anthropogenic land CO2 flux in NGHGIs (LULUCF) include the impact of direct effects and, in most cases, of indirect effects on a much greater area considered ‘managed’ than global models (Grassi et al. 2021).

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The approach used by countries follows the IPCC methodological guidance for NGHGIs (IPCC 2006, 2019). Since separating direct, indirect and natural effects on the land CO2 sink is impossible with direct observation such as national forest inventories (IPCC 2010), upon which most NGHGIs are based, the IPCC adopted the ‘managed land’ concept as a pragmatic proxy to facilitate NGHGI reporting. Anthropogenic land GHG fluxes (direct and indirect effects) are defined as all those occurring on managed land, that is, where human interventions and practices have been applied to perform production, ecological or social functions (IPCC 2006, 2019). GHG fluxes from unmanaged land are not reported in NGHGIs because they are assumed to be non-anthropogenic. Countries report NGHGI data with a range of methodologies, resolution and completeness, dependent on capacity and available data, consistent with IPCC guidelines (IPCC 2006, 2019) and subject to an international review or assessment processes.

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The FAOSTAT approach is conceptually similar to NGHGIs. FAOSTAT data on forests are based on country reports to FAO-FRA 2020 (FAO 2020a), and include changes in biomass carbon stock in ‘forest land’ and ‘net forest conversions’ in five-year intervals. ‘Forest land’ may include unmanaged natural forest, leading to possible overall overestimation of anthropogenic fluxes for both sources and sinks, though emissions from deforestation are likely underestimated (Tubiello et al. 2020). FAOSTAT also estimate emissions from forest fires and other land uses (organic soils), following IPCC methods (Prosperi et al. 2020). The FAO-FRA 2020 (FAO 2020b) update leads to estimates of larger sinks in Russia since 1991, and in China and the USA from 2011, and larger deforestation emissions in Brazil and smaller in Indonesia than FRA 2015 (FAO 2015; Tubiello et al. 2020).

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Remote sensing products can help to attribute changes to anthropogenic activity or natural inter-annual climate variability (Fan et al. 2019; Wigneron et al. 2020). Products with higher spatial resolution make it easier to determine forest and carbon dynamics in relatively small-sized managed forests (e.g., Y. Wang et al. 2020; Heinrich et al. 2021; Reiche et al. 2021). For example, secondary forest regrowth in the Brazilian Amazon offset 9 to 14% of gross emissions due to deforestation 1 (Aragão et al. 2018; Silva Junior et al. 2021). Yet disturbances such as fire and repeated deforestation cycles due to shifting cultivation over the period 1985 to 2017, were found to reduce the regrowth rates of secondary forests by 8 to 55% depending on the climate region of regrowth (Heinrich et al. 2021).

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There is about 5.5 GtCO2 yr –1 difference in the anthropogenic AFOLU estimates between NGHGIs and global models (this number relates to an IAMs comparison for the period 2005–2015 – see Cross-Chapter Box 6 in this chapter; for comparison with other models see Figure 7.4). Reconciling the differences, in other words, making estimates comparable, can build confidence in land-related CO2 estimates, for example for the purpose of assessing collective progress in the context of the Global Stocktake (Cross-Chapter Box 6 in this chapter). The difference largely results from greater estimated CO2 in NGHGIs, mostly occurring in forests (Grassi et al. 2021). This difference is potentially a consequence of: (i) simplified and/or incomplete representation of management in global models (Popp et al. 2017; Pongratz et al. 2018), for example, concerning impacts of forest management in biomass expansion and thickening (Nabuurs et al. 2013; Grassi et al. 2017), (ii) inaccurate and/or incomplete estimation of LULUCF fluxes in NGHGIs (Grassi et al. 2017), especially in developing countries, primarily in non-forest land uses and in soils, and (iii) conceptual differences in how global models and NGHGIs define ‘anthropogenic’ CO2 flux from land (Grassi et al. 2018). The impacts of (i) and (ii) are difficult to quantify and result in uncertainties that will decrease slowly over time through improvements of both models and NGHGIs. By contrast, the inconsistencies in (iii) and its resulting biases were assessed as explained below.

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Since changing the NGHGIs’ approach is impractical, an interim method to translate and adjust the output of global models was outlined for reconciling a bookkeeping model and NGHGIs (Grassi et al. 2018). More recently, an improved version of this approach has been applied to the future mitigation pathways estimated by IAMs (Grassi et al. 2021), with the implications for the Global Stocktake discussed in Cross-Chapter Box 6 in this chapter. This method implies a post-processing of current global models’ results that addresses two components of the conceptual differences in the ‘anthropogenic’ CO2 flux; (i) how the impact of human-induced environmental changes (indirect effects) are considered, and (ii) the extent of forest area considered ‘managed’. Essentially, this approach adds DGVM estimates of CO2 fluxes due to indirect effects from countries’ managed forest area (using non-intact forest area maps as a proxy) to the original global models’ anthropogenic land CO2 fluxes (Figure 7.6).

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In fact, there is about 5.5 GtCO2 yr –1 difference during 2005–2015 between global anthropogenic land CO2 net flux estimates of IAMs and aggregated NGHGIs, due to different conceptual approaches to what is ‘anthropogenic’. This approach and its implications when comparing climate targets with global mitigation pathways are illustrated in this Box Figure 1a–e.

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By adjusting the original IAM output (Cross-Chapter Box 6, Figure 1a) with the indirect effects from countries’ managed forest (Cross-Chapter Box 6, Figure 1b, estimated by DGVMs, see also Figure 7.6), NGHGI-comparable pathways can be derived (Cross-Chapter Box 6, Figure 1c). The resulting apparent increase in anthropogenic sink reflects simply a reallocation of a CO2 flux previously labelled as natural, and thus does not reflect a mitigation action. These changes do not affect non-LULUCF emissions. However, since the atmosphere concentration is a combination of CO2 emissions from LULUCF and from fossil fuels, the proposed land-related adjustments also influence the NGHGI-comparable economy-wide (all sector) CO2 pathways (Cross-Chapter Box 6, Figure 1d).

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This approach does not imply a change in the original decarbonisation pathways, nor does it suggest that indirect effects should be considered in the mitigation efforts. It simply ensures that a like-with-like comparison is made: if countries’ climate targets use the NGHGI definition of anthropogenic emissions, this same definition can be applied to derive NGHGI-comparable future CO2 pathways. This would have an impact on the NGHGI-comparable remaining carbon or GHG budget (i.e., the allowable emissions until net zero CO2 or GHG emissions consistent with a certain climate target). For example, for SSP2-1.9 and SSP2-2.6 (representing pathways in line with 1.5°C and well-below 2°C limits under SSP2 assumptions), carbon budget is 170 GtCO2-eq lower than the original remaining carbon budget according to the models’ approach (Cross-Chapter Box 6, Figure 1e). Similarly, the remaining carbon (or GHG) budgets in Chapter 3 (this report), as well as the net zero carbon (or GHG) targets, could only be used in combination with the definition of anthropogenic emissions as used by the IAMs (Cross-Chapter Box 3 in Chapter 3). In the absence of these adjustments, collective progress would appear better than it is.

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Using FAOSTAT data, the SRCCL estimated average CH4 emissions from AFOLU to be 161.2 ± 43 MtCH4 yr –1 for the period 2007–2016, representing 44% of total anthropogenic CH4 emissions, with agriculture accounting for 88% of the AFOLU component (Jia et al. 2019). The latest data (FAO 2021a, 2020b) highlight a trend of growing AFOLU CH4 emissions, with a 10% increase evident between 1990 and 2019, despite year-to-year variation. Forestry and other land use (FOLU) CH4 emission sources include biomass burning on forest land and combustion of organic soils (peatland fires) (FAO 2020c). The agricultural share of AFOLU CH4 emissions remains relatively unchanged, with the latest data indicating agriculture to have accounted for 89% of emissions on average between 1990 and 2019. The SRCCL reported with medium evidence and high agreement that ruminants and rice production were the most important contributors to overall growth trends in atmospheric CH4 (Jia et al. 2019). The latest data confirm this in terms of agricultural emissions, with agreement between databases that agricultural CH4 emissions continue to increase and that enteric fermentation and rice cultivation remain the main sources (Figure 7.7). The proportionally higher emissions from rice cultivation indicated by EDGAR data compared to the other databases, may result from the use of a Tier 2 methodology for this source within EDGAR (Janssens-Maenhout et al. 2019).

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The SRCCL also noted a trend of increasing atmospheric N2O concentration, with robust evidence and high agreement that agriculture accounted for approximately two-thirds of overall global anthropogenic N2O emissions. Average AFOLU N2O emissions were reported to be 8.7 ± 2.5 MtN2O yr –1 for the period 2007–2016, accounting for 81% of total anthropogenic N2O emissions, with agriculture accounting for 95% of AFOLU N2O emissions (Jia et al. 2019). A recent comprehensive review confirms agriculture as the principal driver of the growing atmospheric N2O concentration (Tian et al. 2020). The latest FAOSTAT data (FAO 2020b, 2021a) document a 25% increase in AFOLU N2O emissions between 1990 and 2019, with the average share from agriculture remaining approximately the same (96%). Agricultural soils were identified in the SRCCL and in recent literature as a dominant emission source, notably due to nitrogen fertiliser and manure applications to croplands, and manure production and deposition on pastures (Jia et al. 2019; Tian et al. 2020). There is agreement within latest data that agricultural soils remain the dominant source (Figure 7.7).

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Since AR5 several global assessments (IPBES 2018a; NYDF Assessment Partners 2019; UNEP 2019; IPCC 2019) and studies (e.g., Tubiello 2019; Tian et al. 2020) have reported on drivers (natural and anthropogenic factors that affect emissions and sinks of the land-use sector) behind AFOLU emissions trends, and associated projections for the coming decades. The following analysis aligns with the drivers typology used by IPBES (2019b) and the Global Environmental Outlook (UNEP 2019). Drivers are divided into direct drivers resulting from human decisions and actions concerning land use and land-use change, and indirect drivers that operate by altering the level or rate of change of one or more direct drivers. Although drivers of emissions in agriculture and FOLU are presented separately, they are interlinked, operating in many complex ways at different temporal and spatial scales, with outcomes depending on their interactions. For example, deforestation in tropical forests is a significant component of sectorial emissions. A review of deforestation drivers’ studies published between 1996 and 2013, indicated a wide range of factors associated with deforestation rates across many analyses and studies, covering different regions (Busch and Ferretti-Gallon 2017) (Figure 7.9). Higher agricultural prices were identified as a key driver of deforestation, while law enforcement, area protection, and ecosystem services payments were found to be important drivers of reduced deforestation, while timber activity did not show a consistent impact.

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Critical assessment and conclusion. There is medium confidence that coastal wetland protection has a technical potential of 0.8 (0.06–5.4) GtCO2-eq yr –1 of which 0.17 (0.06–0.27) GtCO2-eq yr –1 is available up to USD100 tCO2–1. There is a high certainty (robust evidence, high agreement ) that coastal ecosystems have among the largest carbon stocks of any ecosystem. As these ecosystems provide many important services, reduced conversion of coastal wetlands is a valuable mitigation strategy with numerous co-benefits. However, the vulnerability of coastal wetlands to climatic and other anthropogenic stressors may limit the permanence of climate mitigation.

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Activities, co-benefits, risks and implementation barriers. Coastal wetland restoration involves restoring degraded or damaged coastal wetlands including mangroves, salt marshes, and seagrass ecosystems, leading to sequestration of ‘blue carbon’ in wetland vegetation and soil (SRCCL, Chapter 6; SROCC, Chapter 5). Successful approaches to wetland restoration include: (i) passive restoration, the removal of anthropogenic activities that are causing degradation or preventing recovery; and (ii) active restoration, purposeful manipulations to the environment in order to achieve recovery to a naturally functioning system (Elliott et al. 2016) (IPCC AR6 WGII Chapter 3). Restoration of coastal wetlands delivers many valuable co-benefits, including enhanced water quality, biodiversity, aesthetic values, fisheries production (food security), and protection from rising sea levels and storm impacts (Barbier et al. 2011; Hochard et al. 2019; Sun and Carson 2020; Duarte et al. 2020). Of the 0.3 Mkm 2 coastal wetlands globally, 0.11 Mkm 2 of mangroves are considered feasible for restoration (Griscom et al. 2017). Risks associated with coastal wetland restoration include uncertain permanence under future climate scenarios (IPCC AR6 WGII, Box 3.4), partial offsets of mitigation through enhanced methane and nitrous oxide release and carbonate formation, and competition with other land uses, including aquaculture and human settlement and development in the coastal zone (SROCC, Chapter 5). To date, many coastal wetland restoration efforts do not succeed due to failure to address the drivers of degradation (van Katwijk et al. 2016). However, improved frameworks for implementing and assessing coastal wetland restoration are emerging that emphasise the recovery of ecosystem functions (Zhao et al. 2016; Cadier et al. 2020). Restoration projects that involve local communities at all stages and consider both biophysical and socio-political context are more likely to succeed (Brown et al. 2014; Wylie et al. 2016).

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Land-based mitigation options interact and create various trade-offs, and thus need to be assessed together as well as with mitigation options in other sectors, and in combination with other sustainability goals (Popp et al. 2014; Obersteiner et al. 2016; Roe et al. 2019; Van Vuuren et al. 2019; Prudhomme et al. 2020; Strefler et al. 2021). The assessments of individual mitigation measures or sectoral estimates used to estimate mitigation potential in Section 7.4, when aggregated together, do not account for interactions and trade-offs. Integrative land-use models (ILMs) combine different land-based mitigation options and are partially included in Integrated Assessment Models (IAMs) which combine insights from various disciplines in a single framework and cover the largest sources of anthropogenic GHG emissions from different sectors. Over time, ILMs and IAMs have extended their system coverage (Johnson et al. 2019). However, the explicit modelling and analysis of integrated land-use systems is relatively new compared to other sectoral assessments such as the energy system (Jia et al. 2019). Consequently, ILMs as well as IAMs differ in their portfolio and representation of land-based mitigation options, the representation of sustainability goals other than climate action as well as the interplay with mitigation in other sectors (van Soest et al. 2019; Johnson et al. 2019). These structural differences have implications for the regional and global deployment of different mitigation options as well as their sustainability impacts.

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Permanence focuses on the potential for carbon sequestered in offsets to be released in the future due to natural or anthropogenic disturbances. Most offset registries have strong permanence requirements, although they vary in their specific requirements. The Verified Carbon Standard (VCS) from the Verra programme requires a pool of additional carbon credits that provides a buffer against inadvertent losses. The Climate Action Reserve (CAR) protocol for forests requires carbon to remain on the site for 100 years. The carbon on the site will be verified at pre-determined intervals over the life of the project. If carbon is diminished on a given site, the credits for the site have to be relinquished and the project developer has to use credits from their reserve fund (either other projects or purchased credits) to make up for the loss. Estimates of leakage in forestry projects in AR5 suggest that it can range from 10% to over 90% in the USA (Murray et al. 2004), and 20–50% in the tropics (Sohngen and Brown 2004) for forest set-asides and reduced harvesting. Carbon offset protocols have made a variety of assumptions. The Climate Action Reserve (CAR) assumes it is 20% in the USA. One of the voluntary protocols (Verra) uses specific information about the location of the project to calculate a location specific leakage factor.

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Population growth, economic development, urbanisation, technology, climate change, global trade and consumption, policy and governance are key drivers of global environmental change over recent decades (Kram et al. 2014; UNEP 2019; WWF 2020). Changes in biodiversity and ecosystem services are mainly driven by habitat loss, climate change, invasive species, over-exploitation of natural resources, and pollution (Millenium Ecosystem Assesment 2005). The relative importance of these drivers varies across biomes, regions, and countries. Climate change is expected to be a major driver of biodiversity loss in the coming decades, followed by commercial forestry and bioenergy production (OECD 2012; UNEP 2019). Population growth along with rising incomes and changes in consumption and dietary patterns, will exert immense pressure on land and other natural resources (IPCC 2019). Current estimates suggest that 75% of the land surface has been significantly anthropogenically altered, with 66% of the ocean area experiencing increasing cumulative impacts and over 85% of wetland area lost (IPBES 2019a). As discussed, in Section 7.3, land-use change is driven amongst others by agriculture, forestry (logging and fuelwood harvesting), infrastructural development and urbanisation, all of which may also generate localised air, water, and soil pollution (IPBES 2019a). Over a third of the world’s land surface and nearly three-quarters of available freshwater resources are devoted to crop or livestock production (IPBES 2019a). Despite a slight reduction in global agricultural area since 2000, regional agricultural area expansion has occurred in Latin America and the Caribbean, Africa and the Middle East (FAO 2019c; OECD and FAO 2019). The degradation of tropical forests and biodiversity hotspots, endangers habitat for many threatened and endemic species, and reduces valuable ecosystem services. However, trends vary considerably by region. As noted in Section 7.3, global forest area declined by roughly 178 Mha between 1990 and 2020 (FAO 2020a), though the rate of net forest loss has decreased over the period, due to reduced deforestation in some countries and forest gains in others. Between 1990 to 2015, forest cover fell by almost 13% in South-East Asia, largely due to an increase in timber extraction, large-scale biofuel plantations and expansion of intensive agriculture and shrimp farms, whereas in North-East Asia and South Asia it increased by 23% and 6% respectively, through policy instruments such as joint forest management, payment for ecosystem services, and restoration of degraded forests (IPBES 2018b). It is lamenting that the area under natural forests which are rich in biodiversity and provide diverse ecosystem services decreased by 301 Mha between 1990 and 2020, decreasing in most regions except Europe and Oceania with largest losses reported in sub-Saharan Africa (FAO 2020a). The increasing trend of mining in forest and coastal areas, and in river basins for extracting has had significant negative impacts on biodiversity, air and water quality, water distribution, and on human health (Section 7.3). Freshwater ecosystems equally face a series of combined threats including from land-use change, water extraction, exploitation, pollution, climate change and invasive species (IPBES 2019a).

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IPCC, 2010: Revisiting the use of managed land as a proxy for estimating national anthropogenic emissions and removals. [Eggleston, H.S., N. Srivastava, K.Tanabe, and J. Baasansuren (eds.)]. Institute for Global Environmental Strategies (IGES), Kanagawa, Japan, 56 pp.

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Jackson, R.B. et al., 2020: Increasing anthropogenic methane emissions arise equally from agricultural and fossil fuel sources. Environ. Res. Lett. , 15(7) , 071002, doi:10.1088/1748-9326/ab9ed2.

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Barkhordarian, A., H. von Storch, E. Zorita, P.C. Loikith, and C.R. Mechoso, 2018: Observed warming over northern South America has an anthropogenic origin. Clim. Dyn. , 51(5–6) , 1901–1914, doi:10.1007/s00382-017-3988-z.

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However, increasing media coverage does not always lead to more accurate coverage of climate change mitigation, as it can also spur diffusion of misinformation (Boykoff and Yulsman 2013; van der Linden et al. 2015; Whitmarsh and Corner 2017; Fahy 2018; Painter 2019). In addition, media professionals have at times drawn on the norm of representing both sides of a controversy, bearing the risk of the disproportionate representation of scepticism of anthropogenic climate change despite the convergent agreement in climate science that humans contribute to climate change, (robust evidence, high agreement ) (Freudenburg and Muselli 2010; Boykoff 2013; Painter and Gavin 2016; Tindall et al. 2018; McAllister et al. 2021). This occurs despite increasing consensus among journalists regarding the basic scientific understanding of climate change (Brüggemann and Engesser 2017).

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Heede, R., 2014: Tracing anthropogenic carbon dioxide and methane emissions to fossil fuel and cement producers, 1854–2010. Clim. Change, 122(1–2) , 229–241, doi:10.1007/s10584-013-0986-y.

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Winiwarter, W., L. Höglund-Isaksson, Z. Klimont, W. Schöpp, and M. Amann, 2018: Technical opportunities to reduce global anthropogenic emissions of nitrous oxide. Environ. Res. Lett. , 13(1) , 014011, doi:10.1088/1748-9326/aa9ec9.

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The overall purpose of international cooperation through the Paris Agreement is to enhance the implementation of the UNFCCC, including its objective of stabilising atmospheric GHG concentrations ‘at a level that would prevent dangerous anthropogenic interference with the climate system’ (UNFCCC 1992, Art. 2). The Paris Agreement aims to strengthen the global response to the threat of climate change, in the context of sustainable development and efforts to eradicate poverty, by inter alia‘[h]olding the increase in the global average temperature to well below 2°C above pre-industrial levels and pursuing efforts to limit the temperature increase to 1.5°C above pre-industrial levels’ (UNFCCC 2015a, Art. 2(1)(a)). There is an ongoing structured expert dialogue under the UNFCCC in the context of the second periodic review of the long-term global goal (the first was held between 2013–2015) aimed at enhancing understanding of the long-term global goal, pathways to achieving it, and assessing the aggregate effect of steps taken by Parties to achieve the goal.

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A particular locus for international cooperation on technology development and innovation is found within institutions and mechanisms of the UN climate regime. The UNFCCC, in Article 4.1(c), calls on ‘all Parties’ to ‘promote and cooperate in the development, application and diffusion, including transfer, of technologies, practices and processes that control, reduce or prevent anthropogenic emissions of greenhouse gases’ and places responsibility on developed country Parties to ‘take all practicable steps to promote, facilitate and finance, as appropriate, the transfer of, or access to environmentally sound technologies and know-how to other Parties, particularly developing country Parties, to enable them to implement the provisions of the Convention’ (UNFCCC 1992, Art. 4.5). The issue of technology development and transfer has continued to receive much attention in the international climate policy domain since its initial inclusion in the UNFCCC in 1992 – albeit often overshadowed by dominant discourses around market-based mechanisms – and its role in reducing GHG emissions and adapting to the consequences of climate change ‘is seen as becoming ever more critical’ (de Coninck and Sagar 2015a). Milestones in the development of international cooperation on climate technologies under the UNFCCC have included: (i) the development of a technology transfer framework and establishment of the Expert Group on Technology Transfer (EGTT) under the SBSTA in 2001; (ii) recommendations for enhancing the technology transfer framework put forward at the Bali COP in 2007 and creation of the Poznan strategic programme on technology transfer under the GEF; and (iii) the establishment of the Technology Mechanism by the COP in 2010 as part of the Cancun Agreements (UNFCCC 2010b). The Technology Mechanism is presently the principal avenue within the UNFCCC for facilitating cooperation on the development and transfer of climate technologies to developing countries (UNFCCC 2015b). As discussed in Section 14.3.2.9 above, the Paris Agreement tasks the Technology Mechanism also to serve the Paris Agreement (UNFCCC 2015b, Art. 10.3).

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Another MEA that may play a role in aiding climate change mitigation is the 2013 Minamata Convention on Mercury, which came into force on 16 August 2017. Coal burning for electricity generation represents the second largest source (behind artisanal and small-scale gold mining) of anthropogenic mercury emissions to air (UNEP 2013). Efforts to control and reduce atmospheric emissions of mercury from coal-fired power generation under the Minamata Convention may reduce GHG emissions from this source (Eriksen and Perrez 2014; Selin 2014). For instance, Giang et al. (2015) have modelled the implications of the Minamata Convention for mercury emissions from coal-fired power generation in India and China, concluding that reducing mercury emissions from present-day levels in these countries is likely to require ‘avoiding coal consumption and transitioning toward less carbon-intensive energy sources’ (Giang et al. 2015). Parties to the Minamata Convention include five of the six top global CO2 emitters – China, the United States, the EU, India and Japan (Russia has not ratified the Convention). The Minamata Convention also establishes an Implementation and Compliance Committee to review compliance with its provisions on a ‘facilitative’ basis (Eriksen and Perrez 2014).

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In sum, existing international agreements have had a small impact on reducing emissions from the AFOLU sector and some success in achieving the enhancement of sinks through restoration. However, these outcomes are nowhere near levels required to meet the Paris Agreement temperature goal – which would require turning land use and forests globally from a net anthropogenic source during 1990–2010 to a net sink of carbon by 2030, and providing a quarter of emissions reductions planned by countries (Grassi et al. 2017). The AFOLU sector has so far contributed only modestly to net mitigation (Chapter 7).

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The Global Methane Initiative (GMI) is a multilateral partnership launched in 2004 by the United States Environmental Protection Agency along with 36 other countries to generate a voluntary, non-binding agenda for global collaboration to decrease anthropogenic methane releases. The GMI builds on the Methane to Markets (M2M) Partnership, an international partnership launched in 2004. In addition to the GMI’s own financial assistance, the initiative receives financial backing from the Global Methane Fund (GMF) for methane reduction projects. The GMF is a fund created by governments and private donors (Leonard 2014).

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Lee, D.S. et al., 2021a: The contribution of global aviation to anthropogenic climate forcing for 2000 to 2018. Atmos. Environ. , 244, 117834, doi:10.1016/j.atmosenv.2020.117834.

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The ocean, which covers over 70% of the Earth’s surface, contains about 38,000 gigatonnes of carbon, some 45 times more than the present atmosphere, and oceanic uptake has already consumed close to 30–40% of anthropogenic carbon emissions (Sabine et al. 2004; Gruber et al. 2019). The ocean is characterised by diverse biogeochemical cycles involving carbon, and ocean circulation has much longer timescales than the atmosphere, meaning that additional anthropogenic carbon could potentially be stored in the ocean for centuries to millennia for methods that increase deep ocean-dissolved carbon concentrations or temporarily bury the carbon; or essentially permanently (over ten thousand years) for methods that store the carbon in mineral forms or as ions by increasing alkalinity (Siegel et al., 2021) (Cross-Chapter Box 8, Figure 1). A wide range of methods and implementation options for marine CDR have been proposed (Gattuso et al. 2018; Hoegh-Guldberg et al. 2018; GESAMP 2019). The most studied ocean-based CDR methods are ocean fertilisation, alkalinity enhancement (including electrochemical methods) and intensification of biologically-driven carbon fluxes and storage in marine ecosystems, referred to as ‘blue carbon’. The mitigation potentials, costs, co-benefits and trade-offs of these three options are discussed below. Less well studied are methods including artificial upwelling, terrestrial biomass dumping into oceans, direct CO2 removal from seawater (with CCS), and sinking marine biomass into the deep ocean or harvesting it for bioenergy (with CCS) or biochar (GESAMP 2019). These methods are summarised briefly below. Potential climate response and influence on the carbon budget of ocean-based CDR methods are discussed in WGI AR6, Chapter 5.

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The SRCCL estimated overall global anthropogenic emissions from food systems to range between 10.8 and 19.1 GtCO2-eq yr –1, equivalent to 21–37% of total anthropogenic emissions (Mbow et al. 2019; Rosenzweig et al. 2020a). The authors identified major knowledge gaps for the GHG emissions inventories of food systems, particularly in providing disaggregated emissions from the food industry and transportation. The food system approach taken in the SRCCL (Mbow et al. 2019) evaluates the synergies and trade-offs of food system response options and their implications for food security, climate change adaptation and mitigation. This integrated framework allows the identification of fundamental attributes of responses to maximise co-benefits, while avoiding maladaptation measures and adverse side effects. A food system approach supports the design of interconnected climate policy responses to tackle climate change, incorporating perspectives of producers and consumers. The SRCCL (Mbow et al. 2019) found that the technical mitigation potential by 2050 of demand-side responses at 0.7–8.0 GtCO2-eq yr –1 is comparable to supply-side options at 2.3–9.6 GtCO2-eq yr –1. This shows that mitigation actions need to go beyond food producers and suppliers to incorporate dietary changes and consumers’ behavioural patterns and reveals that producers and consumers need to work together to reduce GHG emissions.

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New calculations using the EDGAR v6.0 (Crippa et al. 2021a) and FAOSTAT (FAO 2021) databases provide territorial-based food system GHG emissions by country globally for the period 1990 to 2018 (Crippa et al. 2021b). The data are calculated based on a combination of country-specific data and aggregated information as described byCrippa et al. (2021b) and Tubiello et al. (2021). The data show that, in 2018, 17 GtCO2-eq yr –1 (95% confidence range 13–23GtCO2-eq yr –1, calculated according to Solazzo et al. (2020)) were associated with the production, processing, distribution, consumption of food and management of food system residues. This corresponded to 31% (range 23–42%) of total anthropogenic GHG emissions of 54 GtCO2-eq yr –1. Based on the IPCC sectoral classification (Table 12.7 and Figure 12.5), the largest contribution of food systems GHG emissions in 2018 was from agriculture, that is, livestock and crop production systems (6.3 GtCO2-eq yr –1, range 2.6–11.9) and land use, land use change and forestry (LULUCF) (4.0 GtCO2-eq yr –1, range 2.1–5.9) (Figure 12.5). Emissions from energy use were 3.9 GtCO2-eq yr –1 (3.6–4.4) , waste management 1.7 GtCO2-eq yr –1 (0.9–2.6), and industrial processes and product use 0.9 GtCO2-eq yr –1 (0.6–1.1). The share of GHG emissions from food systems generated outside the AFOLU (agriculture and LULUCF) sectors has increased over recent decades, from 28% in 1990 to 39% in 2018.

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Limiting the increase in warming to well below 2°C, and achieving net zero CO2 or GHG emissions, will require anthropogenic CO2 removal from the atmosphere.

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Activities associated with the food system caused about one-third of total anthropogenic GHG emissions in 2015, distributed across all sectors. Agriculture and fisheries produce crops and animal-source food, which are partly processed in the food industry, packed, distributed, retailed, cooked, and finally eaten. Each step is associated with resource use, waste generation, and GHG emissions.

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Beuttler, C., L. Charles, and J. Wurzbacher, 2019: The Role of Direct Air Capture in Mitigation of Anthropogenic Greenhouse Gas Emissions. Front. Clim. , 1, 10, doi:10.3389/fclim.2019.00010.

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Crippa, M., E. Solazzo, D. Guizzardi, F. Monforti-Ferrario, and A. Leip, 2021b: Food systems are responsible for a third of global anthropogenic GHG emissions. Nat. Food,. 2, 198–209, https://www.nature.com/articles/s43016-021-00225-9.

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Gruber, N. et al., 2019: The oceanic sink for anthropogenic CO2 from 1994 to 2007. Science, 363(6432) , 1193–1199, doi:10.1126/science.aau5153.

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Höglund-Isaksson, L. et al., 2020: Technical potentials and costs for reducing global anthropogenic methane emissions in the 2050 timeframe –results from the GAINS model. Environ. Res. Commun. , 2(2) , 25004, doi:10.1088/2515-7620/ab7457.

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House, K.Z., C.H. House, D.P. Schrag, and M.J. Aziz, 2007: Electrochemical Acceleration of Chemical Weathering as an Energetically Feasible Approach to Mitigating Anthropogenic Climate Change. Environ. Sci. Technol. , 41(24) , 8464–8470, doi:10.1021/es0701816.

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Observed increases in well-mixed GHG concentrations since around 1750 are unequivocally caused by GHG emissions from humanactivities. Land and ocean sinks have taken up a near-constant proportion (globally about 56% per year) of CO2 emissions from humanactivities over the past six decades, with regional differences (high confidence). In 2019, atmospheric CO2 concentrations reached 410 parts per million (ppm), CH4 reached 1866 parts per billion (ppb) and nitrous oxide (N2O) reached 332 ppb68 . Other major contributors to warming aretropospheric ozone (O3) and halogenated gases. Concentrations of CH4 and N2O have increased to levels unprecedented in at least 800,000 years (very high confidence), and there ishigh confidencethat current CO2 concentrations are higher than at any time over at least the past two million years. Since 1750, increases in CO2 (47%) and CH4 (156%) concentrations far exceed – and increases in N2O (23%) are similar to – the natural multi-millennial changes between glacial and interglacialperiods overat least the past 800,000 years (very high confidence). The net cooling effect which arises from anthropogenic aerosols peaked in the late 20th century (high confidence). {WGI SPM A1.1, WGI SPM A1.3, WGI SPM A.2.1, WGI Figure SPM.2, WGI TS 2.2, WGI 2ES, WGI Figure 6.1}

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Average annual GHG emissions during 2010 –2019 were higher than in any previous decade, but the rate of growth between 2010 and 2019 (1.3% yr-1 ) was lower than that between 2000 and 2009 (2.1% yr-1 ) 69. Historical cumulative net CO2 emissions from 1850 to 2019 were 2400 ±240 GtCO2. Of these, more than half (58%) occurred between 1850 and 1989 [1400 ±195 GtCO2], and about 42% between 1990 and 2019 [1000 ±90 GtCO2]. Global net anthropogenic GHG emissions have been estimated to be 59±6.6 GtCO2-eq in 2019, about 12% (6.5 GtCO2-eq) higher than in 2010 and 54% (21 GtCO2-eq) higher than in 1990. By 2019, the largest growth in gross emissions occurred in CO2 from fossil fuels and industry (CO2-FFI) followed by CH4, whereas the highest relative growth occurred in fluorinated gases (F-gases), starting from low levels in 1990. (high confidence) {WGIII SPM B1.1, WGIII SPM B.1.2, WGIII SPM B.1.3, WGIII Figure SPM.1, WGIII Figure SPM.2}

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Regional contributions to global human-caused GHG emissions continue to differ widely. Historical contributions of CO2 emissions vary substantially across regions in terms of total magnitude, but also in terms of contributions to CO2-FFI (1650 ± 73 GtCO2-eq) and net CO2-LULUCF (760 ± 220 GtCO2-eq) emissions (Figure 2.2). Variations in regional and national per capita emissions partly reflect different development stages, but they also vary widely at similar income levels. Average per capita net anthropogenic GHG emissions in 2019 ranged from 2.6 tCO2-eq to 19 tCO2-eq across regions (Figure 2.2). Least Developed Countries (LDCs) and Small Island Developing States (SIDS) have much lower per capita emissions (1.7 tCO2-eq and 4.6 tCO2-eq, respectively) than the global average (6.9 tCO2-eq), excluding CO2-LULUCF. Around 48% of the global population in 2019 lives in countries emitting on average more than 6 tCO2-eq per capita, 35% of the global population live in countries emitting more than 9 tCO2-eq per capita 70 (excluding CO2-LULUCF) while another 41% live in countries emitting less than 3 tCO2-eq per capita. A substantial share of the population in these low-emitting countries lack access to modern energy services. (high confidence) {WGIII SPM B.3, WGIII SPM B3.1, WGIII SPM B.3.2, WGIII SPM B.3.3}

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WGI assessed the climate response to five illustrative scenarios based on SSPs 105 that cover the range of possible future development of anthropogenic drivers of climate change found in the literature. These scenarios combine socio-economic assumptions, levels of climate mitigation, land use and air pollution controls for aerosols and non-CH4 ozone precursors. The high and very high GHG emissions scenarios (SSP3-7.0 and SSP5-8.5) have CO2 emissions that roughly double from current levels by 2100 and 2050, respectively 106 . The intermediate GHG emissions scenario (SSP2-4.5) has CO2 emissions remaining around current levels until the middle of the century. The very low and low GHG emissions scenarios (SSP1-1.9 and SSP1-2.6) have CO2 emissions declining to net zero around 2050 and 2070, respectively, followed by varying levels of net negative CO2 emissions. In addition, Representative Concentration Pathways (RCPs)107 were used by WGI and WGII to assess regional climate changes, impacts and risks. {WGI BoxSPM.1} (Cross-Section Box.2 Figure 1)

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Changes in short-lived climate forcers (SLCF) resulting from the five considered scenarios lead to an additional net global warming in the near and long term (high confidence) . Simultaneous stringent climate change mitigation and air pollution control policies limit this additional warming and lead to strong benefits for air quality (high confidence) . In high and very high GHG emissions scenarios (SSP3-7.0 and SSP5-8.5), combined changes in SLCF emissions, such as CH4, aerosol and ozone precursors, lead to a net global warming by 2100 of likely 0.4°C to 0.9°C relative to 2019. This is due to projected increases in atmospheric concentration of CH4, tropospheric ozone, hydrofluorocarbons and, when strong air pollution control is considered, reductions of cooling aerosols. In low and very low GHG emissions scenarios (SSP1-1.9 and SSP1-2.6), air pollution control policies, reductions in CH4 and other ozone precursors lead to a net cooling, whereas reductions in anthropogenic cooling aerosols lead to a net warming (high confidence). Altogether, this causes a likely net warming of 0.0°C to 0.3°C due to SLCF changes in 2100 relative to 2019 and strong reductions in global surface ozone and particulate matter (high confidence). {WGI SPMD.1.7, WGI Box TS.7}. (Cross-Section Box.2)

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Solar Radiation Modification (SRM) approaches, if they were to be implemented, introduce a widespread range of new risks to people and ecosystems, which are not well understood. SRM has the potential to offset warming within one or two decades and ameliorate some climate hazards but would not restore climate to a previous state, and substantial residual or overcompensating climate change would occur at regional and seasonal scales (high confidence). Effects of SRM would depend on the specific approach used 122 , and a sudden and sustained termination of SRM in a high CO2 emissions scenario would cause rapid climate change (high confidence). SRM would not stop atmospheric CO2 concentrations from increasing nor reduce resulting ocean acidification under continued anthropogenic emissions (high confidence). Large uncertainties and knowledge gaps are associated with the potential of SRM approaches to reduce climate change risks. Lack of robust and formal SRM governance poses risks as deployment by a limited number of states could create international tensions.{WGI 4.6; WGII SPM B.5.5; WGIII 14.4.5.1; WGIII 14 Cross-Working Group Box Solar Radiation Modification; SR1.5 SPM C.1.4}

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The likelihood of abrupt and irreversible changes and their impacts increase with higher global warming levels (high confidence). As warming levels increase, so do the risks of species extinction or irreversible loss of biodiversity in ecosystems such as forests (medium confidence), coral reefs (very high confidence) and in Arctic regions (high confidence). Risks associated with large-scale singular events or tipping points, such as ice sheet instability or ecosystem loss from tropical forests, transition to high risk between 1.5°C to 2.5°C (medium confidence) and to very high risk between 2.5°C to 4°C (low confidence). The response of biogeochemical cycles to anthropogenic perturbations can be abrupt at regional scales and irreversible on decadal to century time scales (high confidence). The probability of crossing uncertain regional thresholds increases with further warming (high confidence). {WGI SPMC.3.2, WGI Box TS.9, WGI TS.2.6; WGII Figure SPM.3, WGII SPM B.3.1, WGII SPM B.4.1, WGII SPM B.5.2, WGII Table TS.1, WGII TS.C.1, WGII TS.C.13.3; SROCC SPM B.4}

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Limiting human-caused global warming requires net zero anthropogenic CO2 emissions. Pathways consistent with 1.5°C and 2°C carbon budgets imply rapid, deep, and in most cases immediate GHG emission reductions in all sectors (high confidence). Exceeding a warming level and returning (i.e. overshoot) implies increased risks and potential irreversible impacts; achieving and sustaining global net negative CO2 emissions would reduce warming (high confidence).

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The best estimates of the remaining carbon budget (RCB) from the beginning of 2020 for limiting warming to 1.5°C with a 50% likelihood127 is estimated to be 500 GtCO2 ; for 2°C (67% likelihood) this is 1150 GtCO2 . 128 Remaining carbon budgets have been quantified based on the assessed value of TCRE and its uncertainty, estimates of historical warming, climate system feedbacks such as emissions from thawing permafrost, and the global surface temperature change after global anthropogenic CO2 emissions reach net zero, as well as variations in projected warming from non-CO2 emissions due in part to mitigation action. The stronger the reductions in non-CO2 emissions the lower the resulting temperatures are for a given RCB or the larger RCB for the same level of temperature change. For instance, the RCB for limiting warming to 1.5°C with a 50% likelihood could vary between 300 to 600 GtCO2 depending on non-CO2 warming 129 . Limiting warming to 2°C with a 67% (or 83%) likelihood would imply a RCB of 1150 (900) GtCO2 from the beginning of 2020. To stay below 2°C with a 50% likelihood, the RCB is higher, i.e., 1350 GtCO2130 . {WGI SPM D.1.2, WGI Table SPM.2; WGIII Box SPM.1, WGIII Box 3.4; SR1.5 SPM C.1.3}

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From a physical science perspective, limiting human-caused global warming to a specific level requires limiting cumulative CO2 emissions, reaching net zero or net negative CO2 emissions, along with strong reductions of other GHG emissions (see Cross-Section Box.1). Global modelled pathways that reach and sustain net zero GHG emissions are projected to result in a gradual decline in surface temperature (high confidence). Reaching net zero GHG emissions primarily requires deep reductions in CO2, methane, and other GHG emissions, and implies net negative CO2 emissions. 134 Carbon dioxide removal (CDR) will be necessary to achieve net negative CO2 emissions 135 . Achieving global net zero CO2 emissions, with remaining anthropogenic CO2 emissions balanced by durably stored CO2 from anthropogenic removal, is a requirement to stabilise CO2-induced global surface temperature increase (see 3.3.3). (high confidence). This is different from achieving net zero GHG emissions, where metric-weighted anthropogenic GHG emissions (see Cross-Section Box.1) equal CO2 removal (high confidence). Emissions pathways that reach and sustain net zero GHG emissions defined by the 100-year global warming potential imply net negative CO2 emissions and are projected to result in a gradual decline in surface temperature after an earlier peak (high confidence). While reaching net zero CO2 or net zero GHG emissions requires deep and rapid reductions in gross emissions, the deployment of CDR to counterbalance hard-to-abate residual emissions (e.g., some emissions from agriculture, aviation, shipping, and industrial processes) is unavoidable (high confidence). {WGI SPM D.1, . WGI SPM D.1.1, WGI SPM D.1.8; WGIII SPM C.2, WGIII SPM C.3, WGIII SPM C.11, WGIII Box TS.6; SR1.5 SPM A.2.2}

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Anthropogenic land CO2 emissions and removals in Integrated Assessment Model (IAM) pathways cannot be directly compared with those reported in national GHG inventories (high confidence). Methodologies enabling a more like-for-like comparison between models’ and countries’ approaches would support more accurate assessment of the collective progress achieved under the Paris Agreement. {3.4, 7.2.2.5}

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Stabilising global average-temperature change requires reducing CO2 emissions to net zero. Thus, a central cross-cutting topic within the chapter is the timing of reaching net zero CO2 emissions and how a ‘balance between anthropogenic emissions by sources and removals by sinks’ could be achieved across time and space. This includes particularly the increasing body of literature since the IPCC Special Report on Global Warming of 1.5°C (SR1.5) which focuses on net zero CO2 emissions pathways that avoid temperature overshoot and hence do not rely on net negative CO2 emissions. The chapter conducts a systematic assessment of the associated economic costs as well as the benefits of mitigation for other societal objectives, such as the Sustainable Development Goals (SDGs). In addition, the chapter builds on SR1.5 and introduces a new conceptual framing for the assessment of possible social, economic, technical, political, and geophysical ‘feasibility’ concerns of alternative pathways, including the enabling conditions that would need to fall into place so that stringent climate goals become attainable.

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Scenario and emission pathways are used to explore possible long-term trajectories, the effectiveness of possible mitigation strategies, and to help understand key uncertainties about the future. Ascenario is an integrated description of a possible future of the human–environment system (Clarke et al. 2014), and could be a qualitative narrative, quantitative projection, or both. Scenarios typically capture interactions and processes that change key driving forces such as population, GDP, technology, lifestyles, and policy, and the consequences on energy use, land use, and emissions. Scenarios are not predictions or forecasts. An emission pathway is a modelled trajectory of anthropogenic emissions (Rogelj et al. 2018 a) and, therefore, a part of a scenario.

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In addition to the constraints on change in global mean temperature, the Paris Agreement also calls for reaching a balance of sources and sinks of GHG emissions (Art. 4). Different interpretations of the concept related to balance have been published (Rogelj et al. 2015c; Fuglestvedt et al. 2018). Key concepts include that of net zero CO2 emissions (anthropogenic CO2 sources and sinks equal zero) and net zero greenhouse gas emissions (see Annex I: Glossary, and Box 3.3). The same notion can be used for all GHG emissions, but here ranges also depend on the use of equivalence metrics (Box 2.1). Moreover, it should be noted that while reaching net zero CO2 emissions typically coincides with the peak in temperature increase; net zero GHG emissions (based on GWP-100) imply a decrease in global temperature (Riahi et al. 2021) and net zero GHG emissions typically require negative CO2 emissions to compensate for the remaining emissions from other GHGs. Many countries have started to formulate climate policy in the year that net zero emissions (either CO2 or all greenhouse gases) are reached – although, at the moment, formulations are often still vague (Rogelj et al. 2021). There has been increased attention on the timing of net zero emissions in the scientific literature and ways to achieve it.

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The concept of a finite carbon budget means that the world needs to get to net zero CO2, no matter whether global warming is limited to 1.5°C or well below 2°C (or any other level). Moreover, exceeding the remaining carbon budget will have consequences by overshooting temperature levels. Still, the relationship between the timing of net zero and temperature targets is a flexible one, as discussed further in Cross-Chapter Box 3 in this chapter. It should be noted that the national-level inventory as used by UNFCCC for the land use, land-use change and forestry sector is different from the overall concept of anthropogenic emissions employed by IPCC AR6 WGI. For emissions estimates based on these inventories, the remaining carbon budgets must be correspondingly reduced by approximately 15%, depending on the scenarios (Grassi et al., 2021) (Chapter 7).

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The accounting of anthropogenic carbon dioxide removal on land matters for the evaluation of net zero CO2and net zero GHG strategies. Due to the use of different approaches between national inventories and global models, the current net CO2 emissions are lower by 5.5 GtCO2, and cumulative net CO2 emissions in modelled 1.5°C–2°C pathways would be lower by 104–170 GtCO2, if carbon dioxide removals on land are accounted based on national GHG inventories. National GHG inventories typically consider a much larger area of managed forest than global models, and on this area additionally consider the fluxes due to human-induced global environmental change (indirect effects) to be anthropogenic, while global models consider these fluxes to be natural. Both approaches capture the same land fluxes, only the accounting of anthropogenic vs natural emissions is different. Methods to convert estimates from global models to the accounting scheme of national GHG inventories will improve the use of emission pathways from global models as benchmarks against which collective progress is assessed. (Section 7.2.2.5).

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Net zero CO2and carbon neutrality have different meanings in this assessment, as is the case for net zero GHG and GHG neutrality. They apply to different boundaries in the emissions and removals being considered. Net zero (GHG or CO2) refers to emissions and removals under the direct control or territorial responsibility of the reporting entity. In contrast, (GHG or carbon) neutrality includes anthropogenic emissions and anthropogenic removals within and also those beyond the direct control or territorial responsibility of the reporting entity. At the global scale, net zero CO2 and carbon neutrality are equivalent, as is the case for net zero GHG and GHG neutrality. The term ‘climate neutrality’ is not used in this assessment because the concept of climate neutrality is diffuse, used differently by different communities, and not readily quantified.

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Anthropogenic land CO2 emissions and removals in IAM pathways cannot be directly compared with those reported in national GHG inventories ( high confidence) (Grassi et al. 2018, 2021) (Section 7.2). Due to differences in definitions for the area of managed forests and which emissions and removals are considered anthropogenic, the reported anthropogenic land CO2 emissions and removals differ by about 5.5 GtCO2 yr –1 between IAMs, which rely on bookkeeping approaches (e.g., Houghton and Nassikas 2017), and national GHG inventories (Grassi et al. 2021). Such differences in definitions can alter the reported time at which anthropogenic net zero CO2 emissions are reached for a given emission scenario. Using national inventories would lead to an earlier reported time of net zero (van Soest et al. 2021b) or to lower calculated cumulative emissions until the time of net zero (Grassi et al. 2021) as compared to IAM pathways. The numerical differences are purely due to differences in the conventions applied for reporting the anthropogenic emissions and do not have any implications for the underlying land-use changes or mitigation measures in the pathways. Grassi et al. (Grassi et al. 2021) offer a methodology for adjusting to reconcile these differences and enable a more accurate assessment of the collective progress achieved under the Paris Agreement (Chapter 7 and Cross-Chapter Box 6 in Chapter 7).

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Dottori, F. et al., 2018: Increased human and economic losses from river flooding with anthropogenic warming. Nat. Clim. Change, 8(9) , 781–786, doi:10.1038/s41558-018-0257-z.

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Grassi, G. et al., 2018: Reconciling global-model estimates and country reporting of anthropogenic forest CO2 sinks. Nat. Clim. Change, 8(10) , 914–920, doi:10.1038/s41558-018-0283-x.

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Höglund-Isaksson, L., A. Gómez-Sanabria, Z. Klimont, P. Rafaj, and W. Schöpp, 2020: Technical potentials and costs for reducing global anthropogenic methane emissions in the 2050 timeframe –results from the GAINS model. Environ. Res. Commun. , 2(2) , 25004, doi:10.1088/2515-7620/ab7457.

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Takakura, J. et al., 2019: Dependence of economic impacts of climate change on anthropogenically directed pathways. Nat. Clim. Change, 9(10) , 737–741, doi:10.1038/s41558-019-0578-6.

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Since the start of direct measurements of CH4 in the atmosphere in the 1970s (Figure 5.13), the highest growth rate was observed from 1977 to 1986 at 18 ± 4 ppb yr–1 (multi-year mean and 1 standard deviation) (Rice et al., 2016). This rapid CH4 growth followed the green revolution with increased crop production and a fast rate of industrialization that caused rapid increases in CH4 emissions from ruminant animals, rice cultivation, landfills, oil and gas industry and coal mining (Ferretti et al., 2005; Ghosh et al., 2015; Crippa et al., 2020). Due to increases in oil prices in the early 1980s, emissions from gas flaring declined significantly (Stern and Kaufmann, 1996). This explains the first reduction in CH4 growth rates from 1985 to 1990 (Steele et al., 1992; Chandra et al., 2021). Further emissions reductions occurred following the Mt Pinatubo eruption in 1991 that triggered a reduction in CH4 growth rate through a decrease in wetland emissions driven by lower surface temperatures due to the light scattering by aerosols (Bândă et al., 2016; Chandra et al., 2021). In the late 1990s through to 2006 there was a temporary pause in the CH4 growth rate, with higher confidence on its causes than in AR5: emissions from the oil and gas sectors declined by about 10 Tg yr–1through the 1990s, and atmospheric CH4 loss steadily increased (Dlugokencky et al., 2003; Simpson et al., 2012; Crippa et al., 2020; Höglund-Isaksson et al., 2020; Chandra et al., 2021). The methane growth rate began to increase again at 7 ± 3 ppb yr–1 during 2007–2016, the causes of which are highly debated since AR5 (Rigby et al., 2008; Dlugokencky et al., 2011; Dalsøren et al., 2016; Nisbet et al., 2016; Patra et al., 2016; Schaefer et al., 2016; Schwietzke et al., 2016; Turner et al., 2017; Worden et al., 2017; He et al., 2020); studies disagree on the relative contribution of thermogenic, pyrogenic and biogenic emission processes and variability in tropospheric OH concentration. The renewed CH4 increase is accompanied by a reversal of d13C trend to more negative values post 2007; opposite to what occurred in the 200 years prior (Ferretti et al., 2005; Ghosh et al., 2015; Schaefer et al., 2016; Schwietzke et al., 2016; Nisbet et al., 2019), suggesting an increasing contribution from animal farming, landfills and waste, and a slower increase in emissions from fossil fuel exploitation since the early 2000s (Patra et al., 2016; Jackson et al., 2020; Chandra et al., 2021). Atmospheric concentrations of CH4 reached 1866.3 ppb in 2019 (Figure 5.14). A comprehensive assessment of the CH4 growth rates over the past four decades is presented in Cross-Chapter Box 5.2.

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The tropospheric abundance of N2O was 332.1 ± 0.4 ppb in 2019 (Figure 5.15), which is 23% higher than pre-industrial levels of 270.1 ± 6.0 ppb (robust evidence, high agreement). Current estimates are based on atmospheric measurements with high accuracy and density (Francey et al., 2003; Elkins et al., 2018; Prinn et al., 2018), and pre-industrial estimates are based on multiple ice-core records Section 2.2.3.2.3). The average annual tropospheric growth rate was 0.85 ± 0.03 ppb yr–1 during the period 1995 to 2019 (Figure 5.15a). The atmospheric growth rate increased by about 20% between the decade 2000–2009 and the most recent decade of 2010–2019 (0.95 ± 0.04 ppb yr–1) (robust evidence, high agreement). The growth rate in 2010–2019 was also higher than during 1970–2000 (0.6–0.8 ppb yr–1; Ishijima et al., 2007) and the 30-year period prior to 2011 (0.73 ± 0.03 ppb yr–1), as reported by AR5. New evidence since AR5 (WGI, Section 6.4.3) confirms that, in the tropics and subtropics, large interannual variations in the atmospheric growth rate are negatively correlated with the multivariate ENSO index (MEI) and associated anomalies in land and ocean fluxes (Ji et al., 2019; Thompson et al., 2019; S. Yang et al., 2020) (Figure 5.15a).

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CO2 and non-CO2 biogeochemical feedbacks are an important component of the assessment of TCRE and the remaining carbon budget (Section 5.5). The feedbacks of the carbon cycle of CO2 and climate are implicitly taken account in the TCRE assessment, because they are represented in the various underlying lines of evidence. Other feedback contributions, such as the non-CO2 biogeochemical feedback, can be converted into a carbon-equivalent feedback term (γ; Section 5.4.5.5, 7.6) by reverse application of the linear feedback approximation (Gregory et al., 2009). The contributions of non-CO2 biogeochemical feedbacks combine to a linear feedback term of 30 ± 27 PgCeq °C–1 (1 standard deviation range, 111 ± 98 Gt CO2- eq °C–1), including a feedback term of –11 [–18 to –5] PgCeq °C–1 (5–95% range, –40 [–62 to –18] Gt CO2- eq °C–1) from natural CH4 and N2O sources. The biogeochemical feedback from permafrost thaw leads to a combined linear feedback term of –21 ± 12 PgCeq °C–1 (1 standard deviation range –77 ± 44 Gt CO2- eq °C–1). For the integration of these feedbacks in the assessment of the remaining carbon budget (Section 5.5.2), two individual non-CO2 feedbacks (tropospheric ozone, and methane lifetime) are captured in the AR6-calibrated emulators (Box 7.1). Excluding those two contributions, the resulting combined linear feedback term for application in Section 5.5.2 is assessed at a reduction of 7 ± 27 PgCeq °C–1 (1 standard deviation range, –26 ± 97 PgCeq °C–1). For the same reasons as for the feedback terms expressed in W m–2°C–1 (see above), there is overall low confidence in the magnitude of these feedbacks.

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The assessment in Section 5.4 and Box 5.1 highlights the different nature, magnitude and uncertainties surrounding additional Earth system feedback. The remaining carbon budgets reported in Table 5.8 account for these feedbacks, including corrections due to permafrost CO2 and CH4 feedbacks as well as those due to aerosol and atmospheric chemistry (Section 5.4.8). Two of these additional feedbacks (tropospheric ozone and methane lifetime feedbacks) are included in the projections of non-CO2 warming carried out with AR6-calibrated emulators (Box 7.1). The remainder of these independent Earth system feedbacks combine to a feedback of about 7 ± 27 PgC K–1 (1-sigma range, or 26 ± 97 GtCO2 °C–1). Overall, Section 5.4.8 assessed there to be low confidence in the exact magnitude of these feedbacks and they represent identified additional amplifying factors that scale with additional warming, and mostly increase the challenge of limiting global warming to or below specific temperature levels.

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Gromov, S., C.A.M. Brenninkmeijer, and P. Jöckel, 2018: A very limited role of tropospheric chlorine as a sink of the greenhouse gas methane. Atmospheric Chemistry and Physics, 18(13), 9831–9843, doi: 10.5194/acp-18-9831-2018.

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Hossaini, R. et al., 2016: A global model of tropospheric chlorine chemistry: Organic versus inorganic sources and impact on methane oxidation. Journal of Geophysical Research: Atmospheres, 121(23), 14271–14297, doi: 10.1002/2016jd025756.

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Naik, V. et al., 2013: Preindustrial to present-day changes in tropospheric hydroxyl radical and methane lifetime from the Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP). Atmospheric Chemistry and Physics, 13(10), 5277–5298, doi: 10.5194/acp-13-5277-2013.

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Nicely, J.M. et al., 2018: Changes in global tropospheric OH expected as a result of climate change over the last several decades. Journal of Geophysical Research: Atmospheres, 123(18), 10774–10795, doi: 10.1029/2018jd028388.

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Stevenson, D.S. et al., 2020: Trends in global tropospheric hydroxyl radical and methane lifetime since 1850 from AerChemMIP. Atmospheric Chemistry and Physics, 20(21), 12905–12920, doi: 10.5194/acp-20-12905-2020.

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Strode, S.A. et al., 2020: Strong sensitivity of the isotopic composition of methane to the plausible range of tropospheric chlorine. Atmospheric Chemistry and Physics, 20(14), 8405–8419, doi: 10.5194/acp-20-8405-2020.

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Wang, X. et al., 2019: The role of chlorine in global tropospheric chemistry. Atmospheric Chemistry and Physics, 19(6), 3981–4003, doi: 10.5194/acp-19-3981-2019.

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Xia, L., P.J. Nowack, S. Tilmes, and A. Robock, 2017: Impacts of stratospheric sulfate geoengineering on tropospheric ozone. Atmospheric Chemistry and Physics, 17(19), 11913–11928, doi: 10.5194/acp-17-11913-2017.

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The AR5 concluded that, on interannual time scales, the radiative effects of volcanic aerosols are a dominant natural driver of climate variability, with the greatest effects occurring within the first 2–5 years following a strong eruption. Reconstructions of radiative forcing by volcanic aerosols used in the Paleoclimate Modelling Intercomparison Project Phase III (PMIP3) simulations and in AR5 featured short-lived perturbations of a range of magnitudes, with events of greater magnitude than –1 W m–2 (annual mean) occurring on average every 35–40 years, although no associated assessment of confidence was given. This section focuses on advances in reconstructions of stratospheric aerosol optical depth (SAOD), whereas (Chapter 7 focuses on the ERF of volcanic aerosols, and Chapter 5 assesses volcanic emissions of CO2 and CH4; tropospheric aerosols are discussed in Section 2.2.6. Cross-Chapter Box 4.1 undertakes an integrative assessment of volcanic effects including potential for 21st century effects.

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Advances in analysis of sulphate records from the Greenland Ice Sheet (GrIS) and AIS have resulted in improved dating and completeness of SAOD reconstructions over the past 2.5 kyr (Sigl et al., 2015), a more uncertain extension back to 10 ka (Kobashi et al., 2017; Toohey and Sigl, 2017), and a better differentiation of sulphates that reach high latitudes via stratospheric (strong eruptions) versus tropospheric pathways (A. Burke et al., 2019; Gautier et al., 2019). The PMIP4 volcanic reconstruction extends the period analysed in AR5 by 1 kyr (Figure 2.2c; Jungclaus et al., 2017) and features multiple strong events that were previously misdated, underestimated or not detected, particularly before about 1500 CE. The period between successive large volcanic eruptions (Negative ERF greater than –1 W m–2), ranges from 3–130 years, with an average of 43 ± 7.5 years between such eruptions over the past 2.5 kyr (data from Toohey and Sigl, 2017). The most recent such eruption was that of Mt Pinatubo in 1991. Century-long periods that lack such large eruptions occurred once every 400 years on average. Systematic uncertainties related to the scaling of sulphate abundance in glacier ice to radiative forcing have been estimated to be about 60% (Hegerl et al., 2006). Uncertainty in the timing of eruptions in the proxy record is ± 2 years (95% confidence interval) back to 1.5 ka and ± 4 years before (Toohey and Sigl, 2017).

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In summary, compared to the 1964–1980 average, stratospheric ozone columns outside polar regions (60°S–60°N) declined by about 2.5% over 1980–1995, and stabilized after 2000, with 2.2% lower values in 2014–2017. Large ozone depletions continue to appear in spring in the Antarctic and, in particularly cold years, also in the Arctic. Model-based estimates disagree on the sign of the ERF due to stratospheric ozone changes, but agree that it is much smaller in magnitude than that due to tropospheric ozone changes (Section 7.3.2.5).

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The AR5 assessed medium confidence in large-scale increases of tropospheric ozone at rural surface sites across the NH (1970–2010), and in a doubling of European surface ozone during the 20th century, with the increases of surface ozone in the SH being of low confidence. Surface ozone likely increased in East Asia, but levelled off or decreased in the eastern USA and western Europe. Free tropospheric trends (1971–2010) from ozonesondes and aircraft showed positive trends in most, but not all, assessed regions, and for most seasons and altitudes. This section focuses on large scale ozone changes; chemical and physical processes and regional changes in tropospheric ozone are assessed in Section 6.3.2.1 and Section 7.3.2.5 assesses radiative forcing.

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Prior to 1850 ozone observations do not exist, but a recent analysis using clumped-isotope composition of molecular oxygen (18O18O in O2) trapped in polar firn and ice, combined with atmospheric chemistry model simulations, constrains the global tropospheric ozone increase to less than 40% between 1850 and 2005, with most of this increase occurring between 1950 and 1980 (Yeung et al., 2019). Recently, the Tropospheric Ozone Assessment Report identified and evaluated 60 records of surface ozone observations collected at rural locations worldwide between 1896 and 1975, which were based on a range of measurement techniques with potentially large uncertainties (Tarasick et al., 2019). They found that from the mid-20th century (1930s to the early 1970s) to 1990–2014, rural surface ozone increased by 30–70% across the northern extra-tropics. This is smaller than the 100% 20th-century increase reported in AR5, which relied on far fewer measurement sites, all in Europe. In the northern tropics limited low-elevation historical data (1954–1975) provide no clear indication of surface ozone increases (Tarasick et al., 2019). However, similar to the northern mid-latitude increases, lower-free tropospheric ozone at Mauna Loa, Hawaii increased by approximately 50% from the late 1950s to present (Cooper et al., 2020). Historical observations are too limited to draw conclusions on surface ozone trends in the SH tropics and mid-latitudes since the mid-20th century, with tropospheric ozone exhibiting little change across Antarctica (Tarasick et al., 2019; Cooper et al., 2020). Based on reliable UV absorption measurements at remote locations (surface and lower troposphere), ozone trends since the mid-1990s varied spatially at northern mid-latitudes, but increased in the northern tropics (2–17%; 1–6 ppbv per decade; (Cooper et al., 2020; Gaudel et al., 2020). Across the SH these more recent observations are too limited to determine zonal trends (e.g., tropics, mid-latitudes, high latitudes).

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The earliest observations of free tropospheric ozone (1934–1955) are available from northern mid-latitudes where limited data indicate a tropospheric column ozone increase of 48 ± 30% up to 1990–2012 (Tarasick et al., 2019). Starting in the 1960s, records from ozonesondes show no significant changes in the free troposphere over the Arctic and mid-latitude regions of Canada, but trends are mainly positive elsewhere in the northern mid-latitudes (Oltmans et al., 2013; Cooper et al., 2020). Tropospheric column and free tropospheric trends since the mid-1990s based on commercial aircraft, ozonesonde observations and satellite retrievals (Figure 2.8b,c), are overwhelmingly positive across the northern mid-latitudes (2–7%; 1–4 ppbv per decade) and tropics (2–14%; 1–5 ppbv per decade), with the largest increases (8–14%; 3–6 ppbv per decade) in the northern tropics in the vicinity of southern Asia and Indonesia. Observations in the SH are limited, but indicate average tropospheric column ozone increases of 2–12% (1–5 ppbv) per decade in the tropics (Figure 2.8c), and weak tropospheric column ozone increases (<5%, <1 ppbv per decade) at mid-latitudes (Cooper et al., 2020). Above Antarctica, mid-tropospheric ozone has increased since the late 20th century (Oltmans et al., 2013). The total ozone ERF from 1750 to 2019 best estimate is assessed as 0.47 W m–2 (Section 7.3.2.5) and this is dominated by increases in the troposphere. The underlying modelled global tropospheric ozone column increase (Skeie et al., 2020) from 1850 to 2010 of 40–60%, is somewhat higher than the isotope based upper-limit of Yeung et al. (2019). At mid-latitudes (30°–60°N) model increases of 30–40% since the mid-20th century are broadly consistent with observations.

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In summary, limited available isotopicevidence constrains the global tropospheric ozone increase to less than 40% between 1850 and 2005 (low confidence). Based on sparse historical surface/low altitude data tropospheric ozone has increased since the mid-20th century by 30–70% across the NH (medium confidence). Surface/low altitude ozone trends since the mid-1990s are variable at northern mid-latitudes, but positive in the tropics [2 to 17% per decade] (high confidence). Since the mid-1990s, free tropospheric ozone has increased by 2–7% per decade in most regions of the northern mid-latitudes, and 2–12% per decade in the sampled regions of the northern and southern tropics (high confidence). Limited coverage by surface observations precludes identification of zonal trends in the SH, while observations of tropospheric column ozone indicate increases of less than 5% per decade at southern mid-latitudes (medium confidence).

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This section assesses the observed large-scale temporal evolution of tropospheric aerosols. Aerosol-related processes, chemical and physical properties, and links to air quality, are assessed in Chapter 6. An in-depth assessment of aerosol interactions with radiation and clouds is provided in Section 7.3.3.

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The AR5 reported that it was virtually certain that tropospheric temperatures have risen, and stratospheric temperatures fallen, since the mid-20th century, but that assessments of the rate of change and its vertical structure had only medium confidence in the NH extratropics and low confidence elsewhere. In particular there was low confidence in the vertical structure of temperature trends in the upper tropical troposphere.

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Considering instrumental records, there is considerable interdecadal variability in the strength of the WC, resulting in time-period dependent magnitude and even sign of trends (Carilli et al., 2015; Bordbar et al., 2017; Hou et al., 2018), with some studies reporting weakening over the 20th century (e.g., Power and Kociuba, 2011; Liu et al., 2019), while others reported strengthening (Z. Li et al., 2020), particularly over the last 30–40 years (e.g., Hu et al., 2013; L’Heureux et al., 2013; Yim et al., 2017). Based on estimation of changes in mid-tropospheric velocity from changes in observed cloud cover, Bellomo and Clement (2015) suggest a weakening and eastward shift of the WC over 1920–2010, however the robustness of this signal is questionable due to high uncertainty in the ship-reported cloud data used before 1954. Using centennial-scale 20CR reanalysis Tseng et al. (2019) showed that the vertical westerly wind shear over the western Pacific does not indicate any long-term change during 1900–1980, but shows a marked increase since the 1980s that is not present in ERA-Interim and JRA-55, again calling into question the ability of centennial-scale reanalyses to capture tropical circulation changes. Recent strengthening together with a westward shift of the WC (Bayr et al., 2014; Ma and Zhou, 2016) was identified across several reanalysis products and observational datasets, and using different metrics for quantifying WC. Nevertheless, satellite observations of precipitation and analyses of upper tropospheric humidity suggest substantially weaker strengthening of the WC than implied by reanalyses (Chung et al., 2019). This recent strengthening in the WC is associated with enhanced precipitation in the tropical western Pacific, anomalous westerlies in the upper troposphere, strengthened downwelling in the central and eastern tropical Pacific, and anomalous surface easterlies in the western and central tropical Pacific (Dong and Lu, 2013; McGregor et al., 2014; Choi et al., 2016). Positive trends in sea level pressure over the eastern Pacific and concurrent negative trends over the Indonesian region result in a pattern implying a shift towards a La Niña-like WC regime, with strengthening of the Pacific Trade Winds mainly over 1979–2012 (L’Heureux et al., 2013; England et al., 2014; Sohn et al., 2016; Zhao and Allen, 2019). Seasonal assessment of the WC showed significant changes in the vertical westerly wind shear over the Pacific during the austral summer and autumn implying a strengthening (Clem et al., 2017).

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A consistent poleward shift of the tropospheric extratropical jets since 1979 is reported by multiple reanalyses (Figure 2.18; Davis and Rosenlof, 2012; Davis and Birner, 2013; Pena-Ortiz et al., 2013; Manney and Hegglin, 2018), and radiosonde winds (Allen et al., 2012). This is generally consistent with the previously reported shifts retrieved from satellite temperature observations (Fu and Lin, 2011; Davis and Rosenlof, 2012). After the 1960s the magnitude of meridional shifts in extratropical jets over both the North Atlantic and North Pacific in August is enhanced compared to multi-century variability (Trouet et al., 2018). Despite some regional differences (Woollings et al., 2014; Norris et al., 2016; J. Wang et al., 2017a; Xue and Zhang, 2017; Ma and Zhang, 2018; Melamed-Turkish et al., 2018), overall poleward deflection of storm tracks in boreal winter over both the North Atlantic and the North Pacific was identified during 1979–2010 (Tilinina et al., 2013). Over the SH extra-tropics there is a similarly robust poleward shift in the polar jet since 1979 (Pena-Ortiz et al., 2013; Manney and Hegglin, 2018; WMO, 2018), although after 2000 the December–January–February (DJF) tendency to poleward shift of the SH jet stream position ceased (Banerjee et al., 2020). The general poleward movement in midlatitude jet streams (Lucas et al., 2014) is consistent with the expansion of the tropical circulation (Section 2.3.1.4.1). The changes of extratropical jets and westerlies are also related to the annular modes of variability (Section 2.4 and Annex IV).

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Allen, R.J., S.C. Sherwood, J.R. Norris, and C.S. Zender, 2012: Recent Northern Hemisphere tropical expansion primarily driven by black carbon and tropospheric ozone. Nature, 485, 350–354, doi: 10.1038/nature11097

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Gaudel, A. et al., 2020: Aircraft observations since the 1990s reveal increases of tropospheric ozone at multiple locations across the Northern Hemisphere. Science Advances, 6(34), eaba8272, doi: 10.1126/sciadv.aba8272.

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Manney, G.L. and M.I. Hegglin, 2018: Seasonal and regional variations of long-term changes in upper-tropospheric jets from reanalyses. Journal of Climate, 31(1), 423–448, doi: 10.1175/jcli-d-17-0303.1.

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Mears, C.A. and F.J. Wentz, 2017: A Satellite-Derived Lower-Tropospheric Atmospheric Temperature Dataset Using an Optimized Adjustment for Diurnal Effects. Journal of Climate, 30(19), 7695–7718, doi: 10.1175/jcli-d-16-0768.1.

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Oltmans, S.J. et al., 2013: Recent tropospheric ozone changes – A pattern dominated by slow or no growth. Atmospheric Environment, 67, 331–351, doi: 10.1016/j.atmosenv.2012.10.057.

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Shi, L., C.J. Schreck, and M. Schröder, 2018: Assessing the Pattern Differences between Satellite-Observed Upper Tropospheric Humidity and Total Column Water Vapor during Major El Niño Events. Remote Sensing, 10(1188), 1–15, doi: 10.3390/rs10081188.

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Tarasick, D. et al., 2019: Tropospheric Ozone Assessment Report: Tropospheric ozone from 1877 to 2016, observed levels, trends and uncertainties. Elementa: Science of the Anthropocene, 7(1), 39, doi: 10.1525/elementa.376.

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Yeung, L.Y. et al., 2019: Isotopic constraint on the twentieth-century increase in tropospheric ozone. Nature, 570(7760), 224–227, doi: 10.1038/s41586-019-1277-1.

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The AR5 assessed with low confidence that most, though not all, CMIP3 (Meehl et al., 2007) and CMIP5 (Taylor et al., 2012) models overestimated the observed warming trend in the tropical troposphere during the satellite period 1979–2012, and that a third to a half of this difference was due to an overestimate of the SST trend during this period (Flato et al., 2013). Since AR5, additional studies based on CMIP5 and CMIP6 models show that this warming bias in tropospheric temperatures remains. Recent studies have investigated the role of observational uncertainty, the model response to external forcings, the influence of the time period considered, and the role of biases in SST trends in contributing to this bias.

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Mitchell et al. (2013) and Mitchell et al. (2020) found a smaller discrepancy in tropical tropospheric temperature trends in models forced with observed SSTs (see also Figure 3.10a), and CMIP5 models and observations were found to be consistent below 150 hPa when viewed in terms of the ratio of temperature trends aloft to those at the surface (Mitchell et al., 2013). Flannaghan et al. (2014) and Tuel (2019) showed that most of the tropospheric temperature trend difference between CMIP5 models and the satellite-based observations over the 1970–2018 period is due to respective differences in SST warming trends in regions of deep convection, and Po-Chedley et al. (2021) showed that CMIP6 models with a more realistic SST simulation in the central and eastern Pacific show a better performance than other models. Though systematic biases still remain, this indicates that the bias in tropospheric temperature warming in models is in part linked to surface temperature warming biases, especially in the lower troposphere.

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The tropical tropospheric circulation features meridional and zonal overturning circulations, called Hadley and Walker circulations. In the zonal mean, the downwelling branch of the Hadley circulation cell is located in the subtropics and is often used as an indicator of the meridional extent of the tropics. In the equatorial zonal-vertical section, the major rising branch of the Walker circulation is located over the Maritime continent with secondary ascending regions over northern South America and Africa. The zonal component of the surface trade winds over most of the equatorial Pacific and Atlantic is associated with the Walker circulation. This section assesses the zonal-mean Hadley cell extent and the Pacific Walker circulation strength. Regional and water cycle aspects of these circulations are assessed in more detail in Section 8.3.2.

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Reproducing monsoons in terms of domain, precipitation amount, and timings of onset and retreat over the historical period also remains difficult. While CMIP5 historical simulations broadly capture global monsoon domains and intensity based on summer and winter precipitation differences, they underestimate the extent and intensity of East Asian and North American monsoons while overestimating them over the tropical western North Pacific (Lee and Wang, 2014; M. Yan et al., 2016). B. Wang et al. (2020) reported that CMIP6 models simulate the global monsoon domain and precipitation better (Figure 3.17a,b), albeit with biases in annual mean precipitation and the timings of onset and withdrawal of the Southern Hemisphere monsoon. Notable inter-model differences were identified in CMIP5, with the multi-model ensemble mean outperforming individual models (Lee and Wang, 2014). Common biases were identified across CMIP5 models in moist static energy and upper-tropospheric temperature associated with the South Asian summer monsoon, which may arise from overly smoothed model topography (Boos and Hurley, 2012). However, in atmospheric models with increasing resolution approaching 20 km, improvements in monsoon precipitation are not universal across regions and models, and overall improvements are unclear (Johnson et al., 2016; Ogata et al., 2017; L. Zhang et al., 2018b).

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CMIP5 models with a model top within the stratosphere seriously underestimate the amplitude of the variability of the wintertime NAM expression in the stratosphere, in contrast to CMIP5 models which extend well above the stratopause (Lee and Black, 2015). However, even in the latter models, the stratospheric NAM events, and their downward influence on the troposphere, are insufficiently persistent (Charlton-Perez et al., 2013; Lee and Black, 2015). Increased vertical resolution does not show any significant added value in reproducing the structure and magnitude of the tropospheric NAM (Lee and Black, 2013) nor in the NAO predictability as assessed in a seasonal prediction context with a multi-model approach (Butler et al., 2016). On the other hand, there is mounting evidence that a correct representation of the Quasi Biennal Oscillation, extratropical stratospheric dynamics (the polar vortex and sudden stratospheric warmings), and related troposphere-stratosphere coupling, as well as their interplay with ENSO, are important for NAO/NAM timing (Scaife et al., 2016; Karpechko et al., 2017; Domeisen, 2019; Domeisen et al., 2019), in spite of underestimated troposphere–stratosphere coupling found in models compared to observations (O’Reilly et al., 2019b).

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Additional research has shown that CMIP5 models reproduce the spatial structure of the SAM well, but tend to overestimate its variability in austral summer at interannual time scales, although this variability is within the observational uncertainty (Figure 3.33c,f,i; Zheng et al., 2013; Schenzinger and Osprey, 2015). This is related to the models’ tendency to simulate slightly more persistent SAM anomalies in summer compared to reanalyses (Schenzinger and Osprey, 2015; Bracegirdle et al., 2020). This may be due in part to too weak a negative feedback from tropospheric planetary waves (Simpson et al., 2013). CMIP6 models show improved performance in reproducing the spatial structure and interannual variance of the SAM in summer based on Lee et al. (2019) diagnostics (Figure 3.33i), with a better match of its trend with reanalyses over 1979–2014 (Figure 3.33l), more realistic persistence and improved positioning of the westerly jet, which in CMIP5 models on average is located too far equatorward (Bracegirdle et al., 2020; Grose et al., 2020). In CMIP5, it is also found that models which extend above the stratopause tend to simulate stronger summertime trends in the late 20th century than their counterparts with tops within the stratosphere (Rea et al., 2018; Son et al., 2018), though other differences between these sets of models, such as additional physical processes operating in the stratosphere or interactive ozone chemistry, may have also affected these results (Gillett et al., 2003a; Sigmond et al., 2008; Rea et al., 2018). At the surface, Ogawa et al. (2015) demonstrate with an atmospheric model the importance of sharp mid-latitude SST gradients for stratospheric ozone depletion to affect the SAM in summer. These studies imply that the well resolved stratosphere combined with finer ocean horizontal resolution has contributed to the stronger simulated trends in CMIP6 than in CMIP5.

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Fettweis, X. et al., 2013: Brief communication “Important role of the mid-tropospheric atmospheric circulation in the recent surface melt increase over the Greenland ice sheet”. The Cryosphere, 7(1), 241–248, doi: 10.5194/tc-7-241-2013.

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Kidston, J. et al., 2015: Stratospheric influence on tropospheric jet streams, storm tracks and surface weather. Nature Geoscience, 8(6), 433–440, doi: 10.1038/ngeo2424.

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McKitrick, R. and J. Christy, 2020: Pervasive Warming Bias in CMIP6 Tropospheric Layers. Earth and Space Science, 7(9), 1–8, doi: 10.1029/2020ea001281.

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Mitchell, D.M., P.W. Thorne, P.A. Stott, and L.J. Gray, 2013: Revisiting the controversial issue of tropical tropospheric temperature trends. Geophysical Research Letters, 40(11), 2801–2806, doi: 10.1002/grl.50465.

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Pallotta, G. and B.D. Santer, 2020: Multi-frequency analysis of simulated versus observed variability in tropospheric temperature. Journal of Climate, 33(23), 10383–10402, doi: 10.1175/jcli-d-20-0023.1.

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Po-Chedley, S. et al., 2021: Natural variability contributes to model–satellite differences in tropical tropospheric warming. Proceedings of the NationalAcademy of Sciences, 118(13), e2020962118, doi: 10.1073/pnas.2020962118.

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Santer, B.D. et al., 2014: Volcanic contribution to decadal changes in tropospheric temperature. Nature Geoscience, 7(3), 185–189, doi: 10.1038/ngeo2098.

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Santer, B.D. et al., 2017a: Causes of differences in model and satellite tropospheric warming rates. Nature Geoscience, 10(7), 478–485, doi: 10.1038/ngeo2973.

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Santer, B.D. et al., 2017b: Comparing tropospheric warming in climate models and satellite data. Journal of Climate, 30(1), 373–392, doi: 10.1175/jcli-d-16-0333.1.

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Santer, B.D. et al., 2018: Human influence on the seasonal cycle of tropospheric temperature. Science, 361(6399), eaas8806, doi: 10.1126/science.aas8806.

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Sigmond, M., J.F. Scinocca, and P.J. Kushner, 2008: Impact of the stratosphere on tropospheric climate change. Geophysical Research Letters, 35(12), L12706, doi: 10.1029/2008gl033573.

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Son, S.-W. et al., 2018: Tropospheric jet response to Antarctic ozone depletion: An update with Chemistry-Climate Model Initiative (CCMI) models. Environmental Research Letters, 13(5), 054024, doi: 10.1088/1748-9326/aabf21.

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Suárez-Gutiérrez, L., C. Li, P.W. Thorne, and J. Marotzke, 2017: Internal variability in simulated and observed tropical tropospheric temperature trends. Geophysical Research Letters, 44(11), 5709–5719, doi: 10.1002/2017gl073798.

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Takahashi, H., H. Su, and J.H. Jiang, 2016: Error analysis of upper tropospheric water vapor in CMIP5 models using “A-Train” satellite observations and reanalysis data. Climate Dynamics, 46(9–10), 2787–2803, doi: 10.1007/s00382-015-2732-9.

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Tian, B. et al., 2013: Evaluating CMIP5 models using AIRS tropospheric air temperature and specific humidity climatology. Journal of Geophysical Research: Atmospheres, 118(1), 114–134, doi: 10.1029/2012jd018607.

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Tuel, A., 2019: Explaining differences between recent model and satellite tropospheric warming rates with tropical SSTs. Geophysical Research Letters, 46, 9023–9030, doi: 10.1029/2019gl083994.

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A model experiment based on the SSP3-7.0 scenario with aerosols, their precursors, and non-methane tropospheric ozone precursors set to SSP1-1.9 abundances (SSP3-7.0-lowSLCF-highCH4; Collins et al., 2017) shows a projected multi-model mean GSAT anomaly that is higher by 0.22°C at mid-century (2045-2054) compared to SSP3-7.0 (Figure 4.18; Allen et al., 2020), but this difference is smaller than the inter-model spread of the SSP3-7.0 projections based on the CMIP6 models. Note the SSP3-7.0-lowSLCF-highCH4 experiment does not perturb methane from SSP3-7.0 concentrations. A modified SSP3-7.0-lowSLCF-lowCH4 scenario that also includes methane mitigation shows a lower GSAT by mid-century compared to SSP3-7.0 (Allen et al., 2021).

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There remains substantial uncertainty in the magnitude of projected Arctic amplification (Smith et al., 2020), with the Arctic warming ranging from two to four times the global average in models (Holland and Bitz, 2003; Nummelin et al., 2017). This uncertainty primarily stems from different representations of polar surface-albedo, lapse-rate, and cloud feedbacks, and from different projected poleward energy transport changes (Holland and Bitz, 2003; Crook et al., 2011; Mahlstein and Knutti, 2011; Pithan and Mauritsen, 2014; Bonan et al., 2018). The magnitude of Arctic amplification may also depend on the mix of radiative forcing agents (Najafi et al., 2015; Sand et al., 2016; Stjern et al., 2019) such as the contribution of ozone depleting substances (Polvani et al., 2020). Tropospheric aerosol emissions tend to reduce simulated Arctic warming over the middle of the 20th century (Gagné et al., 2017b) and consequently aerosol emission reductions in observations and SSP scenarios enhance simulated Arctic warming over recent and future decades (Section 6.4.3; Gagné et al., 2015; Acosta Navarro et al., 2016; Wobus et al., 2016; Wang et al., 2018).

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Section 12.4.3.2 of AR5 assessed that there is high confidence in the overall pattern of projected end of 21st century tropospheric temperature change and that it is very likely that some of the largest warming will occur in the northern high latitudes. They further assessed that proportionately larger warming is likely to occur in the tropical upper troposphere than at the tropical surface, but with medium confidence owing to the relatively large observational uncertainties and contradictory analyses regarding model accuracy in simulating tropical upper tropospheric temperature trends.

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CMIP6 projections show warming throughout the troposphere by the end of this century and a mix of warming and cooling in the stratosphere depending on the emissions scenario (Figure 4.22). The patterns of tropospheric temperature change are highly consistent with those derived from earlier generations of climate models as assessed in AR5, AR4 and TAR. In SSP1-2.6, the multi-model mean warming remains below 3°C everywhere in the troposphere except near the surface in the Arctic; this is similar to the findings in AR5 based on CMIP5 models for RCP2.6. In SSP3-7.0, the zonal mean tropospheric warming is also largest in the tropical upper troposphere, reaching more than 5°C, and near the surface in the Arctic where warming exceeds 8°C (Figure 4.22). It is likely that the warmer projected GSAT in the unconstrained CMIP6 model ensemble contributes to larger warming in the tropical upper troposphere and in the Arctic lower troposphere. This assessment is based on the understanding of polar amplification assessed in Chapter 7 (Section 7.4.4.1), and at low latitudes is based on the understanding of moist convective processes as well as the relationship between CMIP5- and CMIP6-simulated surface temperatures and tropical upper tropospheric warming over the historical period (Section 3.3.1.2).

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In summary, new results since AR5 do not generally alter the understanding of projected zonal mean atmospheric temperature changes. There is high confidence in the overall pattern of projected tropospheric temperature changes given its robustness across many generations of climate models. It is furthervery likely that projected long-term tropospheric warming will be larger than the global mean in the Arctic lower troposphere. It is likely that tropical upper tropospheric warming will be larger than at the tropical surface, however with an uncertain magnitude owing to the potentially large role of natural internal variability and differences across models in the simulated free tropospheric temperature response to a given forcing scenario (Section 3.3.1.2). It is very likely that global mean stratospheric cooling will be larger by the end of the 21st century in a pathway with higher atmospheric CO2 concentrations.

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Since AR5, progress has been achieved in understanding changes in patterns and rates of precipitation with GSAT rise. The projected precipitation changes can be decomposed into a part that is related to atmospheric circulation referred to as dynamical component and a part related to water vapour changes, the thermodynamic component. Based on process understanding and modelling (Fläschner et al., 2016; Samset et al., 2016), global mean precipitation will very likely increase by 1–3% per °C of GSAT warming (Section 8.2.1). The increase in atmospheric water vapour is a robust change under global warming, the sensitivity of global precipitation change to warming is smaller (2% per °C) as compared to water vapour change (7% per °C; Held and Soden, 2006). Global energy balance places a strong constraint on the global mean precipitation (Allen and Ingram, 2002; Pendergrass and Hartmann, 2014; Myhre et al., 2018; Siler et al., 2019). Tropospheric radiative cooling constrains global precipitation (Pendergrass and Hartmann, 2014), leading to a slow SST-dependent response and a forcing-dependent rapid adjustment. Rapid adjustments account for large regional differences in hydrological sensitivity across multiple drivers (Samset et al., 2016; Myhre et al., 2017). The rapid regional precipitation response to increased CO2 is robust across models, implying that the uncertainty in long-term changes is mainly associated with the response to SST-mediated feedbacks (Richardson et al., 2016). Precipitation response to fast adjustments and slow temperature-driven responses are assessed in detail in Chapter 8 (Section 8.2.1).

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Under both SSP1-2.6 and SSP3-7.0 there is a strengthening and lifting of the subtropical jets in both hemispheres (Figure 4.26), consistent with the response to large-scale tropospheric warming found in earlier generations of climate models (Collins et al., 2013). In the SH, GHG emissions tend to force a poleward shift of the jet, but this is opposed, particularly in austral summer, by the stratospheric ozone hole recovery (Barnes and Polvani, 2013; Barnes et al., 2014; Bracegirdle et al., 2020b). Consistent with sea level pressure changes, CMIP6 models project a strengthening and poleward shift of the SH jet in austral summer and winter under SSP3-7.0, but smaller and non-robust changes in SH mid-latitude zonal winds under SSP1-2.6 (Figure 4.26; see also Section 4.5.3.1). CMIP6 models show an improved simulation of the SH jet stream latitude (Bracegirdle et al., 2020a; Curtis et al., 2020). This has been linked to a reduction in the projected poleward shift of the SH jet in austral summer compared to the CMIP5 models (Curtis et al., 2020; Goyal et al., 2021), although differences in the pattern of SST response may also play a role (Wood et al., 2020). In the NH extratropics, the changes in lower-tropospheric zonal-mean zonal winds by the end of the century are generally smaller than in the SH. In boreal winter, there is a weak poleward shift of the NH zonal-mean westerly jet maximum in SSP3-7.0.

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Progress since AR5 has improved understanding of the climate change aspects that can drive these different, and potentially opposite, responses in the mid-latitude jets and storm tracks. A poleward shift of the jets and storm tracks is expected in response to an increase in the atmospheric stratification and in the upper-tropospheric equator-to-pole meridional temperature gradient, while it is opposed by the decrease in the meridional temperature gradient in the lower troposphere associated with the polar amplification of global warming (Harvey et al., 2014; Shaw et al., 2016). Recent analyses have identified additional climate aspects that can drive mid-latitude jet changes, including patterns in sea surface warming (Mizuta et al., 2014; Langenbrunner et al., 2015; Ceppi et al., 2018; Wood et al., 2020), land–sea warming contrast (Shaw and Voigt, 2015), loss of sea ice (Deser et al., 2015; Harvey et al., 2015; Screen et al., 2018b; Zappa et al., 2018), and changes in the strength of the stratospheric polar vortex (Manzini et al., 2014; Grise and Polvani, 2017; Simpson et al., 2018; Ceppi and Shepherd, 2019). From an energetics perspective,the uncertainty in the response of the jet streams depends on the response of clouds, their non-spatially uniform radiative feedbacks shaping the meridional profile of warming (Ceppi et al., 2014; Voigt and Shaw, 2015, 2016; Ceppi and Hartmann, 2016; Ceppi and Shepherd, 2017; Lipat et al., 2018; Albern et al., 2019; Voigt et al., 2019). Climate models seem to underestimate the forced component of the year-to-year variability in the atmospheric circulation, particularly in the North Atlantic sector (Scaife and Smith, 2018), which suggests some relevant dynamical processes may not be well represented. Whether and how this may affect long-term projections is unknown. In conclusion, due to the influence from competing dynamical drivers and the absence of observational evidence, there is medium confidence in a projected poleward shift of the NH zonal-mean low-level westerlies in autumn and summer and low confidence in the other seasons. There is also overall low confidence in projected regional changes in the NH low-level westerlies, particularly for the North Atlantic basin in boreal winter.

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As stated in AR5, the number of extratropical cyclones (ETC) composing the storm tracks is projected to weakly decline in future projections, but by no more than a few percent change. The reduction is mostly located on the equatorward flank of the storm tracks, which is associated with the Hadley cell expansion and a poleward shift in the mean genesis latitude of ETCs (Tamarin-Brodsky and Kaspi, 2017). Furthermore, the poleward propagation of individual ETCs is expected to increase with warming (Graff and LaCasce, 2014; Tamarin-Brodsky and Kaspi, 2017), thus contributing to a poleward shift in the mid-latitude transient-eddy kinetic energy. The increased poleward propagation results from the strengthening of the upper tropospheric jet and increased cyclone-associated precipitation (Tamarin-Brodsky and Kaspi, 2017), which are robust aspects of climate change.

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Suárez-Gutiérrez, L., C. Li, P.W. Thorne, and J. Marotzke, 2017: Internal variability in simulated and observed tropical tropospheric temperature trends. Geophysical Research Letters, 44, 5709–5719, doi: 10.1002/2017gl073798.

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Visioni, D., G. Pitari, G. di Genova, S. Tilmes, and I. Cionni, 2018: Upper tropospheric ice sensitivity to sulfate geoengineering. Atmospheric Chemistry and Physics, 18(20), 14867–14887, doi: 10.5194/acp-18-14867-2018.

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Xia, L., P.J. Nowack, S. Tilmes, and A. Robock, 2017: Impacts of stratospheric sulfate geoengineering on tropospheric ozone. Atmospheric Chemistry and Physics, 17(19), 11913–11928, doi: 10.5194/acp-17-11913-2017.

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Regarding future climate, it is important to note that mid-latitude variability is also affected by many drivers other than the Arctic changes and that those drivers as well as the linkages to mid-latitude variability might change in a warmer world. The AMV, PDV, ENSO (see Annex IV), upper tropospheric tropical heating, polar stratospheric vortex, and land surface processes associated with soil moisture (Miralles et al., 2014; Hauser et al., 2016) and snow cover (Nakamura et al., 2019; Sato and Nakamura, 2019) are a few examples. A considerable body of literature has shown that changes to the NAO/AO on seasonal and climate change time scales can be driven by variations in the wavelength and amplitude of Rossby waves, mainly of tropical origin (Fletcher and Kushner, 2011; Cattiaux and Cassou, 2013; Ding et al., 2014; Goss et al., 2016). The influence of future Arctic warming on mid-latitude circulation is difficult to disentangle from the effect of such a plethora of drivers (Blackport and Kushner, 2017; F. Li et al., 2018). One of the consequences of climate change is a poleward shift of the jet induced by the tropical warming (Barnes and Polvani, 2013), which is less obvious in winter especially over the North Atlantic (Peings et al., 2018; Oudar et al., 2020), and the increase of the meridional temperaturegradient in the upper troposphere, which increases storm track activity (Barnes and Screen, 2015; Parding et al., 2019). Although climate models indicate that future Arctic warming and the associated equator–pole temperature gradient decrease could affect mid-latitude climate and variability (Haarsma et al., 2013a; McCusker et al., 2017; Zappa et al., 2018), and even the tropics and subtropics (Deser et al., 2015; Cvijanovic et al., 2017; K. Wang et al., 2018; England et al., 2020; Kennel and Yulaeva, 2020), they do not reveal a strong influence on extreme weather (Woollings et al., 2014).

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Langenbrunner and Neelin (2013) show that there is little improvement in CMIP5 relative to CMIP3 in amplitude and spatial patterns of the ENSO influence on boreal winter precipitation (spatial pattern correlations against observations are typically less than 0.5). However, the CMIP5 ensemble accurately represents the amplitude of the precipitation response in regions where observed teleconnections are strong. Garcia-Villada et al. (2020) found a decline in performance of the representation of simulated ENSO teleconnection patterns for model experiments with fewer observational constraints. They also show that ENSO warm phase (El Niño) teleconnections are better represented than those for the cold phase (La Niña). Individual CMIP5 and CMIP6 models show a good ability to represent the observed teleconnections at aggregated spatial scales (Power and Delage, 2018; Section 3.7.3 and Figure 3.38). The evaluation of the atmospheric dynamical linkages is also an important part of the assessment. Hurwitz et al. (2014) showed that CMIP5 models broadly simulate the expected (as seen in the MERRA reanalysis) upper-tropospheric responses to central equatorial Pacific or eastern equatorial Pacific ENSO events in boreal autumn and winter. CMIP5 models also simulate the correct sign of the Arctic stratospheric response, consisting of polar vortex weakening during eastern and central Pacific Niño events and vortex strengthening during both types of La Niña events. In contrast, most CMIP5 models do not capture the observed weakening of the Southern Hemisphere polar vortex in response to central Pacific ENSO events (Brown et al., 2013).

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The influence of SST anomalies on south-eastern South America precipitation have been studied extensively on interannual to multi-decadal time scales (Paegle and Mo, 2002). The positive phase of El Niño–Southern Oscillation (ENSO; Annex IV.2.3) is related to stronger mean and extreme rainfall over south-eastern South America (Ropelewski and Halpert, 1987; Grimm and Tedeschi, 2009; Robledo et al., 2016). The ENSO influence may be modulated by the PDV (Kayano and Andreoli, 2007; Fernandes and Rodrigues, 2018) and the AMV (Kayano and Capistrano, 2014). PDV and AMV also influence the south-eastern South American climate independently of ENSO (Barreiro et al., 2014; Grimm and Saboia, 2015; Robledo et al., 2020). While Pacific SSTs dominate the overall influence of oceanic variability in the region, the Atlantic variability seems to dominate on multi-decadal time scales and has been proposed as a driver for the long-term positive trend (Seager et al., 2010; Barreiro et al., 2014). Based on experiments designed to test how south-eastern South America precipitation is modulated by tropical Atlantic SSTs, Seager et al. (2010) showed that cold anomalies in the tropical Atlantic favour wetter conditions by inducing an upper-tropospheric flow towards the equator, which, via advection of vorticity, leads to ascending motion over south-eastern South America (Figure 10.12a). Monerie et al. (2019) supported this argument showing a negative relationship between south-eastern South America precipitation and the AMV index (Huang et al., 2015) using an AGCM coupled to an ocean mixed-layer model with nudged SSTs.

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Physical climate storylines are self-consistent and plausible unfolding of a physical trajectory of the climate system, or a weather or climate event, on time scales from hours to multiple decades (Section 1.4.4.2). Storylines that condition climatic features and processes on a set of plausible but distinct large-scale climatic changes enables the exploration of uncertainties in regional climate projections (Box 10.2, Figure 1 and Section 10.3.4.2). For instance, Zappa and Shepherd (2017) condition projected changes in European surface wind speeds on different plausible projections of tropical upper tropospheric warming and the polar vortex strength in the CMIP5 multi-model ensemble. Storylines of specific events are generated to explore the unfolding and impacts of comparable events in counterfactual climates (Lackmann, 2015; Meredith et al., 2015b; Takayabu et al., 2015; Hegdahl et al., 2020; Sillmann et al., 2021). Those event storylines can be based on pseudo-global warming studies (Lackmann, 2015; Meredith et al., 2015b; Takayabu et al., 2015; see Section 10.3.2.2), selected and possibly downscaled events from long-term climate projections (Hegdahl et al., 2020; Huang et al., 2020a), or based on expert judgment of plausible changes to observed events (Pisaric et al., 2011; Dessai et al., 2018). They can be used for attributing events to different causal factors (Lackmann, 2015; Meredith et al., 2015b; Takayabu et al., 2015; Trenberth et al., 2015; Shepherd, 2016a; Section 11.2.4) as well as for exploring the unfolding of events in future climates.

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In long-term projections, robust signals consist of a weakened upper-tropospheric meridional temperature gradient, either due to upper-level heating over the tropical Pacific (Sooraj et al., 2015) or Indian oceans (Sabeerali and Ajayamohan, 2018) in CMIP5, and increased seasonal mean rainfall, including in CMIP6 (Almazroui et al., 2020b; B. Wang et al., 2021). The weakened temperature gradient combines with increased atmospheric stability to weaken the monsoon overturning circulation, with some findings showing northward movement of the lower-tropospheric monsoon winds in response to a stronger land–sea temperature contrast in CMIP3 and CMIP5 (Sandeep and Ajayamohan, 2015; Endo et al., 2018). The northward shift was also found in the genesis of synoptic systems (monsoon depressions) in a single high-resolution AGCM forced by an ensemble of SSTs derived from four GCMs under the RCP8.5 scenario (Sandeep et al., 2018).

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Decomposition of the increased rainfall signal showed that while the dynamic component led to a drying tendency, this was overcome by the thermodynamic contribution (Sooraj et al., 2015; Z. Chen et al., 2020). Alternative decomposition experiments using AGCMs and their coupled counterparts found increases in the lower-tropospheric temperature gradient and monsoon rainfall to be dominated by the fast radiative response to GHG increase rather than SST changes (Li and Ting, 2017; Endo et al., 2018). The response to SST forcing featured a large model spread, particularly arising from the dynamic component (Li and Ting, 2017). Chen and Zhou (2015) found that the Indo-Pacific SST warming pattern dominated the uncertainty in Indian monsoon rainfall change. Finally, in assessing the relative impact of CO2 radiative forcing and plant physiological changes in quadrupled CO2 experiments in four Earth system models, Cui et al. (2020) showed little impact of plant physiology on annual rainfall for the Indian region.

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Increased warming over land compared to the sea is expected due to the lapse-rate changes associated with tropospheric moisture contrasts (Kröner et al., 2017; Byrne and O’Gorman, 2018; Brogli et al., 2019b; Figure 10.20a). Enhanced land–sea temperature contrast leads to relative humidity and soil moisture feedbacks (Rowell and Jones, 2006), the latter also depending on weather regimes (Quesada et al., 2012). The globally enhanced land–sea contrast in near surface temperature is also a robust result in CMIP5 and CMIP6 models (Section 4.5.1.1).

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An increase in the concentration of greenhouse gases in the atmosphere leads to the warming of tropospheric air and the Earth’s surface. This direct thermodynamic effect leads to warmer temperatures everywhere, with an increase in the frequency and intensity of warm extremes, and a decrease in the frequency and intensity of cold extremes. The initial increase in temperature leads to other thermodynamic responses and feedbacks affecting the atmosphere and the surface. These include an increase in the water vapour content of the atmosphere (water vapour feedback, see Section 7.4.2.2) and a change in the vertical profile of temperature (lapse rate feedback, see Section 7.4.2.2). While the water vapour feedback always amplifies the initial temperature increases (positive feedback), the lapse rate feedback amplifies near-surface temperature increases (positive feedback) in mid- and high latitudes but reduces temperature increases (negative feedback) in tropical regions (Pithan and Mauritsen, 2014).

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Changes in SST and atmospheric temperature and moisture play a role in tropical cyclogenesis (Walsh et al., 2015). Reductions in vertical convective mass flux due to increased tropical stability have been associated with a reduction in cyclogenesis (Held and Zhao, 2011; Sugi et al., 2012). Satoh et al. (2015) further posit that the robust simulated increase in the number of intense TCs, and hence increased vertical mass flux associated with intense TCs, must lead to a decrease in overall TC frequency because of this association. The Genesis Potential Index can be modified to mimic the TC frequency decreases of a model by altering the treatment of humidity (Camargo et al., 2014). This supports the idea that increased mid-tropospheric saturation deficit (Emanuel et al., 2008) controls TC frequency, but the approach remains empirical. Other possible controlling factors, such as a decline in the number of seeds (held constant in Emanuel’s downscaling approach, or dependent on the genesis index formulation in the approach proposed by C.-Y. Lee et al., 2020) caused by increased atmospheric stability have been proposed, but questioned as an important factor (Patricola et al., 2018). The resolution of atmospheric models affects the number of seeds, hence TC genesis frequency (Vecchi et al., 2019; Sugi et al., 2020; Yamada et al., 2021). The diverse and sometimes inconsistent projected changes in global TC frequency by high-resolution models indicate that better process understanding and improvement of the models are needed to raise confidence in these changes.

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As reported in AR5 and in Section 8.4.2.8, despite small changes in the dynamical intensity of ETCs, there is high confidence that the precipitation associated with ETCs will increase in the future (Zappa et al., 2013b; Marciano et al., 2015; Pepler et al., 2016; Michaelis et al., 2017; Yettella and Kay, 2017; Zhang and Colle, 2017; Barcikowska et al., 2018; Hawcroft et al., 2018; Zarzycki, 2018; Kodama et al., 2019; Bevacqua et al., 2020a; Reboita et al., 2021). There is high confidence that increases in precipitation will follow increases in low-level water vapour (i.e., about 7% per 1°C of surface warming; see Box 11.1) and will be larger for higher warming levels (Zhang and Colle, 2017). There is medium confidence that precipitation changes will show regional and seasonal differences due to distinct changes in atmospheric humidity and dynamical conditions (Zappa et al., 2015; Hawcroft et al., 2018), with decreases in some specific regions such as the Mediterranean (Zappa et al., 2015; Barcikowska et al., 2018). There is high confidence that snowfall associated with winter ETCs will decrease in the future, because increases in tropospheric temperatures lead to a lower proportion of precipitation falling as snow (O’Gorman, 2014; Rhoades et al., 2018; Zarzycki, 2018). However, there is medium confidence that extreme snowfall events associated with winter ETCs will change little in regions where snowfall will be supported in the future (O’Gorman, 2014; Zarzycki, 2018).

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Severe convective storms occur under conditions preferable for deep convection, that is, conditionally unstable stratification, sufficient moisture, both in lower and middle levels of the atmosphere, and a strong vertical shear. These large-scale environmental conditions are viewed as necessary conditions for the occurrence of severe convective systems, or the resulting tornadoes and lightning, and the relevance of these factors strongly depends on the region (e.g., Antonescu et al., 2016a; Allen, 2018; Tochimoto and Niino, 2018). Frequently used metrics are atmospheric static stability, moisture content, convective available potential energy (CAPE) and convective inhibition, wind shear or helicity, including storm-relative environmental helicity (Tochimoto and Niino, 2018; Elsner et al., 2019). These metrics, largely controlled by large-scale atmospheric circulations or synoptic weather systems, such as TCs and ETCs, are then generally used to examine severe convective systems. In particular, there is high confidence that CAPE in the tropics and the subtropics increases in response to global warming (M.S. Singh et al., 2017), as supported by theoretical studies (Singh and O’Gorman, 2013; Seeley and Romps, 2015; Romps, 2016; Agard and Emanuel, 2017). The uncertainty, however, arises from the balance between factors affecting severe storm occurrence. For example, the warming of mid-tropospheric temperatures leads to an increase in the freezing level, which leads to increased melting of smaller hailstones, while there may be some offset by stronger updrafts driven by increasing CAPE, which would favour the growth of larger hailstones, leading to less melting when falling (Allen, 2018; Mahoney, 2020).

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Yokoyama, C., H. Tsuji, and Y.N. Takayabu, 2020: The Effects of an Upper-Tropospheric Trough on the Heavy Rainfall Event in July 2018 over Japan. Journal of the Meteorological Society of Japan. Series II, 98(1), 235–255, doi: 10.2151/jmsj.2020-013.

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Since AR5, weakening of the tropical circulation has been explained as a rapid response to increasing CO2 concentrations and slower response to warming and evolving SST patterns (He and Soden, 2017; Xia and Huang, 2017; Shaw and Tan, 2018; Chemke and Polvani, 2020). Large-scale tropical circulation weakens by 34% in a rapid response to a quadrupling of CO2 concentrations (Plesca et al., 2018), which suppresses tropospheric radiative cooling, particularly in subtropical ocean subsidence regions (Bony et al., 2013; Merlis, 2015; Richardson et al., 2016). The resulting increased atmospheric stability explains the rapid weakening of the Walker circulation (Wills et al., 2017) and Northern Hemisphere Hadley Cell (Chemke and Polvani, 2020). Subsequent surface warming contributes up to a 12% slowing of circulation for a uniform 4°C SST increase, driven by thermodynamic decreases in temperature lapse rate (Plesca et al., 2018).

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Since AR5, understanding of poleward expansion of the Hadley Cells has improved (Section 2.3.1.4.1) but its role in subtropical drying is limited to the zonal mean and dominated by ocean regions (Byrne and O’Gorman, 2015; Grise and Polvani, 2016; He and Soden, 2017; Schmidt and Grise, 2017; Siler et al., 2018; Chemke and Polvani, 2019; Grise and Davis, 2020). Over subtropical land, evolving SST patterns and land–ocean warming contrasts, that are partly explained by rapid responses to CO2 increases, can dominate aspects of the atmospheric circulation response (Byrne and O’Gorman, 2015; He and Soden, 2015; Chadwick et al., 2017; H. Yang et al., 2020) and resultant regional water cycle changes, particularly for projected drying in semi-arid, winter-rainfall dominated subtropical climates (Deitch et al. , 2017; Brogli et al. , 2019; Seager et al. , 2019b; Zappa et al. , 2020). Poleward expansion of the tropical belt is expected to drive a corresponding shift in mid-latitude storm tracks, but the controlling mechanisms differ between hemispheres. Southern Hemisphere expansion is driven by GHG forcing and amplified by stratospheric ozone depletion, while weaker Northern Hemisphere expansion in response to GHG forcing is modulated by tropospheric ozone and aerosol forcing, particularly black carbon (Davis et al. , 2016; Grise et al. , 2019; Watt-Meyer et al. , 2019; Zhao et al. , 2020). However, internal variability is found to dominate observed responses in the NH, precluding attribution to radiative forcing (D’Agostino et al., 2020a). Paleoclimate evidence of poleward expansion and weakening of westerly winds in both hemispheres in the warmer Pliocene is linked to reduced equator-to-pole thermal gradients and ice volume(Abell et al., 2021).

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The AR5 presented evidence of increases in global near-surface and tropospheric specific humidity since the 1970s but with medium confidence of a slowing of near-surface moistening trends over land associated with reduced relative humidity since the late 1990s. According to AR5, radiosonde, Global Positioning System (GPS) and satellite observations of tropospheric water vapour indicate very likely increases at near global scales since the 1970s occurring at a rate that is generally consistent with the Clausius–Clapeyron relation (about 7% °C–1 at low altitudes) and the observed atmospheric warming (Hartmann et al., 2013).

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Since AR5, it is very likely that increases in global atmospheric water vapour were observed based on in situ, satellite and reanalysis data (with medium confidence in the magnitude; Section 2.3.1.3). Satellite records show increases in upper tropospheric water vapour (constant relative humidity while temperatures have increased) since 1979 (E.-S. Chung et al. , 2014; Blunden and Arndt, 2020), to which human influence has likely contributed (Section 3.3.2.2). Combined satellite and reanalysis estimates and CMIP6 atmosphere-only simulations (1988–2014) show global mean precipitable water vapour increases of 6.7 ± 0.3 % °C–1, very close to the Clausius–Clapeyron rate (Allan et al., 2020). Satellite-based products show increases close to the Clausius–Clapeyron rate over the ice-free oceans (about 7 to 9 % °C–1; 19982008), but reanalysis estimates outside this range (Schröder et al., 2019) are an expected consequence of their changing observing systems (Allan et al., 2014; Parracho et al., 2018). Increases in precipitable water vapour are found over the central and sub-Arctic based on multiple reanalyses with some corroboration from sparse, in situ data (Vihma et al., 2016; Rinke et al., 2019; Nygård et al., 2020).

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There has been considerable work on linkages (teleconnections) between Arctic warming and the mid-latitude circulation (see also Cross-Chapter Box 10.1). The limited amount of research on Southern Hemisphere (SH) stationary waves suggests changes in high-latitude, mid-tropospheric stationary waves which influence Antarctic precipitation (Turner et al., 2017) and changes in stratospheric stationary waves that are associated with ozone depletion rather than increases in GHGs (L. Wang et al., 2013). The observed climatology of NH winter stationary waves is well-represented in the CMIP5 multi-model mean (Wills et al., 2019) but individual models have important deficiencies in reproducing stationary wave variability (Lee and Black, 2013). In the SH, the observed climatology of stationary waves in CMIP5 models has considerable bias in both phase and amplitude (Garfinkel et al., 2020). A comprehensive assessment is not yet available for CMIP6 models.

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Currently no consensus exists on observed trends in blocking during 19792013. (Horton et al., 2015) identified increasing trends in anticyclonic circulation regimes based on geopotential height fields in the mid-troposphere, which may be partly related to the tropospheric warming itself and thus not represent real changes in the statistics of weather (Horton et al., 2015; Woollings et al., 2018). Hanna et al. (2018) and (Davini and D’Andrea, 2020) reported a significant increase in the frequency of summer blocking over Greenland. A weakening of the zonal wind, eddy kinetic energy and amplitude of Rossby waves in summer in the NH (Coumou et al., 2015, Kornhuber et al., 2019) and an increased ‘waviness’ of the jet stream associated with Arctic warming (Francis and Vavrus, 2015; Pfahl et al., 2015; Luo et al., 2019) have also been identified, which may be linked to increased blocking.

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Simulated changes in MJO precipitation amplitude are extremely sensitive to the pattern of SST warming (Takahashi et al., 2011; Maloney and Xie, 2013; Arnold et al., 2015) and oceanatmosphere coupling (DeMott et al., 2019; Klingaman and Demott, 2020). In agreement with results from previous model generations, most CMIP5 models still underestimate MJO amplitude, and struggle to generate a coherent eastward propagation of precipitation and wind (Hung et al., 2013; Jiang et al., 2015; Ahn et al., 2017), affecting regional surface climate in the tropics and extratropics. In addition, most CMIP5 models simulate an MJO that propagates faster compared with observations, with a poorly represented intra-seasonal precipitation variability (Ahn et al., 2017). Over the Indian Ocean, the propagation speed of convection in some CMIP5 models tends to be slower than observed due to a strong persistence of equatorial precipitation (Hung et al., 2013; Jiang et al., 2015). Among other processes, improving the moisture-convection coupling, the representation of moist convection, the interaction between lower tropospheric heating and boundary layer convergence, and the topography of the Maritime Continent improve simulations of the MJO (Ahn et al. , 2017, 2020a; Kim and Maloney, 2017; Yang and Wang, 2019; H. Tan et al. , 2020; Y.-M. Yang et al. , 2020). In fact, CMIP6 models reproduce the amplitude and propagation of the MJO better than CMIP5 models due to increased horizontal moisture advection over the Maritime Continent (Ahn et al., 2020b). Despite the diverse theories of MJO evolution and processes that have been developed since its discovery, a better understanding of its dynamics is still needed (Jiang et al., 2020; Zhang et al., 2020). Furthermore, metrics based on dynamical processes are needed to assess model simulations of these events (Stechmann and Hottovy, 2017; B. Wang et al., 2018) as well as related teleconnections (J. Wang et al., 2020).

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Over South Asia, the moisture-bearing monsoon low-level jet is projected to shift northward in CMIP3 and CMIP5 models (Sandeep and Ajayamohan, 2015). Greater warming over the Asian land region compared to the ocean contributes to intensification of the monsoon low-level south-westerly winds and precipitation (Endo et al., 2018), even though the combined effect of upper and lower tropospheric warming makes the Asian monsoon circulation response rather complicated. A high resolution model projection, based on the RCP8.5 scenario, indicates that a northward shift of the low-level jet and associated weakening of the large-scale monsoon circulation can induce a large reduction in the genesis of monsoon low pressure systems by the late 21st century (Sandeep et al., 2018). Experiments with constant forcing indicate that at 1.5°C and 2°C global warming levels, mean precipitation and monsoon extremes are projected to intensify in summer over India and South Asia (Chevuturi et al., 2018; D. Lee et al., 2018) and that a 0.5°C difference would imply a 3% increase of precipitation (Chevuturi et al., 2018). CMIP5 models project an increase in short intense active days and decrease in long active days, with no significant change in the number of break spells for India (Sudeepkumar et al., 2018).

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While the potential role of increasing hydrologic extremes with quasi-resonant stationary waves during NH summer has received considerable attention (see Section 8.3.2.6), as yet there is no clear evidence in model projections that this variability will increase (Teng and Branstator, 2019). The influence of the Arctic on mid-latitude circulation is assessed in Cross-Chapter Box 10.1, which reports that there is low confidence in the dominant contribution of Arctic warming compared to other drivers in future projections. Potential changes to the stratospheric polar vortex in CMIP5 models have a substantial influence on tropospheric stationary waves and associated hydrologic impacts in both the NH (Zappa and Shepherd, 2017) and SH (Mindlin et al., 2020). CMIP5 models have some important limitations in their representation of stationary waves (Lee and Black, 2013; Simpson et al., 2016; Garfinkel et al., 2020) and this aspect of CMIP6 models has not yet been comprehensively evaluated.

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The role of temperature trends in influencing storm tracks has been further investigated, both in terms of upper tropospheric tropical warming (Zappa and Shepherd, 2017) and lower tropospheric Arctic amplification (J. Wang et al., 2017), including the direct role of Arctic sea ice loss (Zappa et al., 2018), and the competition between their influences (Shaw et al. , 2016). Physical linkages between Arctic amplification and changes in the mid-latitudes are uncertain, as discussed in Chapter 10 (Cross-Chapter Box 10.1). The remote and local SST influence has been further examined by Ciasto et al. (2016), who confirmed sensitivity of the storm tracks to the SST trends generated by the models and suggested that the primary greenhouse gas influence on storm track changes was indirect, acting through the greenhouse gas influence on SSTs. The importance of the stratospheric polar vortex in storm track changes has received more attention (Zappa and Shepherd, 2017; Mindlin et al. , 2020) and the anticipated recovery of the ozone layer further complicates the role of the stratosphere (Shaw et al., 2016; Bracegirdle et al., 2020b).

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Sensitivity studies generally project increases in Madden Julian Oscillation (MJO, Annex IV.2.8) precipitation amplitude in a warmer climate, with increases of up to 14%°C–1of warming (Arnold et al., 2013, 2015; Caballero and Huber, 2013; Liu and Allan, 2013; Maloney and Xie, 2013; Schubert et al., 2013; Subramanian et al., 2014; Carlson and Caballero, 2016; Pritchard and Yang, 2016; Adames et al., 2017a; Wolding et al., 2017; Haertel, 2018). However, in CMIP5 models with realistic historical MJO behaviour, the precipitation amplitude over the Indo-Pacific warm pool region changes from4% to +8%°C–1 in the RCP8.5 scenario relative to the end of the 20th century (Bui and Maloney, 2018; Maloney et al., 2019). When simulated MJO precipitation amplitude increases with warming, the leading factor for such change is the intensification of the lower tropospheric vertical moisture gradient, that supports stronger vertical moisture advection per unit diabatic heating (Arnold et al. , 2015; Adames et al. , 2017a, b; Wolding et al. , 2017). In idealized simulations with constant CO2 forcing with El Niño-like patterns, the MJO activity penetrates farther east into the central and east Pacific with increased warming (Subramanian et al., 2014; Adames et al., 2017a). Increased MJO convective variability in a warmer climate does not reflect into increased ability of the MJO to force the extratropics (Wolding et al., 2017).

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Since AR5, there have been improvements in the representation of convective clouds and related precipitation in GCMs. For instance, the drizzle issue (too light and too frequent rainfall events) has led to modifications in the deep convection triggering scheme (Rochetin et al. , 2014b; Han et al. , 2017; Xie et al. , 2018; Wu et al. , 2019). Although high-resolution studies have highlighted these limitations, most GCMs still rely on a convective available potential energy (CAPE) closure which has been adapted to various cloud regimes (Bechtold et al., 2014; Han et al., 2017; Walters et al., 2019) or evaluated against convection-permitting models (CPMs; J. Chen et al., 2020a). To increase the sensitivity of convection to tropospheric humidity, several models now include a representation of deep convective entrainment dependent on relative humidity (Bechtold et al. , 2008; Han et al. , 2017; M. Zhao et al. , 2018; Walters et al. , 2019). Other efforts have focused on the improvement of shallow convection and low-level cloudiness due to their major contribution to uncertainty in climate sensitivity (Section 7.4.2.4). A cloud-regime-based study however highlights an apparent disconnection between cloud and precipitation processes in GCMs (Tan et al., 2018), suggesting that a good representation of clouds does not lead to systematic improvement in simulated precipitation. A global simulation in which the parametrized convection is switched off shows a strong influence of parametrized convection on daily precipitation extremes(P. Maher et al., 2018). Regional simulations at a 25km resolution suggest that an explicit deep convection can be beneficial even at such a relatively coarse resolution (Vergara-Temprado et al., 2020). Perturbed physics ensembles (PPE, Section 1.4.4) make it possible to identify parameters in the convection scheme that are most important in determining future precipitation changes (Bernstein and Neelin, 2016).

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Since AR5, there has been increasing recognition of the need to better understand the role of land–atmosphere coupling and related feedbacks (Joetzjer et al. , 2014; Berg et al. , 2016; Catalano et al. , 2016; Berg and Sheffield, 2018a; Santanello et al. , 2018). This has led to the development of dedicated field campaigns (Song et al., 2016; Phillips et al., 2017; Dirmeyer et al., 2018), remotely sensed observations (Ferguson and Wood, 2011; Roundy and Santanello, 2017), and tailored diagnostics (Tawfik et al., 2015a, b; Miralles et al., 2016, 2019; Dirmeyer and Halder, 2017). Dynamic vegetation models have been introduced in global ESMs but they need further evaluation (Medlyn et al., 2015; Prentice et al., 2015; Cantú et al., 2018; Franks et al., 2018) to provide valuable information on potential vegetation feedbacks. Plant migration and mortality, increased disturbances from wild fires, insects and extreme events, interactive nitrogen cycle, or the impact of increased levels of tropospheric ozone are often ignored or poorly represented in the current-generation of ESMs (Bonan and Doney, 2018; Fisher et al., 2018).

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Akinsanola, A.A. and W. Zhou, 2020: Understanding the Variability of West African Summer Monsoon Rainfall: Contrasting Tropospheric Features and Monsoon Index. Atmosphere, 11(3), 309, doi: 10.3390/atmos11030309.

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Ceppi, P. and J.M. Gregory, 2017: Relationship of tropospheric stability to climate sensitivity and Earth’s observed radiation budget. Proceedings of the National Academy of Sciences, 114(50), 13126–13131, doi: 10.1073/pnas.1714308114.

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Choudhury, A.D. et al., 2018: A Phenomenological Paradigm for Midtropospheric Cyclogenesis in the Indian Summer Monsoon. Journal of the Atmospheric Sciences, 75(9), 2931–2954, doi: 10.1175/jas-d-17-0356.1.

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Kidston, J. et al., 2015: Stratospheric influence on tropospheric jet streams, storm tracks and surface weather. Nature Geoscience, 8(6), 433–440, doi: 10.1038/ngeo2424.

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Ma, J., S.-P. Xie, and Y. Kosaka, 2012: Mechanisms for Tropical Tropospheric Circulation Change in Response to Global Warming. Journal of Climate, 25(8), 2979–2994, doi: 10.1175/jcli-d-11-00048.1.

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MacIntosh, C.R. et al., 2016: Contrasting fast precipitation responses to tropospheric and stratospheric ozone forcing. Geophysical Research Letters, 43(3), 1263–1271, doi: 10.1002/2015gl067231.

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Takahashi, H.G., 2018: A Systematic Tropospheric Dry Bias in the Tropics in CMIP5 Models: Relationship between Water Vapor and Rainfall Characteristics. Journal of the Meteorological Society of Japan. Series II, 96(4), 415–423, doi: 10.2151/jmsj.2018-046.

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Yang, Y.-M. and B. Wang, 2019: Improving MJO simulation by enhancing the interaction between boundary layer convergence and lower tropospheric heating. Climate Dynamics, 52(7–8), 4671–4693, doi: 10.1007/s00382-018-4407-9.

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Data at altitude came initially from scattered mountain summits, balloons and kites, but the upper troposphere and stratosphere were not systematically observed until radiosonde (weather balloon) networks emerged in the 1940s and 1950s. These provide the longest continuous quasi-global record of the atmosphere’s vertical dimension (Stickler et al., 2010). New methods for spatial and temporal homogenisation (intercalibration and quality control) of radiosonde records were introduced in the 2000s (Sherwood et al., 2008, 2015; Haimberger et al., 2012). Since 1978, Microwave Sounding Units (MSU) mounted on Earth-orbiting satellites have provided a second high-altitude data source, measuring temperature, humidity, ozone, and liquid water throughout the atmosphere. Over time, these satellite data have required numerous adjustments to account for such factors as orbital precession and decay (Edwards, 2010). Despite repeated adjustments, however, marked differences remain in the temperature trends from surface, radiosonde, and satellite observations; between the results from three research groups that analyse satellite data (University of Alabama in Huntsville (UAH), Remote Sensing Systems (RSS), and NOAA); and between modelled and satellite-derived tropospheric warming trends (Thorne et al., 2011; Santer et al., 2017). These differences are the subject of ongoing research (Maycock et al., 2018). In the 2000s, Atmospheric Infrared Sounder (AIRS) and radio occultation (GNSS-RO) measurements provided new ways to measure temperature at altitude, complementing data from the MSU. GNSS-RO is a new independent, absolutely calibrated source, using the refraction of radio-frequency signals from the Global Navigation Satellite System (GNSS) to measure temperature, pressure and water vapour (Section 2.3.1.2.1; Foelsche et al., 2008; Anthes, 2011).

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Precipitation is not usually assimilated in reanalyses and, depending on the region, reanalysis precipitation can differ from observations by more than the observational error (Zhou and Wang, 2017; Sun et al., 2018; Alexander et al., 2020; Bador et al., 2020), although these studies did not include ERA5. Assimilation of radiance observations from microwave imagers which, over ice-free ocean surfaces, improve the analysis of lower-tropospheric humidity, cloud liquid water and ocean-surface wind speed have resulted in improved precipitation outputs in ERA5 (Hersbach et al., 2020). Global averages of other fields, particularly temperature, from ERA-Interim and JRA-55 reanalyses continue to be consistent over the last 20 years with surface observational data sets that include the polar regions (Simmons and Poli, 2015), although biases in precipitation and radiation can influence temperatures regionally (Zhou et al., 2018). The global average surface temperature from MERRA-2 is far cooler in recent years than temperatures derived from ERA-Interim and JRA-55, which may be due to the assimilation of aerosols and their interactions (Section 2.3).

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Although reanalyses such as ERA5 take advantage of new observational datasets and present a great improvement in atmospheric reanalyses, the issues introduced by the evolving observational network remain. Sparse input reanalyses, where only a limited set of reliable and long-observed records are assimilated, address these issues, with the limitation of fewer observational constraints. These efforts are sometimes called centennial-scale reanalyses. One example is the atmospheric 20th century Reanalysis (Compo et al., 2011; Slivinski et al., 2021) which assimilates only surface and sea-level pressure observations, and is constrained by time-varying observed changes in atmospheric constituents, prescribed sea surface temperatures and sea ice concentration, creating a reconstruction of the weather over the whole globe every three hours for the period 1806–2015. The ERA-20C atmospheric reanalysis (covering 1900–2010; Poli et al., 2016) also assimilates marine wind observations, and CERA-20C is a centennial-scale reanalysis that assimilates both atmospheric and oceanic observations for the 1901–2010 period (Laloyaux et al., 2018). These centennial-scale reanalyses are often run as ensembles that provide an estimate of the uncertainty in the simulated variables over space and time. Slivinski et al. (2021) conclude that the uncertainties in surface circulation fields in version 3 of the 20th century Reanalysis are reliable and that there is also skill in its tropospheric reconstruction over the 20th century. Long-term changes in other variables, such as precipitation, also agree well with direct observation-based datasets (Sections 2.3.1.3 and 8.3.2.8).

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Since AR5, simplified climate models have been developed further, and their use is increasing. Different purposes motivating development include: being as simple as possible for teaching purposes (e.g., a two-layer energy balance model); being as comprehensive as possible to allow for propagation of uncertainties across multiple Earth system domains (MAGICC and others); or focusing on higher-complexity representation of specific domains (e.g., OSCAR). The common theme motivating many models is to improve parameterizations that reflect the latest findings in complex ESM interactions – such as the nitrogen cycle addition to the carbon cycle, or tropospheric and stratospheric ozone exchange – with the aim of emulating their global mean temperature response. Also, within the simple models that have a rudimentary representation of spatial heterogeneity (e.g., four-box simple climate models), the ambition is to represent heterogeneous forcers such as black carbon more adequately (Stjern et al., 2017), provide an appropriate representation of the forcing–feedback framework (e.g., Sherwood et al., 2015), investigate new parameterizations of ocean heat uptake, and implement better representations of volcanic aerosol-induced cooling (Gregory et al., 2016a).

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Instrument simulators provide estimates of what a satellite would see if looking down on the model-simulated planet, and improve the direct comparison of modelled variables such as clouds, precipitation and upper tropospheric humidity with observations from satellites (e.g., Kay et al., 2011; Klein et al., 2013; Cesana and Waliser, 2016; Konsta et al., 2016; Jin et al., 2017; Chepfer et al., 2018; Swales et al., 2018; Zhang et al., 2018). Within the framework of the Cloud Feedback Model Intercomparison Project (CFMIP) contribution to CMIP6 (Webb et al., 2017), a new version of the Cloud Feedback Model Intercomparison Project Observational Simulator (COSP; Swales et al., 2018) has been released which makes use of a collection of observation proxies or satellite simulators. Related approaches in this rapidly evolving field include simulators for Arctic Ocean observations (Burgard et al., 2020) and measurements of aerosol observations along aircraft trajectories (Watson-Parris et al., 2019).

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Checa-Garcia, R., M.I. Hegglin, D. Kinnison, D.A. Plummer, and K.P. Shine, 2018: Historical Tropospheric and Stratospheric Ozone Radiative Forcing Using the CMIP6 Database. Geophysical Research Letters, 45(7), 3264–3273, doi: 10.1002/2017gl076770.

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Santer, B.D. et al., 2017: Causes of differences in model and satellite tropospheric warming rates. Nature Geoscience, 10(7), 478–485, doi: 10.1038/ngeo2973.

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Sherwood, S.C., C.L. Meyer, R.J. Allen, and H.A. Titchner, 2008: Robust Tropospheric Warming Revealed by Iteratively Homogenized Radiosonde Data. Journal of Climate, 21(20), 5336–5352, doi: 10.1175/2008jcli2320.1.

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Thorne, P.W., J.R. Lanzante, T.C. Peterson, D.J. Seidel, and K.P. Shine, 2011: Tropospheric temperature trends: history of an ongoing controversy. WIREs Climate Change, 2(1), 66–88, doi: 10.1002/wcc.80.

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Process-level understanding of tropospheric gas and aerosol chemistry developed through laboratory and simulation chamber experiments, as well as quantum chemical theory, is used to generate chemical mechanisms. Atmospheric simulation chambers are designed to identify the chemical pathways and quantify reaction kinetics in isolation from atmospheric transport, deposition and emission processes. Ideally the chemical regimes studied are representative for ambient atmospheric complexity and concentrations (e.g., McFiggans et al. , 2019). Recently, quantum chemical theory has advanced to a level that it can provide kinetic and product information in a parameter range not possible with laboratory experiments (Vereecken et al. , 2015). Iterative and interlinked use of simulation chamber and quantum chemical theory has led to improved knowledge of chemical mechanisms (Peeters et al. , 2009, 2014; Nguyen et al. , 2010; Fuchs et al. , 2013). For application in chemistry–climate models (CCMs), the chemical mechanisms need to be computationally efficient, requiring simplifications. Such simplifications include reduced hydrocarbon representations, the application of lumping techniques (one compound or a chemical structure representing a family of compounds, for example, as done for parametrizing SOA formation) and/or the implementation of artificial operators representing key steps of the chemistry (Emmerson and Evans, 2009; Xia et al., 2009; Stockwell et al., 2020). Additionally, aerosol microphysical processes (nucleation, coagulation, condensation, evaporation and sedimentation) that determine the evolution of aerosol number concentrations and size particle distribution are represented in parametrized forms in global models with varying levels of complexity (Mann et al., 2014).

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Global three-dimensional CCMs (Box 6.1, Figure 1) represent the full coupling of chemistry with climate physics (e.g., Morgenstern et al. , 2017) with different levels of complexity (e.g., interactive aerosols with or without tropospheric and/or stratospheric chemistry). Methane concentrations are typically prescribed or constrained to observations while emissions of other SLCFs (or their precursors) are either prescribed or calculated interactively in the current generation of CCMs (Collins et al. , 2017). CCMs, now part of Earth system models (ESMs), are applied extensively to simulate the distribution and evolution of chemical compounds on a variety of spatial and temporal scales to improve current knowledge, make future projections and investigate global scale chemistry–climate interactions and feedbacks (Section 3.8.2.2). CCMs are also used to interpret observations to disentangle the processes that drive observed variability and trends. Some aspects of air quality, such as diurnal peaks or local threshold violations, strong gradient in chemical regimes and coupling between processes cannot be captured by relatively coarse spatial resolution (>50 km) global CCMs (Markakis et al., 2014) and necessitate subsequent downscaling modelling exercises.

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An assessment of the global methane budget is provided in Chapter 5, while this section assesses methane atmospheric lifetime and perturbation time (Prather et al., 2001). The AR5 based its assessment of methane lifetime on Prather et al. (2012). The methane chemical lifetime due to tropospheric OH, the primary sink of methane, was assessed to be 11.2 ± 1.3 years constrained by surface observations of methyl chloroform (MCF), and lifetimes due to stratospheric loss, 2tropospheric halogen loss and soil uptake were assessed to be 150 ± 50 years, 200 ± 100 years, and 120 ± 24 years, respectively (Myhre et al., 2013). Considering the full range of individual lifetimes, the total methane lifetime was assessed in AR5 to be 9.25 ± 0.6 years.

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The global chemical methane sink, essentially due to tropospheric OH, required to calculate the chemical lifetime is estimated by either bottom-up global CCMs and ESMs (BU) or top-down observational inversion methods (TD). BU global models represent the coupled chemical processes and feedbacks that determine the chemical sinks but show large diversity in their estimates, particularly the tropospheric OH sink (Zhao et al., 2019; Stevenson et al., 2020). TD inversion methods, on the contrary, provide independent observational constraints on the methane sink due to tropospheric OH over large spatio-temporal scales, but are prone to observational uncertainties and do not account for the chemical feedbacks on OH (Prather and Holmes, 2017; Naus et al., 2019). The central estimate of mean chemical methane loss over the period 2008–2017 varied from 602 [minimum and maximum range of 507–803] Tg yr–1 from BU chemistry–climate models in the Chemistry–Climate Modelling Initiative (CCMI) to 514 [474–529] Tg yr–1 from TD inverse modelling (Section 5.2.2 and Table 5.2). The smaller range in the TD estimate (11%) results from the use of a common climatological mean OH distribution (Saunois et al., 2020; Zhao et al., 2020a), while the larger range in the BU estimate (49%) reflects the diversity in OH concentrations from different chemical

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The methane perturbation lifetime (τpert ) is defined as the e-folding time it takes for the methane burden to decay back to its initial value after being perturbed by a change in methane emissions. Perturbation lifetime is longer than the total atmospheric lifetime of methane, as an increase in methane emissions decreases tropospheric OH, which in turn increases the lifetime and therefore the methane burden (Prather, 1994; Fuglestvedt et al., 1996; Holmes et al., 2013; Holmes, 2018). Since perturbation lifetime relates changes in emissions to changes in burden, it is used to determine the emissions metrics assessed in Chapter 7 (Section 7.6). The perturbation lifetime is related to the atmospheric lifetime asτpert = f *τtotal where f is the feedback factor and is calculated as f = 1/(1-s), where s = δ(lnτtotal)/ δ(ln[CH4]) (Prather et al., 2001). Since there are no observational constraints for eitherτpert or f, these quantities are derived from CCMs or ESMs. AR5 used f = 1.34 ± 0.06 based on a combination of multi-model (mostly CTMs and a few CCMs) estimates (Holmes et al., 2013). A recent model study explored new aspects of methane feedbacks finding that the strength of the feedback, typically treated as a constant, varies in space and time but will in all likelihood remain within 10% over the 21st century (Holmes, 2018). For this Assessment, the value of f is assessed to be 1.30 ± 0.07 based on a six-member ensemble of AerChemMIP ESMs (Thornhill et al., 2021b). This f value is slightly smaller but within the range of the AR5 value. This results in an overall perturbation methane lifetime of 11.8 ± 1.8 years, within the range of the AR5 value of 12.4 ± 1.4 years. The methane perturbation lifetime assessed here is used in the calculation of emissions metrics in Section 7.6.

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About 10% of the total atmospheric ozone column resides in the troposphere. The ozone forcing on climate strongly depends on its vertical and latitudinal distribution in the troposphere. The lifetime of ozone in the troposphere ranges from a few hours in polluted urban regions to up to few months in the upper troposphere. Observed tropospheric ozone concentrations range from less than 10 ppb over the tropical Pacific Ocean to as much as 100 ppb in the upper troposphere and more than 100 ppb downwind of major ozone precursor emissions regions. An ensemble of five CMIP6 models including whole atmospheric chemistry and interactive ozone has been shown to simulate consistently the present-day ozone distribution (north to south and latitudinal gradients) and its seasonal variability when compared with observations from sondes, background surface stations and satellite products (Griffiths et al., 2021). The biases, whose magnitude is similar to AR5, are lower than 15% against climatological seasonal cycles from ozonesondes with an overestimate in the Northern Hemisphere and an underestimate in the Southern Hemisphere (Griffiths et al., 2021). The CMIP6 multi-model ensemble estimate of the global mean lifetime of ozone for present-day conditions is 25.5 ± 2.2 days (Griffiths et al., 2021), which is within the range of previous multi-model estimates (Stevenson et al., 2006; Young et al., 2013), indicating a high level of confidence.

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The AR5 assessed the tropospheric ozone burden to be 337 ± 23 Tg for the year 2000 based on the ACCMIP ensemble of model simulations (Myhre et al., 2013). Multiple satellite products, ozonesondes and CCMs are used to estimate tropospheric ozone burden (Table 6.3). Satellite products provide lower-bound values as they exclude regions under polar night conditions (Gaudel et al., 2018). The tropospheric ozone burden values from multi-model exercises are within the range of the observational estimates despite different definitions of the tropopause for multi-model estimates which can lead to differences of about 10% on the ozone-burden model estimates (Griffiths et al., 2021). Weighted by their number of members, CMIP6 and CCMI multi-model estimates and observational estimates of tropospheric ozone burden in about the year 2010, lead to an assessment of the tropospheric ozone burden of 347 ± 28 Tg for 2010.

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The tropospheric ozone budget is controlled by chemical production and loss, by stratospheric–tropospheric exchange (STE), and by deposition at the Earth’s surface, whose magnitude are calculated by CCMs (Table 6.3). Despite The high agreement of the model ensemble mean with observational estimates in the present-day tropospheric ozone burden, the values of individual budget terms can vary widely across models in CMIP6, consistent with previous model intercomparison experiments (Young et al., 2018). Furthermore, single-model studies have shown that the halogen chemistry, which is typically neglected from model chemistry schemes in CCMs, may have a notable impact on the ozone budget, as halogens, particularly of marine origin, take part in efficient ozone-loss catalytic cycles in the troposphere (Saiz-Lopez et al. , 2012; Sarwar et al. , 2015; Sherwen et al. , 2016).

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In summary, there is high confidence in the estimated present-day (about 2010) global tropospheric ozone burden based on an ensemble of models and observational estimates (347 ± 28 Tg), but there is medium confidence among the individual models for their estimates of the tropospheric ozone-related budget terms.

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The distribution of tropospheric NOx is highly variable in space and time owing to its short lifetime coupled with highly heterogeneous emission and sink patterns. NOx undergoes chemical processing, including the formation of nitric acid (HNO3), nitrate (NO3), and organic nitrates (e.g., alkyl nitrate and peroxyacyl nitrate), atmospheric transport, and deposition. Despite challenges in retrieving quantitative information from satellite observations (Duncan et al., 2014; Lin et al., 2015; Lorente et al., 2017; Silvern et al., 2018), improved accuracy and resolution of satellite-derived tropospheric NO2 columns over the past two decades have advanced understanding of the global distribution, long-term trends and source attribution of NOx. Long-term average tropospheric NO2 column based on multiple satellite-borne instruments (Figure 6.6a) reveals the highest NO2 levels over the most populated, urbanized and industrialized regions of the world corresponding to high NOx emissions source regions (Krotkov et al., 2016; Georgoulias et al., 2019). Enhanced but highly variable NO2 columns are also associated with biomass-burning regions as well as areas influenced by lightning activity (Miyazaki et al., 2014; Tanimoto et al., 2015).

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The AR5 reported NO2 decreases by 30–50% in Europe and North America, and increases by more than a factor of two in Asia, over the 1996–2011 period based on satellite observations (Hartmann et al., 2013). Extension of this analysis covering the time period up to 2015 reveals that NO2 has continued to decline over the USA, Western Europe and Japan (Schneider et al., 2015; Duncan et al., 2016; Krotkov et al., 2016) because of effective fossil fuel NOx emissions controls (Section 6.2), although this rate of decline has slowed down post-2011 (Jiang et al., 2018). Satellite observations also reveal a 32% decline in NO2 column over China after peaking in 2011 (Figure 6.6b), consistent with declining NOx emissions (Section 6.2) due to the implementation of emissions-control strategies (de Foy et al. , 2016; Irie et al. , 2016; F. Liu et al. , 2016). Over Southern Asia, tropospheric NO2 levels have grown rapidly with increases of 50% during 2005–2015, largely driven by hotspot areas in India experiencing rapid expansion of the power sector (Duncan et al., 2016; Krotkov et al., 2016). Further analysis indicates that many parts of India have also undergone a reversal in NO2 trends since 2011 that has been attributed to a combination of factors, including a slowdown in economic growth, implementation of cleaner technologies, non-linear NOx chemistry, and meteorological variability (Georgoulias et al., 2019). Satellite data reveals spatially heterogeneous NO2 trends over the Middle East with an overall increase over 2005–2010 and a decrease over large parts of the region after 2011–2012. The reasons for trend reversal within individual areas are diverse, including warfare, imposed sanctions, and air-quality controls (Lelieveld et al., 2015a; Georgoulias et al., 2019). Satellite-derived tropospheric NO2 levels over Africa and Latin America do not show a clear trend; both increasing and decreasing trends are observed over large agglomerations in these regions since the early 2000s (Schneider et al., 2015; Duncan et al., 2016).

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In summary, global tropospheric NOx abundance has increased from 1850–2015 (high confidence). Satellite observations of tropospheric NOx indicate strong regional variations in trends over 2005–2015. There is high confidence that NO2 has declined over the USA and Western Europe since the mid-1990s and increased over China until 2011. NO2 trends have reversed (declining) over China beginning in 2012 and NO2 has increased over Southern Asia by 50% since 2005 (medium confidence).

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Halogenated species are emitted in the atmosphere in the form of the synthetically produced chlorofluorocarbons (CFCs), halons, hydrochlorofluorocarbons (HCFCs), hydrofluorocarbons (HFCs) and others. Their historical global abundances are provided in Annex III and discussed in Chapter 2 (Section 2.2.4 and Table 2.3). In summary, for the period 2011–2019, the abundance of total chlorine from HCFCs has continued to increase in the atmosphere with decreased growth rates; total tropospheric bromine from halons and methyl bromide continued to decrease while abundances of most currently measured HFCs increased significantly, consistent with expectations based on the ongoing transition away from the use of ODSs. Here, emphasis is given on the very short-lived halogenated species (VSLSs). The trends for these species were not discussed in IPCC AR5.

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In summary, there is high confidence that the global tropospheric sulphate burden increased from 1850 to around 2005, but there are large regional differences in the magnitude. Sulphate aerosol concentrations in North America and Europe have declined over 1980–2015 with slightly stronger reductions in North America (47 ± 20%) than in Europe (40 ± 30%) over 2000–2015, though Europe had larger reductions in the prior decade (1990–2000; 52 ± 21% and 21 ± 14% respectively for Europe and North America). In Asia, the trends are more scattered, though there is medium confidence that there was a strong increase up to around 2005, followed by a steep decline in China, while over India, the concentrations are increasing steadily.

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Ammonium nitrate is semi-volatile, which results in complex spatial and temporal patterns in its concentrations (Putaud et al., 2010; Hand et al., 2012a; H. Zhang et al., 2012), reflecting variations in its precursors, NH3 and HNO3, as well as SO42–, non-volatile cations, temperature and relative humidity (Nenes et al. , 2020). High relative humidity and low temperature as well as elevated fine particulate matter loading (Huang et al. , 2014; Petit et al. , 2015; H. Li et al. , 2016; Sandrini et al. , 2016) favour nitrate production. Measurements reveal the high contribution of NO3 to surface PM2.5 (>30%) in regions with elevated regional NOx and NH3 emissions, such as the Paris area (Beekmann et al., 2015; Zhang et al., 2019), northern Italy (Masiol et al., 2015; Ricciardelli et al., 2017), Salt Lake City (Kuprov et al., 2014; Franchin et al., 2018), the North China Plains (Guo et al., 2014; Chen et al., 2016) and New Delhi(Pant et al., 2015). Recent observations also show that ammonium nitrate contributes to the Asian tropopause aerosol layer (Vernier et al., 2018; Höpfner et al., 2019). Model diversity in simulating the present-day global fine-mode NO3burden is large with two multi-model intercomparison studies reporting estimates in the range of 0.14–1.88 Tg and 0.08–0.93 Tg respectively (Bian et al., 2017; Gliß et al., 2021). Models differ in their estimates of the global tropospheric nitrate burden by up to a factor of 13 with differences remaining nearly the same across the CMIP5 and CMIP6 generation of models (Bian et al., 2017; Gliß et al., 2021). While regional patterns in the concentration of fine-mode NO3 are qualitatively captured by models, the simulation of fine-mode NO3is generally worse than that of NH4+or SO42– (Bian et al., 2017). This can be partly attributed to the semi-volatile nature of ammonium nitrate and biases in the simulation of its precursors (Heald et al., 2014; Paulot et al., 2016), including the sub-grid scale heterogeneity in NOx and NH3 emissions (Zakoura and Pandis, 2018).

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The atmospheric oxidising capacity is determined primarily by tropospheric hydroxyl (OH) radical and to a smaller extent by NO3 radical, ozone, hydrogen peroxide (H2O2) and halogen radicals. OH is the main sink for many SLCFs, including methane, halogenated compounds (HCFCs and HFCs), CO and NMVOCs, controlling their lifetimes and consequently their abundance and climate influence. OH-initiated oxidation of methane, CO and NMVOCs in the presence of NOx leads to the production of tropospheric ozone. OH also contributes to the formation of aerosols from oxidation of SO2 to sulphate and NMVOCs to secondary organic aerosols. The evolution of the atmospheric oxidising capacity of the Earth driven by human activities and natural processes is, therefore, of significance for climate and air-quality concerns.

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The main source of tropospheric OH is the photoexcitation of tropospheric ozone that creates an electronically excited oxygen atom which reacts with water vapour producing OH. A secondary source of importance for global OH is the recycling of peroxy radicals formed by the reaction of OH with reduced and partly oxidized species, including methane, CO and NMVOCs. In polluted air, NOx emissions control the secondary OH production, while in pristine air it occurs via other mechanisms involving, in particular, isoprene (Lelieveld et al., 2016; Wennberg et al., 2018). Knowledge of the effect of isoprene oxidation on OH recycling has evolved tremendously over the past decade, facilitating mechanistic explanation of elevated OH concentrations observed in locations characterised by low NOx levels (Hofzumahaus et al. , 2009; Paulot et al. , 2009; Peeters et al. , 2009, 2014; Fuchs et al. , 2013). Since AR5, the inclusion of improved chemical mechanisms in some CTMs suggest advances in understanding of the global OH budget, however, these improvements have yet to be incorporated in CMIP6-generation ESMs.

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As a result of the complex photochemistry, tropospheric OH abundance is sensitive to changes in SLCF emissions as well as climate. Increases in methane, CO and NMVOCs reduce OH while increases in water vapour and temperature, incoming solar radiation, NOx and tropospheric ozone enhance OH. The OH level thus responds to climate change and climate variability via its sensitivity to temperature and water vapour, as well as the influence of climate on natural emissions (e.g., wetland methane emissions, lightning NOx, BVOCs, fire emissions) with consequent feedbacks on climate (Section 6.4.5). Climate modes of variability, like El Niño–Southern Oscillation, also contribute to OH variability via changes in lightning NOx emissions and deep convection (Turner et al. , 2018), and fire emissions (Rowlinson et al., 2019).

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Over paleo time scales, proxy-based observational constraints from methane and formaldehyde suggest tropospheric OH to be a factor of two to four lower in the Last Glacial Maximum (LGM) relative to pre-industrial levels, though these estimates are highly uncertain (Alexander and Mickley, 2015). Global models, in contrast, exhibit no change in tropospheric OH (and consequently in methane lifetime) at the LGM relative to the pre-industrial period (Murray et al. , 2014; Quiquet et al. , 2015), however, the sign and magnitude of OH changes are sensitive to model predictions of changes in natural emissions, including lightning NOx and BVOCs, and model representation of isoprene oxidation chemistry (Achakulwisut et al., 2015; Hopcroft et al., 2017).

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Regarding change since the pre-industrial era, at the time of the AR5, the ensemble mean of 17 global models participating in ACCMIP indicated little change in tropospheric OH from 1850–2000. This was due to the competing and finally offsetting changes in factors enhancing or reducing OH with a consequent small decline in methane lifetime (Naik et al., 2013; Voulgarakis et al., 2013). However, there was large diversity in both the sign and magnitude of past OH changes across the individual models attributed to the disparate implementation of chemical and physical processes (Nicely et al., 2017; Wild et al., 2020). Analysis of historical simulations from three CMIP6 ESMs indicates little change in global mean OH from 1850 to about 1980 (Stevenson et al., 2020). However, there is no observational evidence of changes in global OH since 1850 up to the early 1980s to evaluate the ESMs.

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Tropospheric aerosols influence the land and ocean ecosystem productivity and the carbon cycle through changing physical climate and meteorology (Jones, 2003; Cox et al., 2008; Mahowald, 2011; Unger et al., 2017) and through changing deposition of nutrients including nitrogen, sulphur, iron and phosphorous (Mahowald et al., 2017; Kanakidou et al., 2018). There is robust evidence and high agreement from field (Oliveira et al. , 2007; Cirino et al. , 2014; Rap et al. , 2015; X. Wang et al. , 2018) and modelling(Mercado et al., 2009; Strada and Unger, 2016; Lu et al., 2017; Yue et al., 2017) studies that aerosols affect plant productivity through increasing the diffuse fraction of downward shortwave radiation, although the magnitude and importance to the global land carbon sink is controversial. At large scales the dominant effect of aerosols on the carbon cycle is likely a global cooling effect of the climate (medium confidence) (Jones, 2003; Mahowald, 2011; Unger et al., 2017). We assess that these interactions between aerosols and the carbon cycle are currently too uncertain to constrain quantitatively the indirect CO2 forcing.

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Climate–ozone feedback: Changes in ozone concentrations in response to projected climate change have been shown to lead to a potential climate-atmospheric chemistry feedback. Chemistry–climate models consistently project a decrease in lower tropical stratospheric ozone levels due to enhanced upwelling of ozone-poor tropospheric air associated with surface warming-driven strengthening of the Brewer-Dobson circulation (Bunzel and Schmidt, 2013). Further, models project an increase in middle and extratropical stratospheric ozone due to increased downwelling through the strengthened Brewer-Dobson circulation (Bekki et al., 2013; Dietmüller et al., 2014). These stratospheric ozone changes induce a net-negative global mean ozone radiative feedback (Dietmüller et al., 2014). Tropospheric ozone shows a range of responses to climate with models generally agreeing that warmer climate will lead to decreases in the tropical lower troposphere owing to increased water vapour, and increases in the subtropical to mid-latitude upper troposphere due to increases in lightning and stratosphere-to-troposphere transport (Stevenson et al., 2013). A small positive feedback is estimated from climate-induced changes in global mean tropospheric ozone (Dietmüller et al., 2014) while a small negative feedback is estimated by Heinze et al. (2019) based on the model results of Stevenson et al. (2013). Additionally, these ozone feedbacks induce a change in stratospheric water vapour amplifying the feedback due to stratospheric ozone (Stuber et al., 2001). Since AR5, several modelling studies have estimated the intensity of meteorology-driven ozone feedbacks on climate from either combined tropospheric and stratospheric ozone changes or separately with contrasting results. One study suggests no change (Marsh et al., 2016), while other studies report reductions of ECS ranging from 7–8% (Dietmüller et al., 2014; Muthers et al., 2014) to 20% (Nowack et al., 2015). The estimate of this climate-ozone feedback parameter is very strongly model-dependent with values ranging from –0.13 to –0.01 W m–2°C–1though there is agreement that it is negative. The assessed central value and the 5–95% range of climate-ozone feedback parameter based on AerChemMIP ensemble is within the range of these published estimates, but closer to the lower bound. This climate-ozone feedback factor does not include the feedback on ozone from lightning changes which is discussed separately below.

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Climate–lightning NOxfeedback: As discussed in Section 6.2.2.1, climate change influences lightning NOx emissions. Increases in lightning NOx emissions will not only increase tropospheric ozone and decrease methane lifetime but also increase the formation of sulphate and nitrate aerosols, via oxidant changes, offsetting the positive forcing from ozone. The response of lightning NOx to climate change remains uncertain and is highly dependent on the parametrization of lightning in ESMs (Section 6.2.1.2; Finney et al., 2016b; Clark et al., 2017). AerChemMIP multi-model ensemble mean estimate a net-negative climate feedback from increases in lightning NOx in a warming world (Thornhill et al., 2021a). All AerChemMIP models use a cloud-top height lightning parametrization that predicts increases in lightning with warming. However, a positive climate-lightning NOx feedback cannot be ruled out because of the dependence of the response to lightning parametrizations as discussed in Section 6.2.2.1.

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Long-lived greenhouse gas (LLGHG) emissions reductions are typically motivated by climate change mitigation policies, whereas SLCF reductions mostly result from air pollution control and climate policies (FAQ6.2), as well as policies focusing on achieving UN Sustainable Development Goals (SDGs; Box 6.2). The management of several SLCFs (BC, methane, tropospheric ozone and HFCs) is considered in the literature as a fast-response, near-term measure to curb climate change, while reduction of emissions of LLGHGs is an essential measure for mitigating long-term climate warming (Shindell et al., 2012, 2017b; Shoemaker et al., 2013; Rogelj et al., 2014b; Lelieveld et al., 2019). Note that the term short-lived climate pollutants (SLCPs), referring only to warming SLCFs, has been used within the policy arena. The SR1.5 report states that limiting warming to 1.5°C to achieve Paris Agreement goals, implies net-zero CO2 emissions around 2050 and concurrent deep reductions in emissions of non-CO2 forcers, particularly methane (Rogelj et al., 2018a). In addition, several SLCFs are key air pollutants or precursors of fine particulate matter (PM2.5) and tropospheric ozone, and therefore subject to control driven by air-quality targets.

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Aviation is associated with a range of SLCFs, in particular emissions of NOx and aerosol particles, alongside emissions of water vapour and CO2. The largest SLCF effects are those from the formation of persistent condensation trails (contrails) and NOx emissions. Persistent contrails are ice-crystal clouds, formed around aircraft soot particles (and water vapour from the engine), injected in ambient cold and ice-supersaturated atmosphere, which can spread and form contrail cirrus clouds. The ‘net NOx’ effect arises from the formation of tropospheric ozone, counterbalanced by the destruction of ambient methane and associated cooling effects of reductions in stratospheric water vapour and background ozone. The AR5 assessed the radiative forcing from persistent linear contrails to be +0.01 [+0.005 to +0.03] W m–2 for the year 2011, with medium confidence (Boucher et al., 2013). The combined linear contrail and their subsequent evolution to contrail cirrus radiative forcing from aviation was assessed to be +0.05 [+0.02 to +0.15] W m–2, with low confidence. An additional forcing of +0.003 W m–2 due to emissions of water vapour in the stratosphere by aviation was also reported (Boucher et al., 2013). The aviation sector was also estimated to lead to a net surface warming at 20- and 100-year horizons following a one-year pulse emission. This net temperature response was determined by similar contributions from contrails, contrail cirrus and CO2 over a 20-year time horizon, and dominated by CO2 in a 100-year perspective (Figure 8.34 in AR5, Myhre et al., 2013).

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Forster et al. (2020) combined the FaIR emulator (Cross-Chapter Box 7.1) with emissions changes for a range of species, relative to a continuation of Nationally Determined Contributions (Rogelj et al., 2017). They found a negative ERF from avoided CO2 emissions that strengthens through 2020 to –0.01 W m–2. During the spring lockdown, they found a peak positive ERF of 0.1 W m–2 from loss of aerosol-induced cooling, and a peak negative ERF of –0.04 W m–2 from reductions in tropospheric ozone (from reduced photochemical production via NOx). Overall, they estimated a net ERF of +0.05 W m–2 for spring 2020, declining to +0.025 W m–2 by the end of the year.

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An additional scenario, based on the SSP3-7.0, has been designed specifically to assess the effect of a strong SLCF emissions abatement and is called SSP3-7.0-lowNTCF in the literature (Collins et al., 2017; Gidden et al., 2019). It has been applied in the modelling studies (e.g., AerChemMIP) with or without consideration of additional methane reduction and we refer here to these scenarios, respectively, as SSP3-7.0-lowSLCF-lowCH4 or SSP3-7.0-lowSLCF-highCH4. In these scenarios, aerosols, their precursors, and non-methane tropospheric ozone precursors are mitigated by applying the same emissions factors as in SSP1-1.9.

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The projections of future SLCF abundances typically follow their emissions trajectories except for SLCFs that are formed from precursor reactions (e.g., tropospheric ozone) or are influenced by biogeochemical feedbacks (Sections 6.2.2 and 6.4.5). According to multi-model CMIP6 simulations, total column ozone (reflecting mostly stratospheric ozone) is projected to return to 1960s values by the middle of the 21st century under the SSP2-4.5, SSP3-7.0, SSP4-3.4, SSP4-6.0 and SSP5-8.5 scenarios (Keeble et al., 2021). ESMs project increasing tropospheric ozone burden over the 2015–2100 period for the SSP3-7.0 scenario (Figure 6.4; Griffiths et al., 2021), there is, however, a large spread in the magnitude of this increase reflecting structural uncertainties associated with the model representation of processes that influence tropospheric ozone. Sources of uncertainties in SLCF-abundance projections include scenario uncertainties, or parametric and structural uncertainties in the model representation of the processes affecting simulated abundances with implications for radiative forcing and air quality. The evolution of methane abundances in SSP scenarios, for example, is derived from integrated assessment models (IAMs) which do not include the effects from biogeochemical feedbacks (e.g., climate-driven changes in wetland emissions; Meinshausen et al., 2020) introducing uncertainty.

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The projections of GSAT for a broad group of forcing agents (aerosols, methane, tropospheric ozone and HFCs with lifetimes lower than 50 years) for the SSP scenarios show how much of the future warming or cooling (relative to 2019) can be attributed to the SLCFs (Figure 6.22). Note that during the first two decades, some of these changes in GSAT are due to emissions before 2019, in particular for the longer-lived SLCFs such as methane and HFCs (Figure 6.15). The scenarios SSP3-7.0-lowSLCF-highCH4 and SSP3-7.0-lowSLCF-lowCH4 are special cases of the SSP3-7.0 scenario with strong, but realistic, reductions in non-methane SLCFs and all SLCFs, respectively (Gidden et al., 2019).

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As discussed in Sections 6.2, 6.3 and 6.4, there are uncertainties relating emissions of SLCFs to changes in abundance (Box 6.2) and further to ERF, in particular for aerosols and tropospheric ozone. Furthermore, there are uncertainties related to climate sensitivity, that is, the relation between ERF and change in GSAT. Uncertainties in the ERF are assessed in Chapter 7 and calibrated impulse response function also includes the assessed range (Box 7.1). There are also uncertainties related to the radiative efficacies of the different SLCFs and time scales for the response, in particular for regional emissions (Schwarber et al., 2019; Yang et al., 2019b) that cannot be accounted for with the simple models used here.

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After about 2040, it is likely that, across the scenarios, the net effect of the removal of aerosols is a further increase in GSAT. However, their contribution to the rate of change decreases towards the end of the century (from up to 0.2°C per decade before 2040 to about 0.03°C per decade after 2040). After 2040, the changes in methane, HFCs and tropospheric ozone become equally important as the changes in the aerosols for the GSAT trends. In the low-emissions scenarios (SSP1-1.9 and SSP1-2.6), the contribution to warming from the SLCFs peaks around 2040 with a very likely range of 0.04°C to 0.34°C. After the peak, the reduced warming from reductions in methane and ozone dominates, giving a best total estimate warming induced by SLCF and HFC changes of 0.12°C and 0.14°C respectively, in 2100, with a very likely range of –0.07°C to +0.45°C (Figure 6.22). However, in the longer term towards the end of the century there are very significant differences between the scenarios. In SSP3-7.0 there is a near-linear warming due to SLCFs of 0.08°C per decade, while for SSP5-8.5 there is a more rapid early warming. In SSP3-7.0, the limited reductions in aerosols, but a steady increase in methane, HFCs and ozone lead to a nearly linear contribution to GSAT reaching a best estimate of 0.5°C in 2100. Contributions from methane and ozone decrease towards 2100 in SSP5-8.5, however the warming from HFCs still increase and the SSP5-8.5 has the largest SLCF and HFC warming in 2100 with a best estimate of 0.6°C. In the SSP2-4.5 scenario, a reduction in aerosols contributes to about 0.3°C warming in 2100, while contributions from ozone and methane in this scenario are small.

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For SLCFs with lifetimes shorter than typical mixing times in the atmosphere (days to weeks), the effects on secondary forcing agents (e.g., tropospheric ozone, sulphate and nitrate aerosols) depend on where and when the emissions occur due to non-linear chemical and physical processes. Also, the ERF following a change in concentrations depends on the local conditions (Sections 6.2, 6.3 and 6.4).

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The role of the different SLCFs, and also the net of all the SLCFs relative to the total warming in the scenarios, is quite different across the SSP scenarios varying with the summed levels of climate change mitigation and air pollution control (Figure 6.24). In the scenario without climate change mitigation but with strong air pollution control (SSP5-8.5) all the SLCFs (methane, aerosols and tropospheric ozone) and the HFCs (with lifetimes less than 50 years) add to the warming, while in the strong climate change and air pollution mitigation scenarios (SSP1-1.9 and SSP1-2.6), the emissions controls act to reduce methane, ozone and BC, and these reductions thus contribute to cooling. In all scenarios, except SSP3-7.0, emissions controls lead to a reduction of the aerosols relative to 2019, causing a warming. However, the warming from aerosol reductions is stronger in the SSP1 scenarios (with best estimates of 0.21°C in 2040 and 0.4°C in 2100 in SSP1-2.6) because of higher emissions reductions from stronger decrease of fossil fuel use in these scenarios than in SSP5-8.5 (0.13°C in 2040 and 0.22°C in 2100). The changes in methane abundance contribute a warming of 0.14°C in SSP5-8.5, but a cooling of 0.14°C in SSP1-2.6 by the end of the 21st century relative to 2019. Furthermore, under SSP5-8.5, HFCs induce a warming of 0.06°C with a very likely range of [0.04 to 0.08] °C in 2050 and 0.2 [0.1 to 0.3] °C by the end of the 21st century, relative to 2019, while under SSP1-2.6, warming due to HFCs is negligible (below 0.02°C) (high confidence). This assessment relies on these estimates, which are based on updated ERFs and HFC lifetimes. It is in accordance with previous estimates (Section 6.6.3.2) of the efficiency of the implementation of the Kigali Amendment and national regulations. It is very likely that under a stringent climate and air pollution mitigation scenario (SSP1-2.6), the warming induced by changes in methane, ozone, aerosols and HFCs towards the end of the 21st century, will be very low compared with the warming they would cause under the SSP5-8.5 scenario (0.14°C in SSP1-2.6 versus 0.62°C in SSP5-8.5).

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There is robust evidence and high agreement that non-methane SLCF mitigation measures, through reductions in aerosols and non-methane ozone precursors to improve air quality (SSP3-7.0-lowSLCF-highCH4 vs SSP3-7.0), would lead to additional near-term warming with a range of 0.1°C–0.3°C. Methane mitigation that also reduces tropospheric ozone, stands out as an option that combines near- and long-term gains on surface temperature (high confidence). With stringent but realistic methane mitigation (SSP3-7.0-lowSLCF-lowCH4), it is very likely that warming (relative to SSP3-7.0) from non-methane SLCFs can be offset (Figure 6.24; Allen et al., 2021). Due to the slower response to the methane mitigation, this offset becomes more robust over time and it is very likely to be an offset after 2050. However, when comparing to the present day, it is unlikely that methane mitigation can fully cancel out the warming over the 21st century from reduction of non-methane cooling SLCFs.

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Liu, M. et al., 2018: Rapid SO2 emission reductions significantly increase tropospheric ammonia concentrations over the North China Plain. Atmospheric Chemistry and Physics, 18(24), 17933–17943, doi: 10.5194/acp-18-17933-2018.

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Luo, M. et al., 2015: Satellite observations of tropospheric ammonia and carbon monoxide: Global distributions, regional correlations and comparisons to model simulations. Atmospheric Environment, 106, 262–277, doi: 10.1016/j.atmosenv.2015.02.007.

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Miyazaki, K., H.J. Eskes, and K. Sudo, 2015: A tropospheric chemistry reanalysis for the years 2005–2012 based on an assimilation of OMI, MLS, TES, and MOPITT satellite data. Atmospheric Chemistry and Physics, 15(14), 8315–8348, doi: 10.5194/acp-15-8315-2015.

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Murray, L.T., J.A. Logan, and D.J. Jacob, 2013: Interannual variability in tropical tropospheric ozone and OH: The role of lightning. Journal of Geophysical Research: Atmospheres, 118(19), 11468–11480, doi: 10.1002/jgrd.50857.

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Naik, V. et al., 2013: Preindustrial to present-day changes in tropospheric hydroxyl radical and methane lifetime from the Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP). Atmospheric Chemistry and Physics, 13(10), 5277–5298, doi: 10.5194/acp-13-5277-2013.

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Naus, S. et al., 2019: Constraints and biases in a tropospheric two-box model of OH. Atmospheric Chemistry and Physics, 19(1), 407–424, doi: 10.5194/acp-19-407-2019.

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Nicely, J.M. et al., 2017: Quantifying the causes of differences in tropospheric OH within global models. Journal of Geophysical Research: Atmospheres, 122(3), 1983–2007, doi: 10.1002/2016jd026239.

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Nicely, J.M. et al., 2018: Changes in Global Tropospheric OH Expected as a Result of Climate Change Over the Last Several Decades. Journal of Geophysical Research: Atmospheres, 123(18), 10774–10795, doi: 10. 1029/2018jd028388.

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Orbe, C. et al., 2018: Large-scale tropospheric transport in the Chemistry–Climate Model Initiative (CCMI) simulations. Atmospheric Chemistry and Physics, 18(10), 7217–7235, doi: 10.5194/acp-18-7217-2018.

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Rowlinson, M.J. et al., 2019: Impact of El Niño-Southern Oscillation on the interannual variability of methane and tropospheric ozone. Atmospheric Chemistry and Physics, 19(13), 8669–8686, doi: 10.5194/acp-19-8669-2019.

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The effective radiative forcing, ERFF; units: W m–2) quantifies the change in the net TOA energy flux of the Earth system due to an imposed perturbation (e.g., changes in greenhouse gas or aerosol concentrations, in incoming solar radiation, or land-use change). ERF is expressed as a change in net downward radiative flux at the TOA following adjustments in both tropospheric and stratospheric temperatures, water vapour, clouds, and some surface properties, such as surface albedo from vegetation changes, that are uncoupled to any GSAT change (Smith et al., 2018b). These adjustments affect the TOA energy balance and hence the ERF. They are generally assumed to be linear and additive (Section 7.3.1). Accounting for such processes gives an estimate of ERF that is more representative of the climate change response associated with forcing agents than stratospheric-temperature-adjusted radiative forcing (SARF) or the instantaneous radiative forcing (IRF; Section 7.3.1). Adjustments are processes that are independent of GSAT change, whereas feedbacks refer to processes caused by GSAT change. Although adjustments generally occur on time scales of hours to several months, and feedbacks respond to ocean surface temperature changes on time scales of a year or more, time scale is not used to separate the definitions. ERF has often been approximated as the TOA energy balance change due to an imposed perturbation in climate model simulations with sea surface temperature and sea-ice concentrations set to their pre-industrial climatological values (e.g., Forster et al., 2016). However, to match the adopted forcing–feedback framework, the small effects of any GSAT change from changes in land surface temperatures need to be removed from the TOA energy balance in such simulations to give an approximate measure of ERF (Box 7.1, Figure 1b and (Section 7.3.1).

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The ERF for a particular forcing agent is the sum of the IRF and the contribution from the adjustments, so in principle this could be constructed bottom-up by calculating the IRF and adding in the adjustment contributions one-by-one or together. However, there is no simple way to derive the global tropospheric adjustment terms or adjustments related to circulation changes without using a comprehensive climate model (e.g., CMIP5 or CMIP6). There have been two main modelling approaches used to approximate the ERF definition in Box 7.1. The first approach is to use the assumed linearity (Box 7.1, Equation 7.1) to regress the net change in the TOA radiation budget (ΔN) against change in global mean surface temperature (ΔT) following a step change in the forcing agent (Box 7.1, Figure 1; Gregory et al., 2004). The ERF (ΔF) is then derived from ΔN when ΔT= 0. Regression-based estimates of ERF depend on the temporal resolution of the data used (Modak et al., 2016, 2018). For the first few months of a simulation both surface temperature change and stratospheric-temperature adjustment occur at the same time, leading to misattribution of the stratospheric-temperature adjustment to the surface temperature feedback. Patterns of sea surface temperature (SST) change also affect estimates of the forcing obtained by regression methods (Andrews et al., 2015). At multi-decadal time scales the curvature of the relationship between net TOA radiation and surface temperature can also lead to biases in the ERF estimated from the regression method (Section 7.4; Armour et al., 2013; Andrews et al., 2015; Knutti et al., 2017). The second modelling approach to estimate ERF is to set the ΔT term in Box 7.1 (Box 7.1, Equation 7.1) to zero. It is technically difficult to constrain land surface temperatures in ESMs (Shine et al., 2003; Ackerley and Dommenget, 2016; Andrews et al., 2021), so most studies reduce the ΔT term by prescribing the SSTs and sea ice concentrations in a pair of ‘fixed-SST’ (fSST) simulations with and without the change in forcing agent (Hansen et al., 2005b). An approximation to ERF (ΔFfsst ) is then given by the difference in ΔNfsst 4 between the simulations. The fSST method has less noise due to internal variability than the regression method. Nevertheless a 30-year fSST integration or 10 × 20-year regression ensemble needs to be conducted in order to reduce the 5–95% confidence range to 0.1 W m–2(Forster et al., 2016).Neither the regression or fSST methods are practical for quantifying the ERF of agents with forcing magnitudes of the order of 0.1 W m–2 or smaller. The internal variability in the fSST method can be further constrained by nudging winds towards a prescribed climatology (Kooperman et al., 2012). This allows the determination of the ERF of forcing agents with smaller magnitudes but excludes adjustments associated with circulation responses (Schmidt et al., 2018). There are insufficient studies to assess whether these circulation adjustments are significant.

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Since the near-surface temperature change over land, ΔTland, is not constrained in the fSST method, this response needs to be removed for consistency with the (Section 7.1 definition. These changes in the near-surface temperature will also induce further responses in the tropospheric temperature and water vapour that should also be removed to conform with the physical definition of ERF. The radiative response to ΔTland can be estimated through radiative transfer modelling in which a kernel, k, representing the change in net TOA radiative flux per unit of change in near-surface temperature change over land (or an approximation using land surface temperature), is precomputed (Smith et al., 2018b, 2020b; Richardson et al., 2019; Tang et al., 2019). Thus ERF ≈ ΔFfsst kΔTland. Since k is negative this means that ΔFfsst underestimates the ERF. For 2×CO2 , this underestimate is around 0.2 W m–2(Smith et al., 2018b, 2020b). There have been estimates of the corrections due to tropospheric temperature and water vapour (Tang et al., 2019; Smith et al., 2020b) showing additional radiative responses of comparable magnitude to those directly from ΔTland. An alternative to computing the response terms directly is to use the feedback parameter, α (Hansen et al., 2005b; Sherwood et al., 2015; Tang et al., 2019). This gives approximately double the correction compared to the kernel approach (Tang et al., 2019). The response to land surface temperature change varies with location and even for GSAT change k is not expected to be the same as α Section 7.4). One study where land surface temperatures are constrained in a model (Andrews et al., 2021) finds this constraint adds +1.0 W m–2 to ΔFfsst for 4×CO2 , thus confirming the need for a correction in calculations where this constraint is not applied. For this assessment the correction is conservatively based only on the direct radiative response kernel to ΔTland as this has a strong theoretical basis to support it. While there is currently insufficient corroborating evidence to recommend including tropospheric temperature and water-vapour corrections in this assessment, it is noted that the science is progressing rapidly on this topic.

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In summary, this Report adopts an estimate of ERF based on the change in TOA radiative fluxes in the absence of GSAT changes. This allows for a theoretically cleaner separation between forcing and feedbacks in terms of factors respectively unrelated and related to GSAT change (Box 7.1). ERF can be computed from prescribed SST and sea ice experiments after removing the TOA energy budget change associated with the land surface temperature response. In this assessment this is removed using a kernel accounting only for the direct radiative effect of the land surface temperature response. To compare these results with sophisticated high spectral resolution radiative transfer models the individual tropospheric adjustment terms can be removed to leave the SARF. SARFs for 2×CO2 calculated by ESMs from this method agree within 10% with the more sophisticated models. The new studies highlighted above suggest that physical feedback parameters computed within this framework have less variation across forcing agents. There is high confidence that an α based on ERF as defined here varies by less (less than variation 10% across a range of forcing agents with global distributions), than α based on SARF. For geographically localized forcing agents there are fewer studies and less agreement between them, resulting in low confidence that ERF is a suitable estimator of the resulting global mean near-surface temperature response .

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As described in (Section 7.3.1, ERFs can be estimated using ESMs, however the radiation schemes in climate models are approximations to high spectral resolution radiative transfer models with variations and biases in results between the schemes (Pincus et al., 2015). Hence ESMs alone are not sufficient to establish ERF best estimates for the well-mixed GHGs (WMGHGs). This assessment therefore estimates ERFs from a combined approach that uses the SARF from radiative transfer models and adds the tropospheric adjustments derived from ESMs.

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The SARF for carbon dioxide (CO2) has been slightly revised due to updates to spectroscopic data and inclusion of the absorption band overlaps between N2O and CO2 (Etminan et al., 2016). The formulae fitting to the Etminan et al. (2016) results in Meinshausen et al. (2020) are used. This increases the SARF due to doubling CO2 slightly from 3.71 W m–2 in AR5 to 3.75 W m–2. Tropospheric responses to CO2 in fSST experiments have been found to lead to an approximate balance in their radiative effects between an increased radiative forcing due to water vapour, cloud and surface-albedo adjustments and a decrease due to increased tropospheric temperature and land surface temperature response (Table 7.3; Vial et al., 2013; Zhang and Huang, 2014; Smith et al., 2018b, 2020b). The ΔFfsst includes any effects represented within the ESMs on tropospheric adjustments due to changes in evapotranspiration or leaf area (mainly affecting surface and boundary-layer temperature, low-cloud amount, and albedo) from the CO2 -physiological effects (Doutriaux-Boucher et al., 2009; Cao et al., 2010; T.B. Richardson et al., 2018). The effect on surface temperature (negative longwave response) is consistent with the expected physiological responses and needs to be removed for consistency with the ERF definition. The split between surface and tropospheric temperature responses was not reported in Vial et al. (2013) or Zhang and Huang (2014) but the total of surface and tropospheric temperature response agrees with Smith et al. (2018b, 2020b), givingmedium confidence in this decomposition. Doutriaux-Boucher et al. (2009) and Andrews et al. (2021) (using the same land surface model) find a 13% and 10% increase respectively in ERF due to the physiological responses to CO2. The physiological adjustments are therefore assessed to make a substantial contribution to the overall tropospheric adjustment for CO2 (high confidence), but there is insufficient evidence to provide a quantification of the split between physiological and thermodynamic adjustments. These forcing adjustments due to the effects of CO2 on plant physiology differ from the biogeophysical feedbacks due to the effects of temperature changes on vegetation discussed in (Section 7.4.2.5. The adjustment is assumed to scale with the SARF in the absence of evidence for non-linearity. The tropospheric adjustment is assessed from Table 7.3 to be +5% of the SARF with an uncertainty of 5%, which is added to the Meinshausen et al. (2020) formula for SARF. Due to the agreement between the studies and the understanding of the physical mechanisms there is medium confidence in the mechanisms underpinning the tropospheric adjustment, but low confidence in its magnitude .

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The ERF from doubling CO2 (2×CO2) from the 1750 level (278 ppm; Section 2.2.3.3) is assessed to be 3.93 ± 0.47 W m–2(high confidence). Its assessed components are given in Table 7.4. The combined spectroscopic and radiative transfer modelling uncertainties give an uncertainty in the CO2 SARF of around 10% or less (Etminan et al., 2016; Mlynczak et al., 2016). The overall uncertainty in CO2 ERF is assessed as ±12%, as the more uncertain adjustments only account for a small fraction of the ERF (Table 7.3). The 2×CO2 ERF estimate is 0.2 W m–2 larger than using the AR5 formula (Myhre et al., 2013b) due to the combined effects of tropospheric adjustments which were assumed to be zero in AR5. CO2 concentrations have increased from 278 ppm in 1750 to 410 ppm in 2019 Section 2.2.3.3). The historical ERF estimate from CO2 is revised upwards from the AR5 value of 1.82 ± 0.38 W m–2(1750–2011) to 2.16 ± 0.26 W m–2(1750–2019) in this assessment, from a combination of the revisions described above (0.06 W m–2) and the 19 ppm rise in atmospheric concentrations between 2011 and 2019 (0.27 W m–2). The ESM estimates of 2×CO2 ERF (Table 7.2) lie within ±12% of the assessed value (apart from CESM2). The definition of ERF can also include further physiological effects – for instance on dust, natural fires and biogenic emissions from the land and ocean – but these are not typically included in the modelling setup for 2×CO2 ERF.

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The SARF for methane (CH4) has been substantially increased due to updates to spectroscopic data and inclusion of shortwave absorption (Etminan et al., 2016). Adjustments have been calculated in nine climate models by Smith et al. (2018b). Since CH4 is found to absorb in the shortwave near infrared, only adjustments from those models including this absorption are taken into account. For these models the adjustments act to reduce the ERF because the shortwave absorption leads to tropospheric heating and reductions in upper tropospheric cloud amounts. The adjustment is –14% ± 15%, which counteracts much of the increase in SARF identified by Etminan et al. (2016). Modak et al. (2018) also found negative forcing adjustments from a methane perturbation including shortwave absorption in the NCAR CAM5 model, in agreement with the above assessment. The uncertainty in the shortwave component leads to a higher radiative modelling uncertainty (14%) than for CO2 (Etminan et al., 2016). When combined with the uncertainty in the adjustment, this gives an overall uncertainty of ±20%. There is high confidence in the spectroscopic revision but only medium confidence in the adjustment modification. CH4 concentrations have increased from 729 ppb in 1750 to 1866 ppb in 2019 Section 2.2.3.3). The historical ERF estimate from AR5 of 0.48 ± 0.10 W m–2(1750–2011) is revised to 0.54 ± 0.11 W m–2(1750 to 2019) in this assessment from a combination of spectroscopic radiative efficiency revisions (+0.12 W m–2), adjustments (–0.08 W m–2) and the 63 ppb rise in atmospheric CH4 concentrations between 2011 and 2019 (+0.03 W m–2). As the adjustments are assessed to be small, there is high confidence in the overall assessment of ERF from methane. Increased methane leads to tropospheric ozone production and increased stratospheric water vapour, so that an attribution of forcing to methane emissions gives a larger effect than that directly from the methane concentration itself. This is discussed in detail in (Chapter 6 (Section 6.4.2) and shown in Figure 6.12.

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The tropospheric adjustments to nitrous oxide (N2O) have been calculated from 5 ESMs as 7% ± 13% of the SARF (Hodnebrog et al., 2020b). This value is therefore taken as the assessed adjustment, but with low confidence. The radiative modelling uncertainty is ±10% (Etminan et al., 2016), giving an overall uncertainty of ±16%. Nitrous oxide concentrations have increased from 270 ppb in 1750 to 332 ppb in 2019 Section 2.2.3.3). The historical ERF estimate from N2O is revised upwards from 0.17 ± 0.06 W m–2(1750–2011) in AR5 to 0.21 ± 0.03 W m–2(1750–2019) in this assessment, of which 0.02 W m–2 is due to the 7 ppb increase in concentrations, and 0.02 W m–2 to the tropospheric adjustment. As the adjustments are assessed to be small there remains high confidence in the overall assessment.

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The tropospheric adjustments to chlorofluorocarbons (CFCs), specifically CFC-11 and CFC-12, have been quantified as 13% ± 10% and 12% ± 14% of the SARF, respectively (Hodnebrog et al., 2020b). The assessed adjustment to CFCs is therefore 12% ± 13% with low confidence due to the lack of corroborating studies. There have been no calculations for other halogenated species so for these the tropospheric adjustments are therefore assumed to be 0 ± 13% with low confidence. The radiative modelling uncertainties are 14% and 24% for compounds with lifetimes greater than and less than five years, respectively (Hodnebrog et al., 2020a). The overall uncertainty in the ERFs of halogenated compounds is therefore assessed to be 19% and 26% depending on the lifetime. The ERF from CFCs is slowly decreasing, but this is compensated for by the increased forcing from the replacement species (HCFCs and HFCs). The ERF from HFCs has increased by 0.028 ± 0.05 W m–2. Thus, the concentration changes mean that the total ERF from halogenated compounds has increased since AR5 from 0.360 ± 0.036 W m–2 to 0.408 ± 0.078 W m–2(Table 7.5). Of this, 0.034 W m–2 is due to increased radiative efficiencies and tropospheric adjustments, and 0.014 W m–2 is due to increases in concentrations. As the adjustments are assessed to be small there remains high confidence in the overall assessment.

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Estimates of the pre-industrial to present-day tropospheric ozone radiative forcing are based entirely on models. The lack of pre-industrial ozone measurements prevents an observational determination. There have been limited studies of ozone ERFs (MacIntosh et al., 2016; Xie et al., 2016; Skeie et al., 2020). Skeie et al. (2020) found little net contribution to the ERF from tropospheric adjustment terms for 1850–2000 change in ozone (tropospheric and stratospheric ozone combined), although MacIntosh et al. (2016) suggested that increases in stratospheric or upper tropospheric ozone reduces high-cloud and increases low-cloud, whereas an increase in lower tropospheric ozone reduces low-cloud. Further studies suggest that changes in circulation due to decreases in stratospheric ozone affect Southern Hemisphere clouds and the atmospheric levels of sea salt aerosol that would contribute additional adjustments, possibly of comparable magnitude to the SARF from stratospheric ozone depletion (Grise et al., 2013, 2014; Xia et al., 2016, 2020). ESM responses to changes in ozone-depleting substances (ODS) in CMIP6 show a much more negative ERF than would be expected from offline calculations of SARF (Morgenstern et al., 2020; Thornhill et al., 2021b) again suggesting a negative contribution from adjustments. However there is insufficient evidence available to quantify this effect.

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Without sufficient information to assess whether the ERFs differ from SARF, this assessment relies on offline radiative transfer calculations of SARF for both tropospheric and stratospheric ozone. Checa-Garcia et al. (2018) found SARF of 0.30 W m–2 for changes in ozone (1850–1860 to 2009–2014). These were based on precursor emissions and ODS concentrations from the Coupled Chemistry Model Initiative (CCMI) project (Morgenstern et al., 2017). Skeie et al. (2020) calculated an ozone SARF of 0.41 ± 0.12 W m–2(1850–2010; from five climate models and one chemistry transport model) using CMIP6 precursor emissions and ODS concentrations (excluding models without fully interactive ozone chemistry and one model with excessive ozone depletion). The ozone precursor emissions are higher in CMIP6 than in CCMI, which explains much of the increase compared to Checa-Garcia et al. (2018).

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Previous assessments have split the ozone forcing into tropospheric and stratospheric components. This does not correspond to the division between ozone production and ozone depletion and is sensitive to the choice of tropopause (high confidence) (Myhre et al., 2013b). The contributions to total SARF in CMIP6 (Skeie et al., 2020) are 0.39 ± 0.07 and 0.02 ± 0.07 W m–2 for troposphere and stratosphere respectively (using a 150 ppb ozone tropopause definition). This small positive (but with uncertainty encompassing negative values) stratospheric ozone SARF is due to contributions from ozone precursors to lower stratospheric ozone and some of the CMIP6 models showing ozone depletion in the upper stratosphere, where depletion contributes a positive radiative forcing (medium confidence).

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The fractional variability in the solar irradiance, over the solar cycle and between solar cycles, is much greater at short wavelengths in the 200–400 nanometre (nm) band than for the broad visible/infrared band that dominates TSI (Krivova et al., 2006). The IRF can be derived simply by ΔTSI× (1 – albedo)/4 irrespective of wavelength, where the best estimate of the planetary albedo is usually taken to be 0.29 and ΔTSIrepresents the change in total solar irradiance (Stephens et al., 2015). (The factor 4 arises because TSI is per unit area of Earth cross section presented to the Sun and IRF is per unit area of Earth’s surface). The adjustments are expected to be wavelength dependent. Gray et al. (2009) determined a stratospheric temperature adjustment of –22% to spectrally resolved changes in the solar radiance over one solar cycle. This negative adjustment is due to stratospheric heating from increased absorption by ozone at the short wavelengths, increasing the outgoing longwave radiation to space. A multi-model comparison (Smith et al., 2018b) calculated adjustments of –4% due to stratospheric temperatures and –6% due to tropospheric processes (mostly clouds), for a change in TSI across the spectrum (Figure 7.4). The smaller magnitude of the stratospheric temperature adjustment is consistent with the broad spectral change rather than the shorter wavelengths characteristic of solar variation. A single-model study also found an adjustment that acts to reduce the forcing (Modak et al., 2016). While there has not yet been a calculation based on the appropriate spectral change, the –6% tropospheric adjustment from Smith et al. (2018b) is adopted along with the Gray et al. (2009) stratospheric temperature adjustment. The ERF due to solar variability over the historical period is therefore represented by 0.72 × ΔTSI× (1 – albedo)/4 using the TSI timeseries from (Chapter 2 Section 2.2.1).

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The AR5 (Myhre et al., 2013b) assessed solar SARF from around 1750 to 2011 to be 0.05 [0.00 to 0.10] W m–2 which was computed from the seven-year mean around the solar minima in 1745 (being closest to 1750) and 2008 (being the most recent solar minimum). The inclusion of tropospheric adjustments that reduce ERF (compared to SARF in AR5) has a negligible effect on the overall forcing. Prior to the satellite era, proxy records are used to reconstruct historical solar activity. In AR5, historical records were constructed using observations of solar magnetic features. In this assessment historical time series are constructed from radiogenic compounds in the biosphere and in ice cores that are formed from cosmic rays (Steinhilber et al., 2012).

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There is large episodic negative radiative forcing associated with sulphur dioxide (SO­­2) being ejected into the stratosphere from explosive volcanic eruptions, accompanied by more frequent smaller eruptions (Figure 2.2 and Cross-Chapter Box 4.1). From SO2 gas, reflective sulphate aerosol is formed in the stratosphere where it may persist for months to years, reducing the incoming solar radiation. The volcanic SARF in AR5 (Myhre et al., 2013b) was derived by scaling the stratospheric aerosol optical depth (SAOD) by a factor of –25 W m–2 per unit SAOD from Hansen et al. (2005b). Quantification of the adjustments to SAOD perturbations from climate model simulations have determined a significant positive adjustment driven by a reduction in cloud amount (Figure 7.4; Marshall et al., 2020). Analysis of CMIP5 models provides a mean ERF of –20 W m–2 per unit SAOD (Larson and Portmann, 2016). Single-model studies with successive generations of Hadley Centre climate models produce estimates between –17 and –19 W m–2 per unit SAOD (Gregory et al., 2016; Marshall et al., 2020), with some evidence that ERF may be non-linear with SAOD for large eruptions (Marshall et al., 2020). Analysis of the volcanically active periods of 1982–1985 and 1990–1994 using the CESM1(WACCM) aerosol–climate model provided an SAOD-to-ERF relationship of –21.5 (± 1.1) W m–2 per unit SAOD (Schmidt et al., 2018). Volcanic SO2 emissions may contribute a positive forcing through effects on upper tropospheric ice clouds, due to additional ice nucleation on volcanic sulphate particles (Friberg et al., 2015; Schmidt et al., 2018), although one observational study found no significant effect (Meyer et al., 2015). Due to low agreement, the contribution of sulphate aerosol effects on ice clouds to volcanic ERF is not included in the overall assessment.

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The AR5 introduced the concept of effective radiative forcing (ERF) and radiative adjustments, and made a preliminary assessment that the tropospheric adjustments were zero for all species other than the effects of aerosol–cloud interaction and black carbon. Since AR5, new studies have allowed for a tentative assessment of values for tropospheric adjustments to CO2, CH4, N2O, some CFCs, solar forcing, and stratospheric aerosols, and to place a tighter constraint on adjustments from aerosol–cloud interaction (Sections 7.3.2, 7.3.3 and 7.3.4). In AR6, the definition of ERF explicitly removes the land-surface temperature change as part of the forcing, in contrast to AR5 where only sea surface temperatures were fixed. The ERF is assessed to be a better predictor of modelled equilibrium temperature change (i.e., less variation in feedback parameter) than SARf (Section 7.3.1).

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As discussed in (Section 7.3.2, the radiative efficiencies for CO2, CH4 and N2O have been updated since AR5 (Etminan et al., 2016). There has been a small (1%) increase in the stratospheric-temperature-adjusted CO2 radiative efficiency, and a +5% tropospheric adjustment has been added. The stratospheric-temperature-adjusted radiative efficiency for CH4 is increased by approximately 25% (high confidence). The tropospheric adjustment is tentatively assessed to be –14% (low confidence). A +7% tropospheric adjustment has been added to the radiative efficiency for N2O and +12% to CFC-11 and CFC-12 (low confidence).

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The CC feedback has a large positive value due to well understood thermodynamic and radiative processes: α CC= 1.36 ± 0.04 W m–2°C–1(one standard deviation; Held and Shell, 2012; Zelinka et al., 2020). The lapse-rate feedback assuming a constant relative humidity (LR*) in CMIP6 models has small absolute values ( α LR*= –0.10 ± 0.07 W m–2°C–1(one standard deviation)), as expected from theoretical arguments (Ingram, 2010, 2013). It includes the pattern effect of surface warming that modulates the lapse rate and associated specific humidity changes (Po-Chedley et al., 2018b). The relative humidity feedback is close to zero ( α RH= 0.00 ± 0.06 W m–2°C–1(one standard deviation)) and the spread among models is confined to the tropics (Sherwood et al., 2010b; Vial et al., 2013; Takahashi et al., 2016; Po-Chedley et al., 2018b). The change in upper tropospheric RH is closely related to model representation of current climate (Sherwood et al., 2010b; Po-Chedley et al., 2019), and a reduction in model RH biases is expected to reduce the uncertainty of the RH feedback. At interannual time scales, it has been shown that the change in RH in the tropics is related to the change of the spatial organization of deep convection (Holloway et al., 2017; Bony et al., 2020).

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Clouds have various types, from optically thick convective clouds to thin stratus and cirrus clouds, depending upon thermodynamic conditions and large-scale circulation (Figure 7.9). Over the equatorial warm pool and inter-tropical convergence zone (ITCZ) regions, high SSTs stimulate the development of deep convective cloud systems, which are accompanied by anvil and cirrus clouds near the tropopause where the convective air outflows. The large-scale circulation associated with these convective clouds leads to subsidence over the subtropical cool ocean, where deep convection is suppressed by a lower tropospheric inversion layer maintained by the subsidence and promoting the formation of shallow cumulus and stratocumulus clouds. In the extratropics, mid-latitude storm tracks control cloud formation, which occurs primarily in the frontal bands of extratropical cyclones. Since liquid droplets do not freeze spontaneously at temperatures warmer than approximately –40°C and ice nucleating particles that can aid freezing at warmer temperatures are scarce (see (Section 7.3.3), extratropical clouds often consist both of super-cooled liquid and ice crystals, resulting in mixed-phase clouds.

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Feedback parameters in climate models are calculated assuming that they are independent of each other, except for a well-known co-dependency between the water vapour (WV) and lapse rate (LR) feedbacks. When the inter-model spread of the net climate feedback is computed by adding in quadrature the inter-model spread of individual feedbacks, it is 17% wider than the spread of the net climate feedback directly derived from the ensemble. This indicates that the feedbacks in climate models are partly co-dependent. Two possible co-dependencies have been suggested (Huybers, 2010; Caldwell et al., 2016). One is a negative covariance between the LR and longwave cloud feedbacks, which may be accompanied by a deepening of the troposphere (O’Gorman and Singh, 2013; Yoshimori et al., 2020) leading both to greater rising of high-clouds and a larger upper-tropospheric warming. The other is a negative covariance between albedo and shortwave cloud feedbacks, which may originate from the Arctic regions: a reduction in sea ice enhances the shortwave cloud radiative effect because the ocean surface is darker than sea ice (Gilgen et al., 2018). This covariance is reinforced as the decrease of sea ice leads to an increase in low-level clouds (Mauritsen et al., 2013). However, the mechanism causing these co-dependences between feedbacks is not well understood yet and a quantitative assessment based on multiple lines of evidence is difficult. Therefore, this synthesis assessment does not consider any co-dependency across individual feedbacks.

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While the contributions to regional warming can be diagnosed within ESM simulations (Figure 7.12), assessment of the underlying role of individual factors is limited by interactions inherent to the coupled climate system. For example, polar feedback processes are coupled and influenced by warming at lower latitudes (Screen et al., 2012; Alexeev and Jackson, 2013; Graversen et al., 2014; Graversen and Burtu, 2016; Rose and Rencurrel, 2016; Feldl et al., 2017a, 2020; Yoshimori et al., 2017; Garuba et al., 2018; Po-Chedley et al., 2018b; Stuecker et al., 2018; Dai et al., 2019), while atmospheric heat transport changes are in turn influenced by the latitudinal structure of regional feedbacks, radiative forcing, and ocean heat uptake (Hwang et al., 2011; Zelinka and Hartmann, 2012; Feldl and Roe, 2013; Huang and Zhang, 2014; Merlis, 2014; Rose et al., 2014; Roe et al., 2015; Feldl et al., 2017b; Stuecker et al., 2018; Armour et al., 2019). The use of different feedback definitions, such as a lapse-rate feedback partitioned into upper and lower tropospheric components (Feldl et al., 2020) or including the influence of water vapour at constant relative humidity (Held and Shell, 2012; Section 7.4.2), would also change the interpretation of which feedbacks contribute most to polar amplification.

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The radiation changes most sensitive to warming patterns are those associated with low-cloud cover (affecting global albedo) and the tropospheric temperature profile (affecting thermal emission to space) (Ceppi and Gregory, 2017; Zhou et al., 2017b; Andrews et al., 2018; Dong et al., 2019). The mechanisms and radiative effects of these changes are illustrated in Figure 7.14a,b. SSTs in regions of deep convective ascent (e.g., in the western Pacific warm pool) govern the temperature of the tropical free troposphere and, in turn, affect low-clouds through the strength of the inversion that caps the boundary layer (i.e., the lower-tropospheric stability) in subsidence regions (Wood and Bretherton, 2006; Klein et al., 2017). Surface warming within ascent regions thus warms the free troposphere and increases low-cloud cover, causing an increase in emission of thermal radiation to space and a reduction in absorbed solar radiation. In contrast, surface warming in regions of overall descent preferentially warms the boundary layer and enhances convective mixing with the dry free troposphere, decreasing low-cloud cover (Bretherton et al., 2013; Qu et al., 2014; Zhou et al., 2015). This leads to an increase in absorption of solar radiation but little change in thermal emission to space. Consequently, warming in tropical ascent regions results in negative lapse-rate and cloud feedbacks while warming in tropical descent regions results in positive lapse-rate and cloud feedbacks (Figure 7.14; Rose and Rayborn, 2016; Zhou et al., 2017b; Andrews and Webb, 2018; Dong et al., 2019). Surface warming in mid-to-high latitudes causes a weak radiative response owing to compensating changes in thermal emission (Planck and lapse-rate feedbacks) and absorbed solar radiation (shortwave cloud and surface-albedo feedbacks; Rose and Rayborn, 2016; Dong et al., 2019), however this compensation may weaken due to less-negative shortwave cloud feedbacks at high warming (Frey and Kay, 2018; Bjordal et al., 2020; Dong et al., 2020).

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A similar behaviour is seen within transient simulations of coupled ESMs, which project SST warming patterns that are initially characterized by relatively large warming rates in the western equatorial Pacific Ocean on decadal time scales and relatively large warming in the eastern equatorial Pacific and Southern Ocean on centennial time scales (Andrews et al., 2015; Proistosescu and Huybers, 2017; Dong et al., 2020). Recent studies based on simulations of 1% yr–1CO2 increase (1pctCO2 ) orabrupt 4xCO2 as analogues for historical warming suggest characteristic values of α = +0.05 W m–2°C–1(–0.2 to +0.3 W m–2°C–1range across models) based on CMIP5 and CMIP6 ESMs (Armour 2017, Lewis and Curry 2018, Dong et al. 2020). Using historical simulations of one CMIP6 ESM (HadGEM3-GC3.1-LL), Andrews et al. (2019) find an average feedback parameter change of α = +0.2 W m–2°C–1(–0.2 to +0.6 W m–2°C–1range across four ensemble members). Using historical simulations from another CMIP6 ESM (GFDL CM4.0), Winton et al. (2020) find an average feedback parameter change of α = +1.5 W m–2°C–1(+1.2 to +1.7 W m–2°C–1range across three ensemble members). This value is larger than The α = +0.7 W m–2°C–1 within GFDL CM4.0 for historical CO2 forcing only, suggesting that the value of α may depend on historical non-CO2 forcings such as those associated with tropospheric and stratospheric aerosols (Marvel et al., 2016; Gregory et al., 2020; Winton et al., 2020).

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Energy budget estimates of TCR and ECS have evolved in the literature over recent decades. Prior to AR4, the global energy budget provided relatively weak constraints, primarily due to large uncertainty in the tropospheric aerosol forcing, giving ranges of the effective ECS that typically included values above 10°C (Forster, 2016; Knutti et al., 2017). Revised estimates of aerosol forcing together with a larger greenhouse gas forcing by the time of AR5 led to an estimate of ΔF that was more positive and with reduced uncertainty relative to AR4. Using energy budget estimates and radiative forcing estimates updated to 2009, Otto et al. (2013) estimated that TCR was 1.3 [0.9 to 2.0] °C, and that the effective ECS was 2.0 [1.2 to 3.9] °C. This AR5-based energy budget estimate of ECS was lower than estimates based on other lines of evidence, leading AR5 to expand the assessed likely range of ECS to include lower values relative to AR4. Studies since AR5 using similar global energy budget methods have produced similar or slightly narrower ranges for TCR and effective ECS (Forster, 2016; Knutti et al., 2017).

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Finding such correlations within models requires simulations that span multiple centuries, suggesting that the satellite record may not be of sufficient length to produce robust feedback estimates. However, correlations between regression-based feedbacks and long-term feedbacks have been found to be higher when focused on specific processes or regions, such as for the cloud- or water-vapour feedbacks (Section 7.4.2; Dessler, 2013; Zhou et al., 2015). Assessing the global radiative feedback in terms of the more stable relationship between tropospheric temperature and TOA radiation offers another potential avenue for constraining ECS. The ‘emergent constraints’ on ECS based on variability in the TOA energy budget are assessed in (Section 7.5.4.1.

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Short-term variations in the TOA energy budget, observable from satellites, arising from variations in the tropical tropospheric temperature have been linked to ECS through models, either as a range of models consistent with observations (those with ECS values between 2.0°C and 3.9°C; Dessler et al., 2018) or as a formal emergent constraint by deriving further model-based relationships to yield a median of 3.3 [2.4 to 4.5] °C (Dessler and Forster, 2018). There are major challenges associated with short-term variability in the energy budget, in particular how it relates to the long-term forced response of clouds (Colman and Hanson, 2017; Lutsko and Takahashi, 2018). Variations in the surface temperature that are not directly affecting the radiation balance lead to an overestimated ECS when using linear regression techniques where it appears as noise in the independent variable (Proistosescu et al., 2018; Gregory et al., 2020). The latter issue is largely overcome when using the tropospheric mean or mid-tropospheric temperature (Trenberth et al., 2015; Dessler et al., 2018).

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The available emergent constraint studies have been divided into two classes: (i) those that are based on global or near-global indices, such as global surface temperature and the TOA energy budget; and (ii) those that are more focussed on physical processes, such as the fidelity of phenomena related to low-level cloud feedbacks or present-day climate biases. The former class is arguably superior in representing ECS, since it is a global surface temperature or energy budget change, whereas the latter class is perhaps best thought of as providing constraints on individual climate feedbacks, for example, the determination that low-level cloud feedbacks are positive. The latter result is consistent with and confirms process-based estimates of low-cloud feedbacks (Section 7.4.2.4), but are potentially biased as a group by missing or biased feedbacks in ESMs and is accordingly not taken into account here. A limiting case here is Dessler and Forster (2018) which is focused on monthly co-variability in the global TOA energy budget with mid-tropospheric temperature, at which time scale the surface-albedo feedback is unlikely to operate, thus implicitly assuming it is unbiased in the model ensemble.

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The radiative properties and lifetimes of compounds are the fundamental component of all emissions metrics. Since AR5, there have been advances in the understanding of the radiative properties of various compounds (see Sections 7.3.1, 7.3.2 and 7.3.3), and hence their effective radiative efficiencies (ERFs per unit change in concentration). For CO2, CH4 and N2O, better accounting of the spectral properties of these gases has led to re-evaluation of their stratospheric-temperature-adjusted radiative forcing (SARF) radiative efficiencies and their dependence on the background gas concentrations (Section 7.3.2). For CO2, CH4, N2O, CFC-11 and CFC-12 the tropospheric adjustments (Sections 7.3.1 and 7.3.2) are assessed to make a non-zero contribution to ERF. There is insufficient evidence to include tropospheric adjustments for other halogenated compounds. The re-evaluated effective radiative efficiency for CO2 will affect all emissions metrics relative to CO2.

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The effective radiative efficiencies (including adjustments from (Section 7.3.2) for 2019 background concentrations for CO2, CH4 and N2O are assessed to be 1.33×10–5, 3.89×10–4and 3.19×10–3W m–2 ppb–1respectively (see Table 7.15 for uncertainties), compared to AR5 assessments of 1.37×10–5, 3.63×10–4and 3.00×10–3W m–2 ppb–1. For CO2, increases due to the adjustments do not quite balance the decreases due to the increasing background concentration. For CH4, increases due to the re-evaluated radiative properties more than offset the decreases due to the increasing background concentration. For N2O the addition of tropospheric adjustments increases the effective radiative efficiency. Radiative efficiencies of halogenated species have been revised slightly (Section 7.3.2.4) and for CFCs include tropospheric adjustments.

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Methane can also affect the oxidation pathways of aerosol formation (Shindell et al., 2009) but the available literature is insufficient to make a robust assessment of this. Hydrocarbon and molecular hydrogen oxidation also leads to tropospheric ozone production and change in methane lifetime (Collins et al., 2002; Hodnebrog et al., 2018). For reactive species the emissions metrics can depend on where the emissions occur, and the season of emission (Aamaas et al., 2016; Lund et al., 2017; Persad and Caldeira, 2018). The AR5 included a contribution to the emissions metrics for ozone-depleting substances (ODSs) from the loss of stratospheric ozone. The assessment of ERFs from ODSs in (Chapter 6 (Section 6.4.2) suggests the quantification of these terms may be more uncertain than the formulation in AR5 so these are not included here.

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Wood, R. and C.S. Bretherton, 2006: On the Relationship between Stratiform Low Cloud Cover and Lower-Tropospheric Stability. Journal of Climate, 19(24), 6425–6432, doi: 10.1175/jcli3988.1.

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Wyant, M.C. et al., 2006: A comparison of low-latitude cloud properties and their response to climate change in three AGCMs sorted into regimes using mid-tropospheric vertical velocity. Climate Dynamics, 27(2–3), 261–279, doi: 10.1007/s00382-006-0138-4.

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Xie, B., H. Zhang, Z. Wang, S. Zhao, and Q. Fu, 2016: A modeling study of effective radiative forcing and climate response due to tropospheric ozone. Advances in Atmospheric Sciences, 33(7), 819–828, doi: 10.1007/s00376-016-5193-0.

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Elevation-dependent warming could speed up the observed, rapid upward shifts of the freezing level height (FLH) in several mountainous regions of the world and lead to faster changes in the snowline, the glacier equilibrium-line altitude and the snow/rain transition height (high confidence). In the Indus, Ganges and Brahmaputra basins in Asia, the FLH is projected to rise at a rate of 4.4 to 10.0 m yr–1under RCP8.5 (Viste and Sorteberg, 2015). In the Argentinian Andes, FLH is projected under RCP8.5 to move up more than twice as much by 2070 as during the entire Holocene under the worst case scenario (Drewes et al., 2018). On the western slope of the subtropical Andes (30°S–38°S) in central Chile, the mean value of the free tropospheric height of the 0°C isotherm under wet conditions is projected to be close to or higher than the upper quartile of the distribution in the current climate, towards the end of the century and under RCP8.5 (Mardones and Garreaud, 2020). In the Alps and the Pyrenees, Spandre et al. (2019) projected a rise in the natural snow elevation of between 200–300 m and 400–600 m by mid-century under RCP2.6 and RCP8.5, respectively. In the same region, the environmental equilibrium-line altitude is projected to exceed the maximum elevation of 69%, 81% and 92% of the glaciers by the end of the century under RCPs 2.6, 4.5 and 8.5, respectively (Žebre et al., 2021).

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Hayes, F., K. Sharps, H. Harmens, I. Roberts, and G. Mills, 2020: Tropospheric ozone pollution reduces the yield of African crops. Journal of Agronomy and Crop Science, 206(2), 214–228, doi: 10.1111/jac.12376.

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Mardones, P. and R.D. Garreaud, 2020: Future Changes in the Free Tropospheric Freezing Level and Rain–Snow Limit: The Case of Central Chile. Atmosphere, 11(11), 1259, doi: 10.3390/atmos11111259.

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Ren, W. et al., 2011: Impacts of tropospheric ozone and climate change on net primary productivity and net carbon exchange of China’s forest ecosystems. Global Ecology and Biogeography, 20(3), 391–406, doi: 10.1111/j.1466-8238.2010.00606.x.

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Tropospheric (i.e., the lowest 6–10 km of the atmosphere) ozone exacerbates negative impacts of climate change (high confidence) (Mattos et al., 2014; Chuwah et al., 2015; McGrath et al., 2015; Bisbis et al., 2018; Mills et al., 2018; Scheelbeek et al., 2018). Ozone is an air pollutant and short-lived GHG that affects air quality and global climate. It is a strong oxidant that reduces physiological functions, yield and quality of crops and animals. Surface ozone concentration has increased substantially since the late 19th century (Cooper et al., 2014; Forster et al., 2021; Gulev et al., 2021; Szopa et al., 2021) and in some locations and times reaches levels that harm plants, animals and human (high confidence) (Fleming et al., 2018).

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Climate change impacts on productivity of agroforestry systems are similar to individual perennial crops, although there is limited research on tree crops (see Section 5.4.1.2). Impacts include increased temperature or water stress, an increase in pathogens affecting crops, changes to pollinator abundance, and changes in the nutrient content of one or more of the agroforestry components. Many tree products such as fruits and nuts are grown in agroforestry settings. The quality and nutrition of these products and other specialty crops are often negatively affected by rising temperatures, ambient CO2 concentrations and tropospheric ozone (Ahmed and Stepp, 2016). There is also evidence that the fungus coffee rust will be positively affected by climate change (Avelino et al., 2015; Bebber et al., 2016), with adverse effects on coffee agroforestry systems.

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Climate change is increasing the number of people experiencing food insecurity through greater incidence and severity of climatic impact drivers (CIDs), (Seneviratne et al., 2021) such as extreme heat, drought and floods. Increasing CO2 concentrations have positive effects on food and forage crops by enhancing photosynthesis and alleviating drought stresses (5.4.3.1, 5.5.3.1) but have negative effects on nutrient concentrations in food crops. Ocean acidification is also caused by increasing CO2, causing negative impacts on aquatic systems. Tropospheric ozone concentrations already hinder crop production (Section 5.4.1.4). Several CIDs increase the number of people experiencing food insecurity (high confidence) (SROCC 2019, FAO et al., 2018; Mbow et al., 2019; Baker and Anttila-Hughes, 2020; Table 5.12).

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Cooper, O.R., et al., 2014: Global distribution and trends of tropospheric ozone: an observation-based review. Elem. Sci. Anthropocene, 2, 29, doi:10.12952/journal.elementa.000029.

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Fleming, Z.L., et al., 2018: Tropospheric ozone assessment report: present-day ozone distribution and trends relevant to human health. Elem. Sci. Anthropocene, 6, 12, doi:10.1525/elementa.273.

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Hickman, J.E., et al., 2017: Nonlinear response of nitric oxide fluxes to fertilizer inputs and the impacts of agricultural intensification on tropospheric ozone pollution in Kenya. Glob Chang Biol, 23 (8), 3193–3204, doi:10.1111/gcb.13644.

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Turnock, S.T., et al., 2018: The impact of future emission policies on tropospheric ozone using a parameterised approach. Atmos. Chem. Phys. , 18 (12), 8953–8978, doi:10.5194/acp-18-8953-2018.

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Ainsworth, E. A. et al., 2012: The Effects of Tropospheric Ozone on Net Primary Productivity and Implications for Climate Change. Annual Review of Plant Biology, 63, 637–661, doi:10.1146/annurev-arplant-042110-103829.

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Arnold, S.R., et al., 2018: Simulated Global Climate Response to Tropospheric Ozone-Induced Changes in Plant Transpiration. Geophys. Res. Lett. , 45 (23), 13070–13079, doi:10.1029/2018GL079938.

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King, J., L. Liu and M. Aspinwall, 2013: Chapter 9 - Tree and Forest Responses to Interacting Elevated Atmospheric CO2 and Tropospheric O3: A Synthesis of Experimental Evidence. In: Developments in Environmental Science[Matyssek, R., et al.(ed.)]. Elsevier, Amsterdam, pp. 179–208 (13).

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Xia, L., P.J. Nowack, S. Tilmes and A. Robock, 2017: Impacts of stratospheric sulfate geoengineering on tropospheric ozone. Atmos. Chem. Phys. , 17 (19), 11913–11928, doi:10.5194/acp-17-11913-2017.

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Fleming, Z.L., et al., 2018: Tropospheric Ozone Assessment Report: Present-day ozone distribution and trends relevant to human health. Elem. Sci. Anthropocene, 6 (1).

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Short-lived climate forcers (SLCFs) also play an important role in climate change, certainly for short-term changes (AR6 WGI, Figure SPM.2) (Shindell et al. 2012). These forcers consist of (i) substances contributing to warming, such as methane, black carbon and tropospheric ozone, and (ii) substances contributing to cooling (other aerosols, such as related to sulphur emissions). Most SLCFs are also air pollutants, and reducing their emissions provides additional co-benefits (Shindell et al. 2017a,b; Hanaoka and Masui 2020). In the case of the first group, emission reduction thus leads to both air pollution and climate benefits. For the second, group there is a possible trade-off (Shindell and Smith 2019; Lund et al. 2020). As aerosol emissions are mostly associated with fossil fuel combustion, the benefits of reducing CO2 could, in the short term, be reduced as a result of lower aerosol cooling. There has been an active discussion on the exact climate contribution of SLCF-focused policies in the literature. This discussion partly emerged from different assumptions on possible reductions in the absence of ambitious climate policy and the uncertain global climate benefit from aerosol (black carbon) (Rogelj et al. 2014). The latter is now assessed to be smaller than originally thought (Takemura and Suzuki 2019; Smith et al. 2020b) (see also AR6 WGI Section 6.4). Reducing SLCF emissions is critical to meet long-term climate goals and might help reduce the rate of climate change in the short term. Deep SLCF emission reductions also increase the remaining carbon budget for a specific temperature goal (Rogelj et al. 2015a; Reisinger et al. 2021) (Box 3.4). A more detailed discussion can be found in AR6 WGI Chapters 5 and 6.

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The relationship between warming limits and near-term action is also affected by the warming contribution of non-CO2 greenhouse gases and other short-lived climate forcers (Section 3.3; AR6 WGI Section 6.7). The estimated budget values for limiting warming to 1.5°C–2°C already assume stringent reductions in non-CO2 greenhouse gases and non-CO2 climate forcing as found in 1.5°C–2°C pathways (Section 3.3 and Cross-Working Group Box 1 in this chapter; AR6 WGI Section 5.5 and Box 5.2 in Chapter 5). Further variations in non-CO2 warming observed across 1.5°C–2°C pathways can vary the median estimate for the remaining carbon budget by 220 GtCO2 (AR6 WGI Section 5.5). In 1.5°C–2°C pathways, the non-CO2 warming contribution differs strongly between the near, medium and long term. Changes to the atmospheric composition of short-lived climate forcers (SLCFs) dominate the warming response in the near term (AR6 WGI Section 6.7). CO2 reductions are combined with strong reductions in air pollutant emissions due to rapid reduction in fossil fuel combustion and in some cases the assumption of stringent air quality policies (Rao et al. 2017b; Smith et al. 2020c). As air pollutants exert a net-cooling effect, their reduction drives up non-CO2 warming in the near term, which can be attenuated by the simultaneous reduction of methane and black carbon (Shindell and Smith 2019; Smith et al. 2020b) (AR6 WGI Section 6.7). After 2030, the reduction in methane concentrations and associated reductions in tropospheric ozone levels tend to dominate so that a peak and decline in non-CO2 forcing and non-CO2-induced warming can occur before net zero CO2 is reached (Figure 3.29) (Rogelj et al. 2018 a). The more stringent the reductions in methane and other short-lived warming agents such as black carbon, the lower this peak and the earlier the decline of non-CO2 warming, leading to a reduction of warming rates and overall warming in the near to medium term (Harmsen et al. 2020; Smith et al. 2020b). This is important for keeping warming below a tight warming limit that is already reached around mid-century as is the case in 1.5°C pathways (Xu and Ramanathan 2017). Early and deep reductions of methane emissions, and other short-lived warming agents such as black carbon, provide space for residual CO2-induced warming until the point of net zero CO2 emissions is reached (see purple lines in Figure 3.29). Such emissions reductions have also been advocated due to co-benefits for, for example, reducing air pollution (Rao et al. 2016; Shindell et al. 2017a, 2018; Shindell and Smith 2019; Rauner et al. 2020a; Vandyck et al. 2020).

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Rapid SLCF reductions, specifically of methane, black carbon, and tropospheric ozone have immediate co-benefits including meeting sustainable development goals for reducing health burdens of household air pollution and reversing health- and crop-damaging tropospheric ozone (Jacobson 2002, 2010). SLCF mitigation measures can have regional impacts, including avoiding premature deaths in Asia and Africa and warming in central and northern Asia, southern Africa, and the Mediterranean (Shindell et al. 2012). Reducing outdoor air pollution could avoid 2.4 million premature deaths and 52 million tonnes of crop losses for four major staples (Haines et al. 2017). Existing research emphasises climate and agriculture benefits of methane mitigation measures with relatively small human health benefits (Shindell et al. 2012). Research also predicts that black carbon mitigation could substantially benefit global climate and human health, but there is more uncertainty about these outcomes than about some other predictions (Shindell et al. 2012). Other benefits to SLCF reduction include reducing warming in the critical near term, which will slow amplifying feedbacks, reduce the risk of non-linear changes, and reduce long-term cumulative climate impacts – like sea-level rise – and mitigation costs (Hu et al. 2017; UNEP and WMO 2011; Rogelj et al. 2018a; Xu and Ramanathan 2017; Shindell et al. 2012).

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UNEP and WMO, 2011: Integrated Assessment of Black Carbon and Tropospheric Ozone. World Meteorological Organization (WMO), Geneva, Switzerland, 283 pp., https://library.wmo.int/doc_num.php?explnum_id=7737 (Accessed November 1, 2021).

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Mertens, M., V. Grewe, V.S. Rieger, and P. Jöckel, 2018: Revisiting the contribution of land transport and shipping emissions to tropospheric ozone. Atmos. Chem. Phys. , 18(8) , 5567–5588, doi:10.5194/ACP-18-5567-2018.

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Changes in local air pollution emissions, particularly due to altered transportation patterns, have caused temporary air quality improvements in many cities around the world (see critical review by Adam et al. 2021). Many outdoor air pollutants, such as particulates, nitrogen dioxide, carbon monoxide, and volatile organic compounds declined during national lockdowns. Levels of tropospheric ozone, however, remained constant or increased. A promising transformation that has been observed in many cities is an increase in the share of active travel modes such as cycling and walking (Sharifi and Khavarian-Garmsir 2020). While this may be temporary, other trends, such as increased rates of teleworking and/or increased reliance on smart solutions that allow remote provision of services provide an unprecedented opportunity to transform urban travel patterns (Belzunegui-Eraso and Erro-Garcés 2020; Sharifi and Khavarian-Garmsir 2020).

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Xia, L., J.P. Nowack, S. Tilmes, and A. Robock, 2017: Impacts of stratospheric sulfate geoengineering on tropospheric ozone. Atmos. Chem. Phys. , 17(19) , 11913–11928, doi:10.5194/acp-17-11913-2017.

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Short-lived climate forcers (SLCFs) also play an important role in climate change, certainly for short-term changes (AR6 WGI, Figure SPM.2) (Shindell et al. 2012). These forcers consist of (i) substances contributing to warming, such as methane, black carbon and tropospheric ozone, and (ii) substances contributing to cooling (other aerosols, such as related to sulphur emissions). Most SLCFs are also air pollutants, and reducing their emissions provides additional co-benefits (Shindell et al. 2017a,b; Hanaoka and Masui 2020). In the case of the first group, emission reduction thus leads to both air pollution and climate benefits. For the second, group there is a possible trade-off (Shindell and Smith 2019; Lund et al. 2020). As aerosol emissions are mostly associated with fossil fuel combustion, the benefits of reducing CO2 could, in the short term, be reduced as a result of lower aerosol cooling. There has been an active discussion on the exact climate contribution of SLCF-focused policies in the literature. This discussion partly emerged from different assumptions on possible reductions in the absence of ambitious climate policy and the uncertain global climate benefit from aerosol (black carbon) (Rogelj et al. 2014). The latter is now assessed to be smaller than originally thought (Takemura and Suzuki 2019; Smith et al. 2020b) (see also AR6 WGI Section 6.4). Reducing SLCF emissions is critical to meet long-term climate goals and might help reduce the rate of climate change in the short term. Deep SLCF emission reductions also increase the remaining carbon budget for a specific temperature goal (Rogelj et al. 2015a; Reisinger et al. 2021) (Box 3.4). A more detailed discussion can be found in AR6 WGI Chapters 5 and 6.

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The relationship between warming limits and near-term action is also affected by the warming contribution of non-CO2 greenhouse gases and other short-lived climate forcers (Section 3.3; AR6 WGI Section 6.7). The estimated budget values for limiting warming to 1.5°C–2°C already assume stringent reductions in non-CO2 greenhouse gases and non-CO2 climate forcing as found in 1.5°C–2°C pathways (Section 3.3 and Cross-Working Group Box 1 in this chapter; AR6 WGI Section 5.5 and Box 5.2 in Chapter 5). Further variations in non-CO2 warming observed across 1.5°C–2°C pathways can vary the median estimate for the remaining carbon budget by 220 GtCO2 (AR6 WGI Section 5.5). In 1.5°C–2°C pathways, the non-CO2 warming contribution differs strongly between the near, medium and long term. Changes to the atmospheric composition of short-lived climate forcers (SLCFs) dominate the warming response in the near term (AR6 WGI Section 6.7). CO2 reductions are combined with strong reductions in air pollutant emissions due to rapid reduction in fossil fuel combustion and in some cases the assumption of stringent air quality policies (Rao et al. 2017b; Smith et al. 2020c). As air pollutants exert a net-cooling effect, their reduction drives up non-CO2 warming in the near term, which can be attenuated by the simultaneous reduction of methane and black carbon (Shindell and Smith 2019; Smith et al. 2020b) (AR6 WGI Section 6.7). After 2030, the reduction in methane concentrations and associated reductions in tropospheric ozone levels tend to dominate so that a peak and decline in non-CO2 forcing and non-CO2-induced warming can occur before net zero CO2 is reached (Figure 3.29) (Rogelj et al. 2018 a). The more stringent the reductions in methane and other short-lived warming agents such as black carbon, the lower this peak and the earlier the decline of non-CO2 warming, leading to a reduction of warming rates and overall warming in the near to medium term (Harmsen et al. 2020; Smith et al. 2020b). This is important for keeping warming below a tight warming limit that is already reached around mid-century as is the case in 1.5°C pathways (Xu and Ramanathan 2017). Early and deep reductions of methane emissions, and other short-lived warming agents such as black carbon, provide space for residual CO2-induced warming until the point of net zero CO2 emissions is reached (see purple lines in Figure 3.29). Such emissions reductions have also been advocated due to co-benefits for, for example, reducing air pollution (Rao et al. 2016; Shindell et al. 2017a, 2018; Shindell and Smith 2019; Rauner et al. 2020a; Vandyck et al. 2020).

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Positive and negative climate and carbon feedbacks involve: (i) fast processes on land and oceans at time scales from minutes to years, such as photosynthesis, soil respiration, net primary production, shallow ocean physics and air–sea fluxes; and (ii) slower processes taking from decades to millennia, such as changing ocean buffering capacity, ocean ventilation, vegetation dynamics, permafrost changes, peat formation and decomposition (Figure 5.2; Ciais et al., 2013; Forzieri et al., 2017; Williams et al., 2019). Depending on the particular combination of driver process and response dynamics, they behave as positive or negative feedbacks that amplify or dampen the magnitude and rates of climate change, respectively (Cox et al., 2000; Friedlingstein et al., 2003, 2006; Hauck and Völker, 2015; Williams et al., 2019); red and turquoise arrows in Figure 5.2 and Table 5.1).

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The gradual increase in atmospheric CO2 across the LDT was punctuated by three centennial 10–13 ppm increments, coeval with 100–200 ppb increases in CH4 (Marcott et al., 2014), reminiscent of similar oscillations reported for the last ice age associated with transient warming events (Dansgaard/Oeschger (DO) events; Ahn and Brook, 2014; Rhodes et al., 2017; Bauska et al., 2018) as well as previous deglacial transitions (Nehrbass-Ahles et al., 2020). The rate of change in atmospheric CO2 accumulation during these transient events exceeds the averaged deglacial growth rates by at least 50% (Table 2.1, Figure 5.4). The early deglacial release of remineralized carbon from the ocean abyss coincided with the resumption of Southern Ocean overturning circulation (Skinner et al.,2010; Schmitt et al., 2012; Ferrari et al., 2014; Gottschalk et al., 2016, 2020a; Jaccard et al., 2016; Rae et al., 2018; Moy et al., 2019) and the concomitant reduction in the global efficiency of the marine BCP, associated, in part, with dwindling iron fertilization (Hain et al., 2010; Martínez-García et al., 2014; Jaccard et al., 2016) The two subsequent pulses, centred 14.8 and 12.9 ka, are associated with enhanced air–sea gas exchange in the Southern Ocean (T. Li et al., 2020), iron fertilization in the South Atlantic and North Pacific (Lambert et al., 2021) and rapid increase in soil respiration owing to the resumption of AMOC and associated southward migration of the ITCZ (Marcottet al., 2014; Bauska et al., 2018). Rapid warming of high northern latitudes contributed to thaw permafrost, possibly liberating labile organic carbon to the atmosphere (Köhler et al.,2014; Crichton et al., 2016; Winterfeld et al., 2018; Meyer et al., 2019). Ocean surface pH reconstructions indicate that the ocean was oversaturated with respect to the atmosphere during the early, mid-LDT (Martínez-Botí et al., 2015b; Shao et al., 2019; Shuttleworth et al., 2021), suggesting that ocean sources at that time may have been larger than terrestrial sources. Over the course of the LDT, the decrease in Northern Hemisphere permafrost carbon stocks has been more than compensated by an increase in the carbon stocks of mineral soils, peatland and vegetation (Lindgren et al., 2018; Jeltsch-Thömmes et al., 2019). The land biosphere was, on average, a net sink for atmospheric carbon and accumulated several hundred Gt of carbon over the LDT. Detailed investigations reveal that Antarctic air temperatures, and more generally Southern Hemisphere (30°S–60°S) proxy temperature reconstructions, led the rise inpCO2 at the onset of the LDT, 18 ka ago, by several hundred years (Shakun et al., 2012; Chowdhry Beeman et al., 2019). Atmospheric CO2 led reconstructed global average temperature by several centuries (Shakun et al., 2012), corroborating the importance of CO2 as an amplifier of orbitally driven warming. During the LDT, the phasing between Antarctic air temperature and atmospheric GHG concentration changes was nearly synchronous, yet variable, owing to the complex nature of the mechanisms modulating the global carbon cycle (Chowdhry Beeman et al., 2019). Mean ocean temperature reconstructions, based on noble gas extracted from Antarctic ice are closely correlated with Antarctic air temperature and pCO2 records, emphasizing the role the Southern Ocean is playing in modulating global climate variability (Bereiter et al., 2018; Baggenstos et al., 2019).

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Significant uncertainties remain for the land CO2 sink partition of processes due to challenges in reconciling multiple-scale evidence from experiments to the globe (Fatichi et al., 2019; Walker et al., 2021), due to large spatial and inter-model differences in diagnosing dominant driving factors affecting the net land CO2 sink (Huntzinger et al., 2017; Fernández-Martínez et al., 2019), and due to model deficiency in process representations (He et al., 2016). Nitrogen dynamics, a major gap in DGVMs identified in AR5, have now been incorporated in about half of the DGVMs contributing to the carbon budget of the Global Carbon Project (GCP) (see Le Quéré et al. (2018a) for model characteristics) and a growing number of ESMs (Arora et al., 2020). However, as the representations of carbon–nitrogen interactions vary greatly among models, large uncertainties remain on how nitrogen cycling regulates the response of ecosystem carbon uptake to higher atmospheric CO2 (Walker et al., 2015; Wieder et al., 2019; Davies-Barnard et al., 2020; Meyerholt et al., 2020; see Section 5.4.1). Fire modules have been incorporated into 10 of 16 DGVMs contributing to the global carbon budget (Le Quéré et al., 2018a), and a growing number of models have representations of human ignitions and fire suppression processes (Rabin et al., 2017; Teckentrup et al., 2019). There are also growing DGVM developments to include management practices (Pongratz et al., 2018) and the effects of secondary forest regrowth (Pugh et al., 2019), though models still under-represent intensively managed ecosystems, such as croplands and managed forests (Guanter et al., 2014; Thurner et al., 2017). Processes that have not yet played a significant role in the land CO2 sink of the past decades but can grow in importance, include permafrost (Box 5.1) and peatlands dynamics (Dargie et al., 2017; Gibson et al., 2019), have also been incorporated in some DGVMs (Koven et al., 2015b; Burke et al., 2017a; Guimberteau et al., 2018). Growing numbers and varieties of Earth observations are being jointly used to drive and benchmark models, helping to further identify missing key processes or mechanisms that are poorly represented in the current generation of DGVMs (e.g., Collier et al., 2018).

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This box presents an assessment of interactions between the carbon and water cycles that influence the dynamics of the biosphere and its interaction with the climate system. It also highlights carbon–water trade-offs arising from the use of land-based climate change mitigation options. Individual aspects of the interactions between the carbon and water cycles are addressed in separate chapters (Sections 5.2.1, 5.4.1, 8.2.3, 8.3.1, 8.4.1 and 11.6). The influence of wetlands and dams on methane emissions is assessed elsewhere (Sections 5.2.2, 5.4.7 and 8.3.1), as well as the consequences of permafrost thawing (Section 9.5.2 and Box 5.1) and/or increased flooding (Sections 8.4.1, 11.5 and 12.4) on wetland extent in the northern high latitudes and wet tropics.

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In AR5, the ocean CH4 emissions were reported together with geological emissions, summing up to 54 (33–75) Tg yr–1. Coastal oceans, fjords and mud volcanos are major sources of CH4 in the marine environment, but CH4 flux measurements are sparse. Saunois et al. (2020) estimate that the oceanic budget, including biogenic, geological and hydrate emissions from coastal and open ocean, is 6 (range 4–10) Tg yr–1 for the 2000s, which is in good agreement with an air–sea flux measurement-based estimate of 6–12 Tg yr–1 (Weber et al., 2019). When estuaries are included, the total oceanic budget is 9–22 Tg yr–1, with a mean value of 13 Tg yr–1. A recent synthesis suggests that CH4 emissions from shallow coastal ecosystems, particularly from mangroves, can be as high as 5–6 Tg yr–1 (Al-Haj and Fulweiler, 2020). The reservoir emissions, including coastal wetlands and tidal flats, contribute up to 13 Tg yr–1 (Borges and Abril, 2011; Deemer et al., 2016). Methane seepage from the Arctic shelf, possibly triggered by the loss of geological storage due to warming and thawing of permafrost and hydrate decomposition, has a wide estimated range of 0.0–17 Tg yr–1 (Shakhova et al., 2010, 2014, 2017; Berchet et al., 2016); advanced eddy covariance measurements put the best estimate at about 3 Tg yr–1 from the East Siberian Arctic shelf (Thornton et al., 2020). The current flux is expected to be a mix of pre-industrial and climate change-driven fluxes, CH4 seepage is anticipated to increase in a warmer world (Dean et al., 2018).

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Uncertainties remain in process-based models with respect to their ability to capture the complicated responses of terrestrial N2O emissions to rain pulses, freeze–thaw cycles and the net consequences of elevated levels of CO2 accurately (Tian et al., 2019). Emerging literature suggests that permafrost thaw may contribute significantly to arctic N2O emissions (Voigt et al., 2020), but these processes are not yet adequately represented in models and upscaling to large-scale remains a significant challenge.

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CMIP6 ESMs predict losses of soil carbon with warming, which are larger than climate-driven vegetation carbon losses (Arora et al., 2020). As in CMIP5 (Todd-Brown et al., 2013), there is also a large CMIP6 ensemble spread in climate-driven soil carbon changes, partially driven by a large spread in the current soil carbon stocks predicted by the models. In CMIP5 ESMs, much of the soil carbon losses with warming can be traced to decreased carbon inputs, with a weaker contribution from changing soil carbon lifetimes due to faster decomposition rates (Koven et al., 2015b), which may be an artefact of the lack of permafrost carbon (Box 5.1). Isotopic constraints suggest that CMIP5 ESMs systematically overestimated the transient sensitivity of soil14C responses to atmospheric14C changes, implying that the models respond too quickly to changes in either inputs or turnover times, and that therefore the soil contribution to all feedbacks may be weaker than currently projected (He et al., 2016). Using natural gradients of soil carbon turnover as a constraint on long-term responses to warming suggests that both CMIP5 and CMIP6 ESMs may systematically underestimate the temperature sensitivity at high latitudes, and may overestimate the temperature sensitivity in the tropics (Koven et al., 2017; Wieder et al., 2018; Varney et al., 2020), although experimental soil warming in tropical forests suggest high sensitivity of decomposition to warming in those regions as well (Nottingham et al., 2020).

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The high agreement and multiple lines of evidence that warming increases decomposition rates lead to high confidence that warming will, overall, result in carbon losses relative to a constant climate and contribute to the positive carbon–climate feedback (Section 5.4.8). However, the wide spread in ESM projections and the lack of model representation of key processes that may amplify or mitigate soil carbon losses on longer time scales (including microbial dynamics, permafrost, peatlands, and nutrients) lead to low confidence in the magnitude of global soil carbon losses with warming.

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Soils in the Arctic and other cold regions contain perennially frozen layers, known as permafrost. Soils in the northern permafrost region store a large amount of organic carbon, estimated at 1460–1600 PgC across surface soils and deeper deposits (Hugelius et al., 2014; Strauss et al., 2017; Mishra et al., 2021). Of that carbon, permafrost soils and deposits store 1070–1360 PgC, of which 300–400 PgC are in the first metre, and the rest at depth. The remaining 280–340 PgC are in permafrost-free soils within the permafrost region. These carbon deposits have accumulated over thousands of years due to the slow rates of organic matter decomposition in frozen and/or waterlogged soil layers, but these frozen soils are highly decomposable upon thaw (Schädel et al., 2014).

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Despite accumulating evidence of increased carbon losses, it is difficult to scale up site- and ecosystem-level measurements to assess the net carbon balance over the entire permafrost region, due to the high spatial heterogeneity, the strong seasonal cycles, and the difficulty in monitoring these regions consistently across the year. The Special Report on Ocean and the Cryosphere in a Changing Climte (SROCC) assessed with high confidence that ecosystems in the permafrost region act as carbon sinks during the summer growing season, and that wintertime carbon losses are significant, consistent with a multi-decadal small increase in CO2 emissions during early winter at Barrow, Alaska (Sweeney et al., 2016; Webb et al., 2016; Meredith et al., 2019). These findings have been further strengthened by recent comprehensive synthesis of in-situ wintertime flux observations that show large carbon losses during the non-growing season (Natali et al., 2019). Increased autumn and winter respiration are a key large-scale fingerprint of top-down permafrost thaw predicted by ecosystem models (Parazoo et al., 2018). However, the length of these wintertime observational records is too short to unequivocally determine whether winter carbon losses are higher now than they used to be. One study inferred a multi-year net CO2 source for the tundra in Alaska (Commane et al., 2017), which is equivalent to 0.3 PgC yr–1 when scaled up to the northern permafrost region (low confidence) (Meredith et al., 2019).

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In addition to CO2, CH4 emissions from the northern permafrost region contribute to the global methane budget, but evidence as to whether these emissions have increased from thawing permafrost is mixed. The SROCC assigned low confidence to the degree of recent additional CH4 emissions from diverse sources throughout the permafrost region. These include observed regional lake area change, which suggest a 1.6–5 Tg CH4yr–1 increase over the last 50 years (Walter Anthony et al., 2016), ice-capped geological sources (Walter Anthony et al., 2012; Kohnert et al., 2017), and shallow Arctic Ocean shelves. The shallow subsea emissions are particularly uncertain due to the wide range of estimates (3 Tg CH4yr–1 (Thornton et al., 2016b) to 17 Tg CH4yr–1 (Shakhova et al., 2014)), and the lack of a baseline with which to infer any changes; however, the upper half of this range in flux estimates is inconsistent with the atmospheric inversions constrained by the pan-Arctic CH4 concentration measurements (Berchet et al., 2016).

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Atmospheric measurements and inversions performed at the global and regional scales do not show any detectable trends in annual mean CH4 emissions from the permafrost region over the past 30 years (Jackson et al., 2020; Saunois et al., 2020; Bruhwiler et al., 2021), consistent with atmospheric measurements in Alaska that showed no significant annual trends, despite significant increase in air temperature (Sweeney et al., 2016). Atmospheric inversions and biospheric models do not show any clear trends in CH4 emissions for wetland regions of the high latitudes during the period 2000–2016 (Patra et al., 2016; Poulter et al., 2017; Jackson et al., 2020; Saunois et al., 2020). Large uncertainties on wetland extent and limited data constraints place low confidence in these modelling approaches.

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In conclusion, there is high confidence that the permafrost region has acted as a historic carbon sink over centuries to millennia, and high confidence that some permafrost regions are currently net sources of CO2. There is robust evidence that some CH4 emissions sources for some regions have increased over the past decades (medium confidence). For the northern permafrost-wide region, no multi-decadal trend has been detected on CO2 and CH4 fluxes but, given the low resolution and sparse observations of current observations and modelling sytems, we place low confidence in this statement.

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Since AR5, there have been new studies showing that permafrost thaw also leads to nitrous oxide (N2O) release from soil (Abbott and Jones, 2015; Karelin et al., 2017; Wilkerson et al., 2019), a previously unaccounted source. However, this release is unquantified at the pan-Arctic scale.

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Large areas of Alaska and Siberia are underlain by frozen, glacial-age, ice- and carbon-rich deposits, and many of these areas show evidence of thermokarst processes during Holocene warm periods. Rapid warming of high northern latitudes contributed to permafrost thaw, liberating labile organic carbon tothe atmosphere (Köhler et al., 2014; Crichton et al., 2016; Winterfeld et al., 2018; Meyer et al., 2019), supporting the vulnerability of these areas to further warming (Strauss et al., 2013, 2017).

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Radiogenic and stable isotopic measurements on CH4 trapped in Antarctic ice support the view that CH4 emissions from fossil carbon reservoirs, including permafrost and methane hydrates, remained small in response to the deglacial warming. Mass-balance calculations reveal that geological CH4 emissions have not exceeded 19 Tg yr–1, highlighting that the deglacial increase in CH4 emissions was predominantly related to contemporary CH4 emissions from tropical wetlands and seasonally inundated floodplains (Bock et al., 2017; Petrenko et al., 2017; Dyonisius et al., 2020). Isotopic constraints on CO2 losses from permafrost with warming after the Last Glacial Maximum (LGM) are weaker than for CH4. While the biosphere as a whole held less carbon during the LGM than the pre-industrial, that change in stocks was smaller than the change in plant productivity, and so carbon losses at high latitudes may have been offset by increased tropical productivity in response to warming during the Last Deglacial Transition (LDT; Ciais et al., 2012). There is also paleoclimate evidence for processes that mitigate carbon losses with warming on longer time scales, such as longer-term carbon accumulation in lake deposits following thermokarst thaw (Walter Anthony et al., 2014), and long-term accumulation of carbon in permafrost soils following LDT carbon loss (Lindgren et al., 2018), particularly in peatlands which accumulated carbon at a slow but persistent rate in warm paleoclimates (Treat et al., 2019).

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In conclusion, several independent lines of evidence indicate that permafrost thaw did not release vast quantities of fossil CH4 associated with the transient warming events of the LDT. This suggests that large emissions of CH4 from old carbon sources will not occur in response to future warming (medium confidence).

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Near-surface permafrost is projected to decrease significantly under future global warming scenarios (high confidence) (Section 9.5.2), thus creating the potential for releasing CO2 and CH4 to the atmosphere, and act as a positive carbon–climate feedback.

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The processes that govern permafrost carbon loss are grouped into gradual and abrupt mechanisms. Gradual processes include the deepening of the seasonally thawed active layer into perennially frozen permafrost layers and lengthening of the thawed season within the active layer, which increases the amount of organic carbon that is thawed and the duration of thaw. Abrupt thaw processes include ice-wedge polygon degradation, hillslope collapse, thermokarst lake expansion and draining, all of which are processes largely occurring in regions with very high soil carbon content (Olefeldt et al., 2016a, b). Abrupt thaw processes can contribute up to half of the total net greenhouse gas release from permafrost loss, the rest attributed to gradual thaw (Schneider von Deimling et al., 2015; Turetsky et al., 2020). Increased fire frequency and severity (Hu et al., 2010) also contributes to abrupt emissions and the removal of the insulating cover which leads to an acceleration of permafrost thaw (Genet et al., 2013). Ecological feedbacks can both mitigate and amplify carbon losses: nutrient release from increased organic matter decomposition can drive vegetation growth that partially offsets soil carbon losses (Salmon et al., 2016), but also lead to biophysical feedbacks that further amplify warming (Myers-Smith et al., 2011).

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Through the Coupled Model Intercomparison Project Phase 5 (CMIP5), Earth system models (ESMs) had not included permafrost carbon dynamics. This remains largely true in Coupled Model Intercomparison Project Phase 6 (CMIP6), with most models not representing permafrost carbon processes, a small number representing the active-layer thickening effect on decomposition (Table 5.4), and no ESMs representing thermokarst or fire-permafrost-carbon interactions. The CMIP6 ensemble mean predicts a negative carbon–climate feedback in the permafrost region. However, those that do include permafrost carbon show a positive carbon–climate feedback in the permafrost region (Figure 5.27). Given the current limited ESM capacity to assess permafrost feedbacks, estimates in this report are based on published permafrost-enabled land surface model results.

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The SROCC assessed that warming under a high-emissions scenario (RCP8.5 or similar) would result in a loss of permafrost carbon by 2100 of 10s to 100s of PgC, with a maximum estimate of 240 PgC and a best estimate of 92 ± 17 PgC (Meredith et al., 2019; SROCC, Figure 3.11). Under lower emissions scenarios, Schneider von Deimling et al. (2015) estimated permafrost feedbacks of 20–58 PgC of CO2 by 2100 under an RCP2.6 scenario, and 28–92 PgC of CO2 under an RCP4.5 scenario.

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This new assessment, based on studies included in or published since SROCC (Schaefer et al., 2014; Koven et al., 2015c; Schneider von Deimling et al., 2015; Schuur et al., 2015; MacDougall and Knutti, 2016a; Gasser et al., 2018; Yokohata et al., 2020), estimates that the permafrost CO2 feedback per degree of global warming (Figure 5.29) is 18 [3.1 to 41, 5–95% range] PgC °C–1. The assessment is based on a wide range of scenarios evaluated at 2100, and an assessed estimate of the permafrost CH4-climate feedback at 2.8 [0.7 to 7.3] PgCeq °C–1 (Figure 5.29). This feedback affects the remaining carbon budgets for climate stabilization and is included in their assessment (Section 5.5.2).

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Beyond 2100, models suggest that the magnitude of the permafrost carbon feedback strengthens considerably over the period 2100–2300 under a high-emissions scenario (Schneider von Deimling et al., 2015; McGuire et al., 2018). Schneider von Deimling et al. (2015) estimated that thawing permafrost could release 20–40 PgC of CO2 in the period from 2100 to 2300 under an RCP2.6 scenario, and 115–172 PgC of CO2 under an RCP8.5 scenario. The multi-model ensemble (McGuire et al., 2018) projects a much wider range of permafrost soil carbon losses of 81–642 PgC (mean 314 PgC) for an RCP8.5 scenario from 2100 to 2300, and of a gain of 14 PgC to a loss of 54 PgC (mean loss of 17 PgC) for an RCP4.5 scenario over the same period.

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Methane release from permafrost thaw (including abrupt thaw) under a high-warming RCP8.5 scenario has been estimated at 836–2614 Tg CH4 over the 21st century and 2800–7400 Tg CH4 from 2100–2300 (Schneider von Deimling et al., 2015), and as 5300 Tg CH4 over the 21st century and 16,000 Tg CH4 from 2100–2300 (Turetsky et al., 2020). For RCP4.5, these numbers are 538–2356 Tg CH4 until 2100 and 2000–6100 Tg CH4 from 2100–2300 (Schneider von Deimling et al., 2015), and 4100 Tg CH4 until 2100 and 10,000 Tg CH4 from 2100–2300 (Turetsky et al., 2020).

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A key uncertainty is whether permafrost carbon feedbacks scale roughly linearly with warming (Koven et al., 2015c), or instead scale at a greater (MacDougall and Knutti, 2016b; McGuire et al., 2018) or smaller rate (e.g., CH4 emissions estimated by Turetsky et al., 2020). It alsoremains unclear whether the permafrost carbon pool represents a coherent global tipping element of the Earth system with a single abrupt threshold (Drijfhout et al., 2015) at a given level of global warming, or a local scale tipping point without abrupt thresholds when aggregated across the pan-Arctic region, as is suggested by recent model results (e.g., Koven et al., 2015a; McGuire et al., 2018).

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In conclusion, thawing terrestrial permafrost will lead to carbon release under a warmer world (high confidence). However, there is low confidence on the timing, magnitude and linearity of the permafrost climate feedback owing to the wide range of published estimates and the incomplete knowledge and representation in models of drivers and relationships. It is projected that CO2 released from permafrost will be 18 (3.1–41) PgC °C–1by 2100, with the relative contribution of CO2 vs CH4 remaining poorly constrained. Permafrost carbon feedbacks are included among the under-represented feedbacks quantified in Figure 5.29.

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Some CMIP6 models considered in this report now include nitrogen limitations on land vegetation growth, along with many other added processes compared to CMIP5. Table 5.4 summarizes characteristics of the land and ocean carbon cycle models used in CMIP6 ESMs (Arora et al., 2020). In CMIP6, most ocean carbon cycle models (8 of 11) track three or more limiting nutrients (most often nitrogen, phosphorus, silicon, iron), and include two or more phytoplankton types. More than half of the land carbon cycle models (6 of 11) now include an interactive nitrogen cycle, and almost half (5 of 11) represent forest fires. However, even for CMIP6, very few models explicitly represent vegetation dynamics (3 of 11) or permafrost carbon (2 of 11). Despite these remaining limitations, the carbon cycle components of CMIP6 represent an advance on those in CMIP5, as they represent additional important processes (e.g., nitrogen limitations on the land carbon sink, and iron limitations on ocean ecosystems).

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The CH4 feedbacks may arise from changing wetland emissions (including rice farming) and from sources that are expected to grow under climate change (e.g., related to permafrost thaw, fires, and freshwater bodies). CH4 emissions from wetlands and landfills generally increase with warming due to enhanced decomposition with higher temperatures, thereby potentially providing a positive CH4 feedback on climate (Dean et al., 2018). The contribution of wetlands to interannual variability of atmospheric CH4 is shaped by the different impacts of temperature and precipitation anomalies on wetland emissions (e.g., during El Niño episodes) and therefore the relationship between climate anomalies and the wetland contribution to the CH4 growth rate is complex (Pison et al., 2013; Nisbet et al., 2016; X. Zhang et al., 2020). As assessed by SROCC (IPCC, 2019b), there is high agreement across model simulations that wetlands CH4 emissions will increase in the 21st century, butlow agreement in the magnitude of the change (Denisov et al., 2013; Shindell et al., 2013; B.D. Stocker et al., 2013; Zhang et al., 2017; Koffi et al., 2020). Climate change increases wetland emissions (Gedney et al., 2004, 2019; Volodin, 2008; Ringeval et al., 2011; Denisov et al., 2013; Shindell et al., 2013) and gives rise to an estimated wetland CH4–climate feedback of 0.03 ± 0.01 W m–2°C–1 (mean ± 1 standard deviation; limited evidence, high agreement) (Arneth et al., 2010; Shindell et al., 2013; B.D. Stocker et al., 2013; Zhang et al., 2017). The effect of rising CO2 on productivity, and therefore on the substrate for methanogenesis, can further increase the projected increase in wetland CH4 emissions (Ringeval et al., 2011; Melton et al., 2013). Model projections accounting for the combined effects of CO2 and climate change suggest a potentially larger climate feedback (0.01–0.16 W m–2°C–1) (limited evidence, low agreement) (Gedney et al., 2019; Thornhill et al., 2021). Methane release from wetlands depends on the nutrient availability for methanogenic and methanotrophic microorganisms that can further modify this feedback (Stepanenko et al., 2016; Donis et al., 2017; Beaulieu et al., 2019). Methane emissions from thermokarst ponds and wetlands resulting from permafrost thaw are estimated to contribute an additional CH4-climate feedback of 0.01 [0.003 to 0.04, 5–95% range] W m–2°C–1 (limited evidence, low agreement).

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Methane release from wildfires may increase by up to a factor of 1.5 during the 21st century (Eliseev et al., 2014a, b; Kloster and Lasslop, 2017). However, given the contemporary estimate for CH4 from wildfires of no more than 16 TgCH4yr–1 (van der Werf et al., 2017; Saunois et al., 2020), this feedback is small, adding no more than 40 ppb to the atmospheric CH44 by the end of the 21st century (medium confidence). Methane emissions from pan-Arctic freshwater bodies is also estimated to increase by 16 TgCH4yr–1 in the 21st century (Tan and Zhuang, 2015). Emissions from subsea and permafrost methane hydrates are not expected to change substantially in the 21st century (Section 5.4.9.1.3).

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Land biosphere models show high agreement that long-term warming will increase N2O release from terrestrial ecosystems (Xu-Ri et al., 2012; B.D. Stocker et al., 2013; Zaehle, 2013; Tian et al., 2019). A positive land N2O climate feedback is consistent with paleo-evidence based on reconstructed and modelled emissions during the last deglacial period (Schilt et al., 2014; H. Fischer et al., 2019; Joos et al., 2020). The response of terrestrial N2O emissions to atmospheric CO2 increase and associated warming is dependent on nitrogen availability (van Groenigen et al., 2011; Butterbach-Bahl et al., 2013; Tian et al., 2019). Model-based estimates do not account for the potentially strong emissions increases in boreal and arctic ecosystems associated with future warming and permafrost thaw (Elberling et al., 2010; Voigt et al., 2017). There is medium confidence that the land N2O climate feedback is positive, but low confidence in the magnitude (0.02 ± 0.01 W m–2°C–1).

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This section assesses the magnitude of the combined biogeochemical feedback in the climate system (Figure 5.29) by integrating evidence from: carbon-cycle projections represented in Earth system models (Section 5.4.5.5), independent estimates of CO2 emissions due to permafrost thaw (Box 5.1) and fire (Section 5.4.3.2), natural CH4 and N2O emissions (Section 5.4.7), and aerosol and atmospheric chemistry (Section 6.3.6). We derive a physical climate feedback parameter α , as defined in Section 7.4.1.1, for CO2 -based feedbacks using the linear framework proposed by Gregory et al. (2009), using the radiative forcing equations for CO2 (Etminan et al., 2016).

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The climate feedback parameter for CO2 (–1.13 ± 0.45 W m–2°C–1, mean and 1 standard-deviation range) is dominated by the contribution of the CO2 -induced increase of ocean and land carbon storage (–1.46 ± 0.41 W m–2°C–1, corresponding to aβL+O of 1.66 ± 0.31 PgC ppm–1), with smaller contributions from the carbon cycle’s response to climate (0.24 ± 0.17 W m–2°C–1, corresponding toγ L+O of –50 ± 34 PgC °C–1), and emissions from permafrost thaw (0.09 [0.02 to 0.20] W m–2°C–1, corresponding toγof –18 [3 to 41] PgC °C–1, mean and 5–95% range) (Figure 5.29a). This estimate does not include an estimate of the fire-related CO2 feedback (range: 0.01–0.06 W m–2°C–1), as onlylimited evidence was available to inform its assessment. The sum (mean and 5–95th percentile range) of feedbacks from natural emissions of CH4 including permafrost thaw, and N2O (0.05 [0.02 to 0.09] W m–2°C–1), and feedbacks from aerosol and atmospheric chemistry (–0.20 [–0.41 to 0.01] W m–2°C–1) leads to an estimate of the non-CO2 biogeochemical feedback parameter of –0.15 [–0.36 to +0.06] W m–2°C–1. There is low confidence in the estimate of the non-CO2 biogeochemical feedbacks, due to the large range in the estimates of α for some individual feedbacks (Figure 5.29c), which can be attributed to the diversity in how models account for these feedbacks, limited process-level understanding, and the existence of known feedbacks where there is insufficient evidence to assess the feedback strength.

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The total global clathrate reservoir is estimated to contain 1500–2000 PgC (Archer et al., 2009; Ruppel and Kessler, 2017), held predominantly in ocean sediments, with only an estimated 20 PgC in and under permafrost (Ruppel, 2015). The present-day CH4 release from shelf clathrates is <10 TgCH4yr–1 (Kretschmer et al., 2015; Saunois et al., 2020). Despite polar amplification (Chapter 7), substantial releases from the permafrost-embedded subsea clathrates is very unlikely (Minshull et al., 2016; Malakhova and Eliseev, 2017, 2020). This is consistent with an overall small release of CH4 from the shelf clathrates during the last deglacial transition, despite large reorganizations in climate state (Bock et al., 2017; Petrenko et al., 2017; Dyonisius et al., 2020). The long time scales associated with clathrate destabilization makes itunlikely that CH4 release from the ocean to the atmosphere will deviate markedly from the present-day value through the 21st century (Hunter et al., 2013), corresponding to no more than additional 20 ppb of atmospheric CH4 (i.e., <0.2 ppb yr–1). Another possible source of CH4 is gas clathrates in deeper terrestrial permafrost and below it (Buldovicz et al., 2018; Chuvilin et al., 2018), which may have caused recent craters in the north of Russia (Arzhanov et al., 2016, 2020; Arzhanov and Mokhov, 2017; Kizyakov et al., 2017, 2018). Land clathrates are formed at depths greater than 200 m (Ruppel and Kessler, 2017; Malakhova and Eliseev, 2020), which precludes a substantial response to global warming over the next few centuries and associated emissions.

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Projecting abrupt changes is intrinsically difficult, because by definition abrupt changes occur in a small region of the parameter and/or forcing space. At the time of AR5 there was no available systematic study of abrupt changes or tipping points in ESMs. An analysis of ESMs since AR5 has identified a number of abrupt changes in the CMIP5 ensemble (Drijfhout et al., 2015; Bathiany et al., 2020). These include abrupt changes in tropical forests and high-latitude greening, permafrost thaw, and vegetation composition change (Bathiany et al., 2020). Most modelled abrupt changes were detected in boreal and tundra regions, with few models showing Amazon forest dieback (Bathiany et al., 2020).

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The land carbon cycle does not appear to play a fundamental role in the origin of the linearity and path-independence of TCRE (Goodwin et al., 2015; MacDougall and Friedlingstein, 2015; Ehlert et al., 2017) but, in contrast to the ocean sink, dominates the uncertainty in the magnitude of TCRE by modulating the cumulative airborne fraction of carbon (Goodwin et al., 2015; Williams et al., 2016; Katavouta et al., 2018; Jones and Friedlingstein, 2020). Some terrestrial carbon cycle feedbacks (such as the permafrost carbon feedback; Section 5.4.8, Box 5.1) have the potential to alter both the linearity and pathway independence of TCRE, if such feedbacks significantly contribute carbon to the atmosphere (Sections 5.5.1.2.3 and 5.4.8, and Box 5.1; MacDougall and Friedlingstein, 2015). A recent study also shows how the value of TCRE can depend on the effect of ocean ventilation modulating ocean heat uptake (Katavouta et al., 2019).

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As cumulative emissions increase, weakening land and ocean carbon sinks increase the airborne fraction of CO2 emissions (see Figure 5.25), but each unit increase in atmospheric CO2 has a smaller effect on global temperature owing to the logarithmic relationship between CO2 and its radiative forcing (Matthews et al., 2009; Etminan et al., 2016). At high values of cumulative emissions, some models simulate less warming per unit CO2 emitted, suggesting that the saturation of CO2 radiative forcing becomes more important than the effect of weakened carbon sinks (Herrington and Zickfeld, 2014; Leduc et al., 2015). The behaviour of carbon sinks at high emissions levels remains uncertain, as models used to assess the limits of the TCRE show a large spread in net land carbon balance (Section 5.4.5), and most estimates did not include the effect of permafrost carbon feedbacks (Sections 5.5.1.2.3 and 5.4). The latter would tend to further increase the airborne fraction at high cumulative emissions levels, and could therefore extend the window of linearity to higher total amounts of emissions (MacDougall et al., 2015). Leduc et al. (2016) suggested further that a declining strength of snow and sea ice feedbacks in a warmer world would also contribute to a smaller TCRE at high amounts of cumulative emissions. However, Tokarska et al. (2016) suggested that a large decrease in TCRE for high cumulative emissions is only associated with some EMICs; in the four ESMs analysed in their study, the TCRE remained approximately constant up to 5000 PgC, owing to stronger declines in the efficiency of ocean heat uptake in ESMs compared to EMICs.

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The AR5-assessed (W.J. Collins et al., 2013) TCRE range was based in part on the ESMs available at the time, which did not include some potentially important Earth system feedbacks. Since then, a number of studies have assessed the importance of permafrost carbon feedbacks, in particular on remaining carbon budgets (MacDougall and Friedlingstein, 2015; MacDougall et al., 2015; Burke et al., 2017b; Gasser et al., 2018; Lowe and Bernie, 2018), a development highlighted and assessed in the IPCC Special Report on Global Warming of 1.5°C (Rogelj et al., 2018b). MacDougall and Friedlingstein (2015) reported a TCRE increase of about 15% when including permafrost carbon feedbacks. The overall linearity of the TCRE during the 21st century was not affected, but they also found that permafrost carbon feedbacks caused an increase in TCRE on multi-century time scales under declining CO2 emissions rates. In addition, other processes that are not regarded, or are only partially considered in individual or all ESMs, could cause a further increase or decrease of TCRE (Matthews et al., 2020). These are discussed in detail in Section 5.4, but their quantitative effects on TCRE have not yet been explored by the literature.

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Whether TCRE remains an accurate predictor of CO2 -induced warming when annual CO2 emissions reach zero and are followed by net carbon dioxide removal (also referred to as TCRE reversibility) therefore hinges on contributions of slow components of the climate system that cause unrealized warming from past CO2 emissions. Such slow components can arise from either physical climate (i.e., ocean heat uptake) or carbon cycle (i.e., ocean carbon uptake and permafrost carbon release) processes. The combined effect of these processes determines the magnitude and sign of the ZEC (MacDougall et al., 2020), which in turn impacts TCRE reversibility. As discussed in Section 4.7.1.1, recent model estimates of the ZEC suggest a range of ±0.19°C centred on zero (MacDougall et al., 2020). This suggests low agreement among models as to the reversibility of the TCRE in response to net-negative CO2 emissions. Furthermore, most models used for ZEC assessments to date do not represent permafrost carbon processes, although understanding their contribution is essential to quantify the TCRE contribution. There is therefore limited evidence that quantifies the impact of permafrost carbon feedbacks on the reversibility of TCRE, leading to low confidence that the TCRE remains an accurate predictor of temperature changes in scenarios of net-negative CO2 emissions on time scales of more than a half a century.

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(Section 5.5.1.2 highlighted recent literature describing potential impacts of Earth system feedbacks that have typically not been included in standard ESMs (MacDougall and Friedlingstein, 2015; Schneider von Deimling et al., 2015; Schädel et al., 2016; Burke et al., 2017b; Mahowald et al., 2017; Comyn-Platt et al., 2018; Gasser et al., 2018; Lowe and Bernie, 2018), the most important of which is carbon release from thawing permafrost. The SR1.5 estimated unrepresented Earth system processes to result in a reduction of remaining carbon budgets of up to 100 GtCO2 over the course of this century, and more thereafter (Rogelj et al., 2018b). Here this assessment is updated based on the Earth system feedback assessment of (Section 5.4.8 and synthesized in Figure 5.29 by applying the reverse method by Gregory et al. (2009).

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Modelling studies consistently show that, relative to a high-CO2 world without SRM, SRM-induced cooling (Section 4.6.3.3) would reduce plant and soil respiration, and also reduce the negative effects of warming on ocean carbon uptake (Tjiputra et al., 2016; Xia et al., 2016; Cao and Jiang, 2017; Jiang et al., 2018; Muri et al., 2018; Sonntag et al., 2018; Plazzotta et al., 2019; C.-E. Yang et al., 2020). A multi-model study (Plazzotta et al., 2019) indicates that, relative to a high-CO2 concentration world without SRM, stratospheric sulphur dioxide (SO2) injection increases the allowable CO2 emissions by enhancing CO2 uptake by both land and ocean (Figure 5.37). As a result of enhanced global carbon uptake, SRM would reduce the burden of atmospheric CO2 (high confidence). However, the amount of SRM-induced reduction in atmospheric CO2 depends on the future emissions scenario and modelled oceanic and terrestrial carbon sinks, which differ widely between models (Tjiputra et al., 2016; Xia et al., 2016; Cao and Jiang, 2017; Muri et al., 2018). Models that include the terrestrial nitrogen cycle usually report a much smaller reduction of atmospheric CO2 in response to SRM than models without the nitrogen cycle, mainly because nitrogen limitation leads to a weaker terrestrial carbon sink (Tjiputra et al., 2016; Muri et al., 2018; C.-E. Yang et al., 2020). Large-scale application of SAI is found to reduce the rate of release of CO2 and CH4 from permafrost thaw (Lee et al., 2019; Chen et al., 2020).

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In the Arctic, large amounts of organic carbon are stored in permafrost – ground that remains frozen throughout the year. If significant areas of permafrost thaw as the climate warms, some of that carbon may be released into the atmosphere in the form of carbon dioxide or methane, resulting in additional warming. Projections from models of permafrost ecosystems suggest that future permafrost thaw will lead to some additional warming – enough to be important, but not enough to lead to a ‘runaway warming’ situation, where permafrost thaw leads to a dramatic, self-reinforcing acceleration ofglobal warming.

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Changes in ice sheets are indicators of the longest-term impacts of climate change and associated with changes in global and regional sea level. Seasonal snow cover has many implications for mid- to high-latitude regions (albedo, hydrological cycle, etc.) with impacts on biospheric components of the system. Changes in sea ice extent, seasonality and thickness have potential impacts for hemispheric-scale circulation (Cross-Chapter Box 10.1). Changes in glacier mass balance contribute to changes in sea level but also have substantial implications for water supply for a substantial proportion of the global population. Finally, changes in permafrost and the seasonally thawed active layer have substantial implications in mid- to high-latitudes and have been hypothesized to be important in potential feedbacks through degassing of WMGHGs as the permafrost thaws.

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The AR5 concluded that in most regions and at most monitoring sites permafrost temperatures since the 1980s had increased (high confidence). Negligible change was observed at a few sites, mainly where permafrost temperatures were close to 0°C, with slight cooling at a limited number of sites. The AR5 also noted positive trends in active layer thickness (ALT; the seasonally thawed layer above the permafrost) since the 1990s for many high latitude sites (medium confidence). The SROCC concluded permafrost temperatures have increased to record high levels since the 1980s (very high confidence) with a recent increase by 0.29°C ± 0.12°C from 2007 to 2016 averaged across polar and high mountain regions globally.

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Permafrost occurrence during the Pliocene has been inferred from pollen in lake sediments in NE Arctic Russia and permafrost-vegetation relationships which indicate that permafrost was absent during the MPWP in this region (Brigham-Grette et al., 2013; Herzschuh et al., 2016). Analysis of speleothem records in Siberian caves, indicates that permafrost was absent in the current continuous permafrost zone at 60°N at the start of the 1.5 Ma record, with aggradation occurring around 0.4 Ma (Vaks et al., 2020). There are indications of extensive permafrost thaw during subsequent interglacials especially further south in the current permafrost zone (Vaks et al., 2013). Reconstruction of permafrost distribution during the LGM indicates that permafrost was more extensive in exposed areas (Vandenberghe et al., 2014). In non-glaciated areas of the North American Arctic there is permafrost that survived the LIG (French and Millar, 2014). Trends and timing of permafrost aggradation and thaw over the last 6 kyr in peatlands of the NH were recently summarized (Hiemstra, 2018; Treat and Jones, 2018). Three multi-century periods (ending 1000 Before the Common Era (BCE), 500 CE and 1850 CE) of permafrost aggradation, associated with neoglaciation periods are inferred resulting in more extensive permafrost in peatlands of the present-day discontinuous permafrost zone, which reached a peak approximately 250 years ago, with thawing occurring concurrently with post 1850 warming (Treat and Jones, 2018). Although permafrost persists in peatlands at the southern extent of the permafrost zone where it was absent prior to 3 ka, there has been thawing since the 1960s (James et al., 2013; B.M. Jones et al., 2016; Holloway and Lewkowicz, 2020).

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Records of permafrost temperature measured in several boreholes located throughout the northern polar regions indicate general warming of permafrost over the last 3–4 decades (Figure 2.25), with marked regional variations (Romanovsky et al., 2017a, b, 2020; Biskaborn et al., 2019). Recent (2018–2019) permafrost temperatures in the upper 20–30 m layer (at depths where seasonal variation is minimal) were the highest ever directly observed at most sites (Romanovsky et al., 2020), with temperatures in colder permafrost of northern North America being more than 1°C higher than they were in 1978. Increases in temperature of colder Arctic permafrost are larger (average 0.4°C–0.6°C per decade) than for warmer (temperature >–2°C) permafrost (average 0.17°C per decade) of sub-Arctic regions (Figures 2.25, 9.22).

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Increases in permafrost temperature over the last 10–30 years of up to 0.3°C per decade have been documented at depths of about 20 m in high elevation regions in the NH (European Alps, the Tibetan Plateau and some other high elevation areas in Asia; G. Liu et al., 2017; Cao et al., 2018; Biskaborn et al., 2019; Noetzli et al., 2020; Zhao et al., 2020). In Antarctica, where records are limited and short (most <10 years) trends are less evident (Noetzli et al., 2019).

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Assessment of trends in ALT is complicated by considerable ALT interannual variability. For example, in north-western North America during the extreme warm year of 1998, ALT was greater than in prior years. Although ALT decreased over the following few years, it has generally increased again since the late 2000s (Duchesne et al., 2015; Romanovsky et al., 2017b, 2020). However, at some sites there has been little change in ALT due to ground subsidence that accompanies thaw of ice-rich permafrost (Streletskiy et al., 2017; O’Neill et al., 2019). In the European and Russian Arctic there has been a broad-scale increase in ALT during the 21st century (Streletskiy et al., 2015; Romanovsky et al., 2020). In high elevation areas in Europe and Asia, increases in ALT have occurred since the mid-1990s (Y. Liu et al., 2017; Cao et al., 2018; Noetzli et al., 2019, 2020; Zhao et al., 2020). Limited and shorter records for Antarctica show marked interannual variability and no apparent trend with ALT being relatively stable or decreasing at some sites since 2006 (Hrbáček et al., 2018).

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Observations of ground subsidence and other landscape change (e.g., thermokarst, slope instability) since the middle of the 20th century in the Arctic associated with ground ice melting have been documented in several studies and provide additional indications of thawing permafrost (Séjourné et al., 2015; Liljedahl et al., 2016; Borge et al., 2017; Kokelj et al., 2017; Nitze et al., 2017; Streletskiy et al., 2017; Derksen et al., 2019; Farquharson et al., 2019; Lewkowicz and Way, 2019; O’Neill et al., 2019; see Section 9.5.2.1). In mountain areas, destabilization and acceleration of rock glacier complexes that may be associated with warming permafrost have also been observed (Eriksen et al., 2018; Marcer et al., 2019).

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In summary, increases in permafrost temperatures in the upper 30 m have been observed since the start of observational programs over the past three to four decades throughout the permafrost regions (high confidence). Limited evidence suggests that permafrost was less extensive during the MPWP (low confidence). Permafrost that formed after 3ka still persists in areas of the NH, but there are indications of thaw after the mid-1800s (medium confidence).

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Assessing the long-term context of recent changes is key to understanding their potential importance and implications. The climate system consists of many observable aspects that vary over a very broad range of timescales. Some biogeochemical indicators of change such as atmospheric CO2 concentrations and ocean pH have shifted rapidly and CO2 concentrations are currently at levels unseen in at least 800 kyr (the period of continuous polar ice-core records) and very likely for millions of years. The GMST in the past decade is likely warmer than it has been on a centennially-averaged basis in the CE and more likely than not since the peak of the LIG. Many more integrative components of the climate system (e.g., glaciers, GMSL) are experiencing conditions unseen in millennia, whereas the most slowly responding components (e.g., ice-sheet extent, permafrost, tree line) are at levels unseen in centuries (high confidence). The rate at which several assessed climate indicators (e.g., GMSL, OHC, GSAT) have changed over recent decades is highly unusual in the context of preceding slower changes during the current post-glacial period (high confidence).

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The latitudinal temperature gradient during the MPWP was reduced relative to present-day and the consistency between proxy and modelled temperatures has improved since AR5 (Section 7.4.4.1.2). Northern high latitude (>60°N) SSTs were up to 7°C higher than 1850–1900 (Bachem et al., 2016; McClymont et al., 2020; Sánchez-Montes et al., 2020), and terrestrial biomes were displaced poleward (e.g., Dowsett et al., 2019) (Cross-Chapter Box 2.4, Figure 1b). Arctic tundra regions currently underlain by permafrost were warm enough to support boreal forests, which shifted northward by approximately 250 km in Siberia, and up to 2000 km in the Canadian Arctic Archipelago (Salzmann et al., 2013; Fletcher et al., 2017). The shift caused high-latitude surface albedo changes, which further amplified the Pliocene global warming (Zhang and Jiang, 2014). Vegetation changes in north-east Siberia indicate that MPWP summer temperatures were up to 6°C higher than present day (Brigham-Grette et al., 2013). Farther south, modern boreal forest regions in Russia and eastern North America were covered with temperate forests and grasslands, whereas highly diverse, warm-temperate forests with subtropical taxa were widespread in central and eastern Europe (Cross-Chapter Box 2.4, Figure 1). While seasonal sea ice was present in the North Atlantic and Arctic oceans, its winter extent was reduced relative to present (Knies et al., 2014; Clotten et al., 2018), and some models suggest that the Arctic was sea ice free during the summer (Howell et al., 2016; Feng et al., 2020).

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AMAP, 2017: Snow, Water, Ice and Permafrost in the Arctic (SWIPA). Arctic Monitoring and Assessment Programme (AMAP), Oslo, Norway, 269 pp., www.amap.no/documents/doc/snow-water-ice-and-permafrost-in-the-arctic-swipa-2017/1610.

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Biskaborn, B.K. et al., 2019: Permafrost is warming at a global scale. Nature Communications, 10(1), 264, doi: 10.1038/s41467-018-08240-4.

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Brown, R. et al., 2017: Arctic terrestrial snow cover. In: Snow, Water, Ice and Permafrost in the Arctic (SWIPA) 2017. Arctic Monitoring and Assessment Programme (AMAP), Oslo, Norway, pp. 25–64, www.amap.no/documents/doc/snow-water-ice-and-permafrost-in-the-arctic-swipa-2017/1610.

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Cao, B. et al., 2018: Thermal Characteristics and Recent Changes of Permafrost in the Upper Reaches of the Heihe River Basin, Western China. Journal of Geophysical Research: Atmospheres, 123, 7935– 7949, doi: 10.1029/2018jd028442.

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Derksen, C. et al., 2019: Changes in Snow, Ice and Permafrost Across Canada. In: Canada’s Changing Climate Report[Bush, E. and D.S. Lemmen (eds.)]. Government of Canada, Ottawa, ON, Canada, pp. 194–260,https://changingclimate.ca/cccr2019/chapter/5-0/.

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Duchesne, C., S.L. Smith, M. Ednie, and P.P. Bonnaventure, 2015: Active layer variability and Change in the Mackenzie Valley, Northwest Territories. In: 68th Canadian Geotechnical Conference and 7th Canadian Permafrost Conference. pp. 1–7.

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Farquharson, L.M. et al., 2019: Climate Change Drives Widespread and Rapid Thermokarst Development in Very Cold Permafrost in the Canadian High Arctic. Geophysical Research Letters, 46(12), 6681–6689, doi: 10.1029/2019gl082187.

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French, H.M. and S.W.S. Millar, 2014: Permafrost at the time of the Last Glacial Maximum (LGM) in North America. Boreas, 43(3), 667–677, doi: 10.1111/bor.12036.

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Hiemstra, J.F., 2018: Permafrost and environmental dynamics: A virtual issue of The Holocene. Holocene, 28(8), 1201–1204, doi: 10.1177/0959683618785835.

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Holloway, J.E. and A.G. Lewkowicz, 2020: Half a century of discontinuous permafrost persistence and degradation in western Canada. Permafrost and Periglacial Processes, 31(1), 85–96, doi: 10.1002/ppp.2017.

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James, M., A.G. Lewkowicz, S.L. Smith, and C.M. Miceli, 2013: Multi-decadal degradation and persistence of permafrost in the Alaska Highway corridor, northwest Canada. Environmental Research Letters, 8(4), 045013, doi: 10.1088/1748-9326/8/4/045013.

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Jones, B.M. et al., 2016: Presence of rapidly degrading permafrost plateaus in south-central Alaska. The Cryosphere, 10(6), 2673–2692, doi: 10.5194/tc-10-2673-2016.

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Kokelj, S., T.C. Lantz, J. Tunnicliffe, R. Segal, and D. Lacelle, 2017: Climate-driven thaw of permafrost preserved glacial landscapes, northwestern Canada. Geology, 45(4), 371–374, doi: 10.1130/g38626.1.

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Liljedahl, A.K. et al., 2016: Pan-Arctic ice-wedge degradation in warming permafrost and its influence on tundra hydrology. Nature Geoscience, 9, 312–318, doi: 10.1038/ngeo2674.

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Liu, G. et al., 2017: Permafrost Warming in the Context of Step-wise Climate Change in the Tien Shan Mountains, China. Permafrost and Periglacial Processes, 28(1), 130–139, doi: 10.1002/ppp.1885.

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Nitze, I. et al., 2017: Landsat-based trend analysis of lake dynamics across Northern Permafrost Regions. Remote Sensing, 9(7), 640, doi: 10.3390/rs9070640.

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Noetzli, J. et al., 2019: Permafrost Thermal State [in “State of the Climate in 2018”]. Bulletin of the American Meteorological Society, 100(9), S21–S22, doi: 10.1175/2019bamsstateoftheclimate.1.

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Noetzli, J. et al., 2020: Permafrost Thermal State [in “State of the Climate in 2019”]. Bulletin of the American Meteorological Society, 101(8), S34–S36.

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O’Neill, H.B., S.L. Smith, and C. Duchesne, 2019: Long-Term Permafrost Degradation and Thermokarst Subsidence in the Mackenzie Delta Area Indicated by Thaw Tube Measurements. Cold Regions Engineering 2019, 643–651, doi: 10.1061/9780784482599.074.

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Romanovsky, V.E. et al., 2017a: Changing permafrost and its impacts. In: Snow, Water, Ice and Permafrost in the Arctic (SWIPA) 2017. Arctic Monitoring and Assessment Program (AMAP), Oslo, Norway, pp. 65–102, www.amap.no/documents/doc/snow-water-ice-and-permafrost-in-the-arctic-swipa-2017/1610.

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Romanovsky, V.E. et al., 2017b: Terrestrial Permafrost. In: Arctic Report Card 2017. National Oceanic and Atmospheric Administration (NOAA), pp. 54–59, https://arctic.noaa.gov/report-card/report-card-2017.

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Romanovsky, V.E. et al., 2020: The Arctic: Terrestrial Permafrost [in “State of the Climate in 2019”]. Bulletin of the American Meteorological Society, 101(8), S265–S269, doi: 10.1175/bams-d-20-0086.1.

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Streletskiy, D.A., A.B. Sherstiukov, O.W. Frauenfeld, and F.E. Nelson, 2015: Changes in the 1963–2013 shallow ground thermal regime in Russian permafrost regions. Environmental Research Letters, 10(12), 125005, doi: 10.1088/1748-9326/10/12/125005.

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Streletskiy, D.A. et al., 2017: Thaw Subsidence in Undisturbed Tundra Landscapes, Barrow, Alaska, 1962–2015. Permafrost and Periglacial Processes, 28(3), 566–572, doi: 10.1002/ppp.1918.

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Treat, C.C. and M.C. Jones, 2018: Near-surface permafrost aggradation in Northern Hemisphere peatlands shows regional and global trends during the past 6000 years. Holocene, 28(6), 998–1010, doi: 10.1177/0959683617752858.

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Vaks, A. et al., 2013: Speleothems Reveal 500,000-Year History of Siberian Permafrost. Science, 340(6129), 183–186, doi: 10.1126/science.1228729.

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Vaks, A. et al., 2020: Palaeoclimate evidence of vulnerable permafrost during times of low sea ice. Nature, 577(7789), 221–225, doi: 10.1038/s41586-019-1880-1.

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Zhao, L. et al., 2020: Changing climate and the permafrost environment on the Qinghai–Tibet (Xizang) plateau. Permafrost and Periglacial Processes, 31(3), 396–405, doi: 10.1002/ppp.2056.

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Zhao, S.-P., Z.-T. Nan, Y.-B. Huang, and L. Zhao, 2017: The Application and Evaluation of Simple Permafrost Distribution Models on the Qinghai-Tibet Plateau. Permafrost and Periglacial Processes, 28(2), 391–404, doi: 10.1002/ppp.1939.

ipcc special report on climate change and landresources/ipcc/cleaned_content/wg1/Chapter03/html_with_ids.html#3.6.1_p1

The AR5 did not make attribution statements on changes in global carbon sinks. The IPCC Special Report on Climate Change and Land (SRCCL) assessed with high confidence that global vegetation photosynthetic activity has increased over the last 2–3 decades (Jia et al., 2019). That increase was attributed to direct land use and management changes, as well as to CO2 fertilization, nitrogen deposition, increased diffuse radiation and climate change (high confidence). The AR5 assessed with high confidence that CMIP5 Earth System Models (ESMs) simulate the global mean land and ocean carbon sinks within the range of observation-based estimates (Flato et al., 2013). The IPCC SRCCL, however, noted the remaining shortcomings of carbon cycle schemes in ESMs (Jia et al., 2019), which for example do not properly incorporate thermal responses of respiration and photosynthesis, and frequently omit representations of permafrost thaw (Comyn-Platt et al., 2018), the nitrogen cycle (R.Q. Thomas et al., 2015) and its influence on vegetation dynamics (Jeffers et al., 2015), the phosphorus cycle (Fleischer et al., 2019), and accurate implications of carbon store changes for a range of land use and land management options (Erb et al., 2018; Harper et al., 2018) (see Sections 5.2.1.4.1 and 5.4, Figure 5.24 and Table 5.4 for details).

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Further such ESM developments include: (i) Apart from the nitrogen cycle, extending terrestrial carbon cycle models to simulate interactions between the carbon cycle and other nutrient cycles, such as phosphorus, that are known to play an important role in limiting future plant uptake of CO2 (Zaehle et al., 2015). (ii) Introducing explicit coupling between interactive atmospheric chemistry and aerosol schemes (Gettelman et al., 2019; Sellar et al., 2019), which has been shown to affect estimates of historical aerosol radiative forcing (Karset et al., 2018). Furthermore, interactive treatment of atmospheric chemistry in a full ESM supports investigation of interactions between climate and air quality mitigation efforts, such as in AerChemMIP (Collins et al., 2017), as well as interactions between stratospheric ozone recovery and global warming (Morgenstern et al., 2018). (iii) Coupling between components of Earth system models has been extended to increase their utility for studying future interactions across the full Earth system, such as between ocean biogeochemistry and cloud-aerosol processes (Mulcahy et al., 2020), and vegetation and impacts on dust production (Kok et al., 2018), production of secondary organic aerosols (SOA, Zhao et al., 2017) and Equilibrium Climate Sensitivity (ECS), whereby enhanced CO2 fertilization of land vegetation causes changes in regional surface albedo (Andrews et al., 2019). Increased coupling between physical climate and biogeochemical processes in a single ESM, along with an increased number of interactively represented processes, such as permafrost thaw, vegetation, wildfires and continental ice sheets increases our ability to investigate the potential for abrupt and interactive changes in the Earth system (see Sections 4.7.3 and 5.4.9, and Box 5.1). Table 5.4 provides an overview of recent advances in representing the carbon cycle in ESMs.

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Comyn-Platt, E. et al., 2018: Carbon budgets for 1.5 and 2°C targets lowered by natural wetland and permafrost feedbacks. Nature Geoscience, 11(8), 568–573, doi: 10.1038/s41561-018-0174-9.

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The SR1.5 (IPCC, 2018b) concluded with high confidence that overshoot trajectories ‘result in higher impacts and associated challenges compared to pathways that limit global warming to 1.5°C with no or limited overshoot’. The degree and duration of overshoot affects the risks and impacts likely to be experienced (Hoegh-Guldberg et al., 2018) and the emissions pathway required to achieve it (Akimoto et al., 2018). Consequences relating to ice sheets and climatic extremes have been found to be greater at 2°C of global warming than at 1.5°C (Schleussner et al., 2016; Hoegh-Guldberg et al., 2018) but even on recovery to lower temperatures, these effects may not reverse. Overshoot has been found to lead to irreversible changes in thermosteric sea level (Tokarska and Zickfeld, 2015; Palter et al., 2018; Tachiiri et al., 2019), AMOC (Palter et al., 2018), ice sheets, and permafrost carbon (Sections 4.7.2 and 5.4.9) and to long-lasting effects on ocean heat (Tsutsui et al., 2006). Abrupt changes and tipping points are not well understood, but the higher the warming level and the longer the duration of overshoot, the greater the risk of unexpected changes (Section 4.7.2). Non-reversal of the hydrological cycle has also been found in some studies with an increase in global precipitation following CO2 decrease being attributed to a build-up of ocean heat (Wu et al., 2010), and to a fast atmospheric adjustment to CO2 radiative forcing (Cao et al., 2011).

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The transient climate response to cumulative CO2 emissions, TCRE, allows climate policy goals to be associated with remaining carbon budgets as global temperature increase is near-linear with cumulative emissions (Section 5.5). Research since AR5 has shown that the concept of near-linearity of climate change to cumulative carbon emissions holds for measures other than just GSAT, such as regional climate (Leduc et al., 2016) or extremes (Harrington et al., 2016; Seneviratne et al., 2016). However, ocean heat and carbon uptake do exhibit path dependence, leading to deviation from the TCRE relationship for levels of overshoot above 300 PgC (Zickfeld et al., 2016; Tokarska et al., 2019). Sea level rise, loss of ice sheets, and permafrost carbon release may not reverse under overshoot and recovery of GSAT and cumulative emissions (Section 4.7). TCRE remains a valuable concept to assess climate policy goals and how to achieve them but given the non-reversibility of different climate metrics with CO2 and GSAT reductions, it has limitations associated with evaluating the climate response under overshoot scenarios and CO2 removal (medium confidence).

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Both CMIP6 and CMIP5 results show that global temperature beyond 2100 is strongly dependent on scenario, and the difference in GSAT projections between high- and low-emissions scenarios continues to increase (high confidence). Under the extended RCP2.6 (Caesar et al., 2013) and SSP1-2.6 scenarios, where CO2 concentration and radiative forcing continue to decline beyond 2100, GSAT stabilizes during the 21st century before decreasing and remaining below 2°C until 2300, except in some of the very high climate-sensitivity ESMs, which project GSAT to stay above 2°C by 2300 (Figure 4.40). Under RCP8.5, regional temperature changes above 20°C have been reported in multiple models over high-latitude land areas (Caesar et al., 2013; Randerson et al., 2015). Non-CO2 forcing and feedbacks remain important by 2300 (high confidence). Randerson et al. (2015) found that 1.6°C of warming by 2300 came from non-CO2 forcing alone in RCP8.5, and Rind et al. (2018) show that regional forcing from aerosols can have notable effects on ocean circulation on centennial time scales. High latitude warming led to longer growing seasons and increased vegetation growth in the CESM1 model (Liptak et al., 2017), and Burke et al. (2017) found that carbon release from permafrost areas susceptible to this warming may amplify future climate change by up to 17% by 2300.

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Burke, E.J. et al., 2017: Quantifying uncertainties of permafrost carbon–climate feedbacks. Biogeosciences, 14(12), 3051–3066, doi: 10.5194/bg-14-3051-2017.

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The Hindu Kush Himalaya (HKH) constitutes the largest glacierized region outside the poles and provides the headwaters for several major rivers (Sharma et al., 2019). Since the 1960s, the HKH has experienced significant trends in the mean and extremes of temperature and precipitation, accompanied by glacier mass loss and retreat, snowmelt and permafrost degradation (Yao et al., 2012a, b; Azam et al., 2018; Bolch et al., 2019; Krishnan et al., 2019a, b; Chug et al., 2020; Sabin et al., 2020). Observational uncertainty and lack of consistent, high-quality datasets hamper reliable assessments of climate change and model evaluation over several mountain areas, including the HKH (Section 10.2.2). This box assesses observed and projected climate change in the extended HKH (outline in Cross-Chapter Box 10.4, Figure 1a), in which we include the Tibetan Plateau (TP) and Pamir mountains.

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The SROCC assessed that snow cover has declined in duration, depth and accumulated mass at lower elevations in mountain regions, including the HKH (high confidence). Glaciers are losing mass (very high confidence) and permafrost is warming (high confidence) over high mountains in recent decades, and it is very likely that atmospheric warming is the main driver. A significant reduction in HKH glacier area has been observed since the 1970s, with smaller glaciers generally shrinking faster (e.g., Bolch et al., 2019). HKH glacier mass loss took place at the lowest rate among high mountain areas in the last 20 years, although with one of the largest total losses (Section 9.5.1.1 and Figure 9.20; Shean et al., 2020). The highest mass-loss rates occurred in the eastern and northern HKH, while gains occurred in the west (e.g., Shean et al., 2020). Glacier mass gain has been coined as the ‘Karakoram anomaly’ (Sections 8.3.1.7.1 and 9.5.1), explained by a combination of low temperature sensitivity of debris-covered glaciers, a decrease in summer air temperatures, and increased snowfall possibly linked to evapotranspiration from irrigated agriculture (You et al., 2017; Bolch et al., 2019; de Kok et al., 2020a; Farinotti et al., 2020). Meanwhile, increased air temperature and decreased snowfall explain the glacier mass decrease elsewhere (Bonekamp et al., 2019; de Kok et al., 2020b; Farinotti et al., 2020; Shean et al., 2020). There is high confidence that glaciers in most HKH regions have thinned, retreated and lost mass since the 1970s.

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The SROCC assessed that glaciers will lose substantial mass (high confidence) and permafrost will undergo increasing thaw and degradation (very high confidence) over high mountain regions (including the HKH), with stronger changes for higher emissions scenarios. Regional differences in warming and precipitation projections and glacier properties cause considerable differences in glacier response within High Mountain Asia (Kraaijenbrink et al., 2017). Glacier mass loss will accelerate through the 21st century, increasing with RCP after 2030 (Section 9.5.1.3; Marzeion et al., 2014). Loss of between 40 ± 25% to 69 ± 21 % of 2015 glacier volume is expected by 2100 in RCP 2.6 and RCP 8.5, respectively (Section 9.5.1.3 and Figure 9.21). Glacier mass loss is expected due to decreased snowfall, increased snowline elevations and longer melt seasons. However, due to projection uncertainties, simplicity of the models, and limited observations, there is medium confidence in the magnitude and timing of glacier mass changes (Section 9.5.1.3). Glacier mass in HKH will decline through the 21st century (high confidence), more so under high-emissions scenarios.

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Forcing dependence of the GWL response is found for global mean precipitation (Section 8.4.3), but less for regional patterns of mean precipitation changes (Cross-Chapter Box 11.1, Figure 2). Limited dependence is found for extremes, as highlighted above. In the cryosphere, elements that are quick to respond to warming like sea ice area, permafrost and snow, show little scenario dependence (Sections 9.3.1.1, 9.5.2.3 and 9.5.3.3), whereas slow-responding variables such as ice volumes of glaciers and ice sheets respond with a substantial delay and, due to their inertia, the response depends on when a certain GWL is reached. This also applies to some extent for sea level rise where, for example, the contributions of melting glaciers and ice sheets depend on the pathway followed to reach a given GWL (Section 9.6.3.4).

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The Special Report on the Ocean and Cryosphere in a Changing Climate (SROCC) provides a comprehensive assessment of recent and projected changes, specifically in snow and ice-covered areas that form a key component of the water cycle in high-elevation and high-latitude areas. High mountain regions have experienced significant warming since the early 20th century, resulting in reduced snowpack on average (Marty et al., 2017), with glaciers retreating globally since the mid-20th century (Marzeion et al., 2018; Zemp et al., 2019). Glacier shrinkage and snow cover changes have led to changes (both increases and decreases) in streamflow in many mountain regions in recent decades (Milner et al., 2017). Permafrost regions have undergone degradation and ground-ice loss due to recent warming (Lu et al., 2017). Glacier mass loss is projected to continue through the 21st century under all scenarios. In high mountain areas, low-elevation snow cover is also projected to decrease, regardless of emissions scenario. Widespread permafrost thaw is projected to continue through this century and beyond. River runoff in snow- or glacier-fed basins is projected to increase in winter and to decrease in summer (and in the annual mean) by 2100. In the oceans, the Atlantic Meridional Overturning Circulation (AMOC) will very likely weaken over the 21st century under all emissions scenarios (SROCC), with potential effects on atmospheric circulation and the water cycle at the regional scale (see also Section 8.6).

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Declining ice-sheet mass, glacier extent and Northern Hemisphere (NH) sea ice, snow cover and permafrost (Collins et al., 2013; Vaughan et al., 2013) is an expected consequence of a warming climate (Sections 2.3.2, 3.4, 4.3.2.1 and 9.39.5). A decline in mountain snow cover and increased snow and glacier melt will alter the amount and timing of seasonal runoff in mountain regions (Sections 3.4.2, 3.4.3 and 9.5). Earlier and more extensive winter and spring snowmelt (X. Zeng et al., 2018) can reduce summer and autumn runoff in snow-dominated river basins of mid–high latitudes of the NH (Rhoades et al., 2018; Blöschl et al., 2019). Since AR5, an earlier but less rapid snowmelt has been explained by reduced winter snowfall and less intense solar radiation earlier in the season (Musselman et al. , 2017; Wu et al. , 2018; Grogan et al. , 2020). Reduced snow cover also increases energy available for evaporation, which can dominate declining river discharge based on modelling of the Colorado River (Milly and Dunne, 2020). An increase in the fraction of precipitation falling as rain compared with snow can lead to declines in both streamflow and groundwater storage in regions where snowmelt is the primary source of recharge (Earman and Dettinger, 2011; Berghuijs et al., 2014). Such regions include western South America and western North America, semi-arid regions which rely on snowmelt from high mountain chains (Ragettli et al., 2016; Milly and Dunne, 2020). Rain-on-snow melt events reduce at lower altitudes due to declining snow cover but increase at higher altitudes where snow tends to be replaced by rain based on observations and modelling (Musselman et al., 2018; Pall et al., 2019), thereby altering seasonal and regional characteristics of flooding (Section 11.5).

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Reductions in snow, freshwater ice and permafrost affect terrestrial hydrology. Permafrost degradation reduces soil ice and alters the extent of thermokarst lake coverage (Section 9.5.2; M. Meredith et al., 2019). A lag between current climate change and permafrost degradation is expected, given the slow response rates in frozen ground and the fact that snow cover insulates soil from sensible heat exchanges with the air above (Hoegh-Guldberg et al. , 2018; García-García et al. , 2019; Soong et al. , 2020). Post‐wildfire areas are also linked with permafrost degradation in the Arctic based on satellite observations (Yanagiya and Furuya, 2020). An increase in spring rainfall can increase heat advection by infiltration, exacerbating permafrost thaw and leading to increased methane emissions (Section 5.4.7; Neumann et al., 2019). Increased heat transport by Arctic rivers can also contribute to earlier sea ice melt (Park et al., 2020).

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Changes in meltwater regimes from glaciers and seasonal snow packs tend to reduce the seasonal duration and magnitude of recharge (Tague and Grant, 2009). Aquifers in mountain valleys show shifts in the timing and magnitude of: (i) peak groundwater levels due to an earlier spring melt; and (ii) low groundwater levels associated with lower baseflow periods (Allen et al., 2010; Dierauer et al., 2018; Hayashi, 2020). The effects of receding alpine glaciers on groundwater systems are not well understood but long-term loss of glacier storage is estimated to reduce summer baseflow (Gremaud et al., 2009). In permafrost regions, coupling between surface water and groundwater systems may be particularly enhanced by warming (Lamontagne-Hallé et al., 2018; Lemieux et al., 2020). In areas of seasonal or perennial ground frost, increased recharge is expected despite a decrease in absolute snow volume (Okkonen and Kløve, 2011; Walvoord and Kurylyk, 2016).

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Previous assessments have concluded that recent warming has led to a reduction in low-elevation snow cover (high confidence) (SROCC), permafrost (high confidence) (SROCC), and glacier mass (high to very high confidence) (AR5; SROCC). The SROCC noted that these declines are projected to continue almost everywhere over the 21st century (high confidence), with complete glacier loss expected in regions with only small glaciers (very high confidence). The SROCC supported the AR5 finding that glacier recession would continue even without further changes in climate. The SROCC concluded that cryosphere changes had already altered the seasonal timing and volume of runoff (very high confidence), which in turn had affected water resources and agriculture (medium confidence), and projected peak water runoff had already been reached before 2019 in some of the glacier regions considered.

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Since AR5, development of new and existing processes in land surface models (LSMs) have been evaluated. These include soil freezing and permafrost (Vergnes et al. , 2014; Chadburn et al. , 2015; K. Yang et al. , 2018; Gao et al. , 2019), soil and snow hydrology (Brunke et al., 2016; Decharme et al., 2016), glaciers (Shannon et al., 2019), surface waters and rivers (Decharme et al., 2012), as well as vegetation (Bartlett and Verseghy, 2015; Betts et al., 2015; Knauer et al., 2015; Tang et al., 2015) and the representation of hydraulic gradients throughout the soil–plant–atmosphere continuum (Bonan et al., 2014). Such land surface model developments have led to significant improvements in global offline hydrological simulations driven by observed atmospheric forcings (e.g., C. Li et al., 2017; Decharme et al., 2019).

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Snowmelt is a nonl-inear process and projected changes in snowfall are also a non-linear combination of changes in total precipitation and in the fraction of solid precipitation. In cold regions, snowfall may first increase because of the increased water capacity of a warmer atmosphere and then decrease because snow falls as rain in an even warmer atmosphere. Such non-linearities can contribute to elevation, latitudinal and seasonal contrasts in the observed and projected retreat of the Northern Hemisphere (NH) snow cover (Shi and Wang, 2015; Thackeray et al., 2016). Mountain glaciers also represent source of non-linear runoff responses since the annual runoff can first increase due to additional melting and then decrease as the glaciers shrink (Kraaijenbrink et al., 2017; Shannon et al., 2019). Section 9.5.1.3 concludes with high confidence that the average annual runoff from glaciers will generally reach a peak at the latest by the end of the 21st century, and decline thereafter. This peak may have already occurred for small catchments with little ice cover, but tends to occur later in basins with large glaciers. Permafrost thawing is another mechanism which can trigger a non-linear hydrological response in the high latitudes of the NH(Walvoord and Kurylyk, 2016), whose magnitude and potential abruptness is assessed in Section 5.4.3.3.

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Chadburn, S. et al., 2015: An improved representation of physical permafrost dynamics in the JULES land-surface model. Geoscientific Model Development, 8(5), 1493–1508, doi: 10.5194/gmd-8-1493-2015.

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Lamontagne-Hallé, P., J.M. McKenzie, B.L. Kurylyk, and S.C. Zipper, 2018: Changing groundwater discharge dynamics in permafrost regions. Environmental Research Letters, 13(8), 84017, doi: 10.1088/1748-9326/aad404.

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Lemieux, J.-M. et al., 2020: Groundwater dynamics within a watershed in the discontinuous permafrost zone near Umiujaq (Nunavik, Canada). Hydrogeology Journal, 28(3), 833–851, doi: 10.1007/s10040-020-02110-4.

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Lu, Q., D. Zhao, and S. Wu, 2017: Simulated responses of permafrost distribution to climate change on the Qinghai–Tibet Plateau. Scientific Reports, 7(1), 3845, doi: 10.1038/s41598-017-04140-7.

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Neumann, R.B. et al., 2019: Warming Effects of Spring Rainfall Increase Methane Emissions From Thawing Permafrost. Geophysical Research Letters, 46(3), 1393–1401, doi: 10.1029/2018gl081274.

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Walvoord, M.A. and B.L. Kurylyk, 2016: Hydrologic Impacts of Thawing Permafrost – A Review. Vadose Zone Journal, 15(6), 1–20, doi: 10.2136/vzj2016.01.0010.

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The cryosphere is undergoing rapid changes, with increased melting and loss of frozen water mass in most regions. This includes all frozen parts of the globe, such as terrestrial snow, permafrost, sea ice, glaciers, freshwater ice, solid precipitation, and the ice sheets covering Greenland and Antarctica (Chapter 9; SROCC, IPCC, 2019b). Figure 1.4 illustrates how, globally, glaciers have been increasingly losing mass for the last fifty years. The total glacier mass in the most recent decade (2010–2019) was the lowest since the beginning of the 20th century (Sections 2.3 and 9.5).

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The SROCC found that the carbon content of Arctic and boreal permafrost is almost twice that of the atmosphere (medium confidence), and assessed medium evidence with low agreement that thawing northern permafrost regions are currently releasing additional net CH4 and CO2.

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The SROCC projected that global-scale glacier mass loss, permafrost thaw, and decline in snow cover and Arctic sea ice extent will continue in the period 2031–2050 due to surface air temperature increases (high confidence). The Greenland and Antarctic ice sheets are projected to lose mass at an increasing rate throughout the 21st century and beyond (high confidence). Sea level rise will also continue at an increasing rate. For the period 2081–2100 with respect to 1986–2005, the likely ranges of GMSL rise are projected at 0.26–0.53 m for RCP2.6 and 0.51–0.92 m for RCP8.5. For the RCP8.5 scenario, projections of GMSL rise by 2100 are higher by 0.1 m than in AR5 due to a larger contribution from the Antarctic Ice Sheet (medium confidence). Extreme sea level events that occurred once per hundred years in the recent past are projected to occur at least once per year at many locations by 2050, especially in tropical regions, under all RCP scenarios (high confidence). According to SR1.5, by 2100 GMSL rise would be around 0.1 m lower with 1.5°C global warming compared to 2°C (medium confidence). If warming is held to 1.5°C, GMSL will still continue to rise well beyond 2100, but at a slower rate and a lower magnitude. However, instability and/or irreversible loss of the Greenland and Antarctic ice sheets, resulting in a multi-metre rise in sea level over hundreds to thousands of years, could be triggered at 1.5°C–2°C of global warming (medium confidence). According to SROCC, sea level rise in an extended RCP2.6 scenario would be limited to around 1 m in 2300 (low confidence) while under RCP8.5 multi-metre sea level rise is projected by then (medium confidence).

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At constant 2017 emissions, these budgets would be depleted by about the years 2032 and 2028, respectively. Using GMST instead of GSAT gives estimates of 770 GtCO2 and 570 GtCO2, respectively (medium confidence). Each budget is further reduced by approximately 100 GtCO2 over the course of this century when permafrost and other less well represented Earth system feedbacks are taken into account.

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Improvements have also been made in the monitoring of permafrost. The Global Terrestrial Network for Permafrost (GTN-P; Biskaborn et al., 2015) provides long-term records of permafrost temperature and active layer thickness at key sites to assess their changes over time. Substantial improvements to our assessments of large-scale snow changes come from intercomparison and blending of several datasets, for snow water equivalent (Mortimer et al., 2020) and snow cover extent (Mudryk et al., 2020), and from bias corrections of combined datasets using in situ data (Sections 2.3.2.5 and 9.5.2; Pulliainen et al., 2020).

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Since AR5, more sophisticated land-use and land-cover change representations in ESMs have been developed to simulate the effects of land management on surface fluxes of carbon, water and energy (Lawrence et al., 2016), although the integration of many processes (e.g., wetland drainage, fire as a management tool) remains a challenge (Pongratz et al., 2018). The importance of nitrogen availability to limit the terrestrial carbon sequestration has been recognized (Section 5.4; Zaehle et al., 2014) and so an increasing number of models now include a prognostic representation of the terrestrial nitrogen cycle and its coupling to the land carbon cycle (Jones et al., 2016; Arora et al., 2020), leading to a reduction in uncertainty for carbon budgets (Section 5.1; Jones and Friedlingstein, 2020). As was the case in CMIP5 (Ciais et al., 2013), the land surface processes represented vary across CMIP6 models, with at least some key processes (fire, permafrost carbon, microbes, nutrients, vegetation dynamics, plant demography) absent from any particular ESM land model (Table 5.4). Ocean biogeochemical models have evolved to enhance the consistency of the exchanges between ocean, atmosphere and land, through riverine input and dust deposition (Stock et al., 2014; Aumont et al., 2015). Other developments include flexible plankton stoichiometric ratios (Galbraith and Martiny, 2015), improvements in the representation of nitrogen fixation (Paulsen et al., 2017), and the limitation of plankton growth by iron (Aumont et al., 2015). Due to the long time scale of biogeochemical processes, how the models are initialized (spun up) strategies has been shown to affect their performance in AR5 (Séférian et al., 2016).

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ESMs are driven by either emissions or concentrations scenarios. Inferring concentration changes from emissions time series requires using carbon cycle and other gas cycle models. To aid comparability across ESMs, and in order to allow participation of ESMs that do not have coupled carbon and other gas cycle models in CMIP6, most of the CMIP6 ESM experiments are so-called ‘concentration-driven’ runs, with concentrations of CO2, CH4, N2O and other well-mixed GHGs prescribed in conjunction with aerosol emissions, ozone changes and effects from human-induced land-cover changes that may be radiatively active via albedo changes (Cross-Chapter Box 1.4, Figure 2). In these concentration-driven climate projections, the uncertainty in projected future climate change resulting from our limited understanding of how the carbon cycle and other gas cycles will evolve in the future is not captured. For example, when deriving the default concentrations for these scenarios, permafrost and other carbon cycle feedbacks are considered using default settings, with a single time series prescribed for all ESMs (Meinshausen et al., 2020). Thus, associated uncertainties (Joos et al., 2013; Schuur et al., 2015) are not considered.

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In order to fully derive climate impacts, warming levels will need to be complemented by additional information, such as their associated CO2 concentrations (e.g., fertilization or ocean acidification), composition of the total radiative forcing (aerosols compared with GHGs, with varying regional distributions) or socio-economic conditions (e.g., to estimate societal impacts). More fundamentally, while a global warming level is a good proxy for the state of the climate (Cross-Chapter Box 11.1), it does not uniquely define a change in global or regional climate state. For example, regional precipitation responses depend on the details of the individual forcing mechanisms that caused the change (Samset et al., 2016); on whether the temperature level is stabilized or transient (King et al., 2020; Zappa et al., 2020); on the vertical structure of the troposphere (Andrews et al., 2010); and, in particular, on the global distribution of atmospheric aerosols (Frieler et al., 2012). Another aspect is how Earth system components with century-to-millennial response time scales, such as long-term sea level rise or permafrost thaw, are affected by global mean warming. For example, sea level rise 50 years after a 1°C warming will be lower than sea level rise 150 years after that same 1°C warming (Chapter 9).

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Biskaborn, B.K. et al., 2015: The new database of the Global Terrestrial Network for Permafrost (GTN-P). Earth System Science Data, 7(2), 245–259, doi: 10.5194/essd-7-245-2015.

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Schuur, E.A.G. et al., 2015: Climate change and the permafrost carbon feedback. Nature, 520(7546), 171–179, doi: 10.1038/nature14338.

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The chemical composition of the atmosphere (beyond CO2 and water vapour changes) is expected to change in response to a warming climate. These changes in greenhouse gases (methane, nitrous oxide and ozone) and aerosol amount (including dust) have the potential to alter the TOA energy budget and are collectively referred to as ‘non-CO2 biogeochemical feedbacks’. Methane (CH4) and nitrous oxide (N2O) feedbacks arise partly from changes in their emissions from natural sources in response to temperature change; these are assessed in (Chapter 5 Section 5.4.7; see also Figure 5.29c). Here we exclude the permafrost CH4 feedback (Section 5.4.9.1.2) because, although associated emissions are projected to increase under warming on multi-decadal to centennial time scales, on longer time scales these emissions would eventually substantially decline as the permafrost carbon pools were depleted (Schneider von Deimling et al., 2012, 2015). This leaves the wetland CH4, land N2O, and ocean N2O feedbacks, the assessed mean values of which sum to a positive feedback parameter of +0.04 [0.02 to 0.06] W m–2°C–1Section 5.4.7. Other non-CO2 biogeochemical feedbacks that are relevant to the net feedback parameter are assessed in Chapter 6 (Section 6.4.5 and Table 6.8). These feedbacks are associated with sea salt, dimethyl sulphide, dust, ozone, biogenic volatile organic compounds, lightning, and CH4 lifetime, and sum to a negative feedback parameter of –0.20 [–0.41 to +0.01] W m–2°C–1. The overall feedback parameter for non-CO2 biogeochemical feedbacks is obtained by summing the Chapter 5 and Chapter 6 assessments, which gives –0.16 [–0.37 to +0.05] W m–2°C–1. However, there is low confidence in the estimates of both the individual non-CO2 biogeochemical feedbacks as well as their total effect, as evident from the large range in the magnitudes of α from different studies, which can be attributed to diversity in how models account for these feedbacks and limited process-level understanding.

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The spread in historical surface warming across CMIP5 ESMs shows a weak correlation with inter-model differences in radiative feedback or ocean heat uptake processes but a high correlation with inter-model differences in radiative forcing owing to large variations in aerosol forcing across models (Forster et al., 2013). Likewise, the spread in projected 21st-century warming across ESMs depends strongly on which emissions scenario is employed (Section 4.3.1; Hawkins and Sutton, 2012). Strong emissions reductions would remove aerosol forcing (Section 6.7.2) and this could dominate the uncertainty in near-term warming projections (Armour and Roe, 2011; Mauritsen and Pincus, 2017; Schwartz, 2018; Smith et al., 2019). On post-2100 time scales carbon cycle uncertainty such as that related to permafrost thawing could become increasingly important, especially under high-emissions scenarios (Figure 5.30).

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MacDougall, A.H., K. Zickfeld, R. Knutti, and H.D. Matthews, 2015: Sensitivity of carbon budgets to permafrost carbon feedbacks and non-CO2 forcings. Environmental Research Letters, 10(12), 125003, doi: 10.1088/1748-9326/10/12/125003.

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Schneider von Deimling, T. et al., 2012: Estimating the near-surface permafrost-carbon feedback on global warming. Biogeosciences, 9(2), 649–665, doi: 10.5194/bg-9-649-2012.

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Schneider von Deimling, T. et al., 2015: Observation-based modelling of permafrost carbon fluxes with accounting for deep carbon deposits and thermokarst activity. Biogeosciences, 12, 3469–3488, doi: 10.5194/bg-12-3469-2015.

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This chapter provides a holistic assessment of the physical processes underlying global and regional changes in the ocean, cryosphere and sea level, as well as improved understanding of observed, attributed and projected future changes since the IPCC Fifth Assessment Report (AR5) and the Special Report on the Ocean and Cryosphere in a Changing Climate (SROCC; see outline in Figure 9.1). The ocean and cryosphere (defined as the frozen components of the Earth system such as sea ice, ice sheets, glaciers, permafrost and snow) exchange heat and freshwater with the atmosphere and each other (Figure 9.2). In a warming climate, the combined effects of thermal expansion of seawater and melting of the terrestrial cryosphere result in global mean sea level rise (Box 9.1).

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There are other advances in scientific understanding. In the cryosphere, this chapter assesses how fast-responding elements (sea ice, permafrost and snow; Sections 9.3, 9.5.2 and 9.5.3) track warming levels across observations and projections independent of scenario, process understanding of uncertainty in Antarctic Ice Sheet projections (Section 9.4.2 and Box 9.4) and new insight into thresholds for Arctic sea ice (Section 9.3.1.1) and Greenland and West Antarctic ice sheets (Sections 9.4.1.4 and 9.4.2.6). In the ocean, process understanding of ocean heat uptake (Section 9.2.2.1 and Cross-Chapter Box 5.3) and observed changes in ocean stratification (Section 9.2.1.3) have implications for ocean biogeochemistry are also important.

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Land-water storage includes surface water, soil moisture, groundwater storage and snow, but excludes water stored in glaciers and ice sheets. Changes in land-water storage can be caused either by direct human intervention in the water cycle (e.g., storage of water in reservoirs by building dams in rivers, groundwater extraction for consumption and irrigation, or deforestation) or by climate variations (e.g., changes in the amount of water in internally drained lakes and wetlands, the canopy, the soil, the permafrost and

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This section focuses on the physical aspects of permafrost (perennially frozen ground) as an element of the climate system, drawing on the assessment of observed global permafrost changes provided in Section 2.3.2.5, and more specifically model evaluation and projections. The permafrost carbon feedback is assessed in Box 5.1. Section 12.4 of this Report provides permafrost information relevant to impacts and risk on regional scales.

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The current extent of the global permafrost region is about 22 ± 3×106km2(Gruber, 2012). Permafrost underlies about 15% of Northern Hemisphere land and more than 50% of the unglacierized land north of 60°N (Zhang et al., 1999; Gruber, 2012; Obu et al., 2019). It is also found in high-altitude areas of mountain ranges in both hemispheres – estimated in SROCC (Hock et al., 2019b) as representing about 27–29% of the global permafrost area (medium confidence) and most unglacierized areas in Antarctica (Vieira et al., 2010; Obu et al., 2020). Ground ice volume in permafrost is variable, reaching up to 90% in syngenetic permafrost deposits (Kanevskiy et al., 2013; Gilbert et al., 2016). The SROCC (Meredith et al., 2019) reported medium confidence in the estimation that Earth’s total perennial ground ice volume is equivalent to 2–10 cm of global sea level (Zhang et al., 2000). There is no evidence suggesting that a large part of this volume, if melted, would run off and contribute to global sea level. Therefore, and because of the modest total volume of mobilizable water, the contribution of permafrost thaw to past and future sea level budgets is usually neglected (see Section 9.6.3.2).

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Permafrost changes mostly refer to changes in extent, temperature and active layer thickness (ALT). The SROCC (Hock et al., 2019b; Meredith et al., 2019) reported with very high confidence that record high permafrost temperatures at the depth of the zero annual amplitude (the depth about 10–20 m below the surface where the seasonal soil temperature cycle vanishes) were attained in recent decades in the Northern circumpolar permafrost region, high confidence that permafrost has warmed over recent decades in many mountain ranges, and overall very high confidence that global warming over the last decades has led to widespread permafrost warming. As reported in SROCC, the global (polar and mountain) permafrost temperature has increased at 0.29°C ± 0.12°C near the depth of zero annual amplitude between 2007 and 2016 (Biskaborn et al., 2019). Stronger warming has been observed in the continuous permafrost zone (0.39°C ± 0.15°C) compared to the discontinuous zone (0.20°C ± 0.10°C), consistent with the fact that, near the melting point, a large amount of energy is required for melting the ice (Figure 9.22), and because of the reduced effect of Arctic amplification in more southerly locations (Romanovsky et al., 2017). This is consistent with longer-term Arctic trends from deep boreholes shown in Figure 2.22. Mountain permafrost temperature trends are heterogeneous, reflecting variations in local conditions such as topography, surface type, soil texture and snow cover, but again, generally weaker warming rates are observed in warmer permafrost at temperatures close to 0°C, particularly when ice content is high (e.g., Mollaret et al., 2019; Noetzli et al., 2019; PERMOS, 2019). In summary, strong variability in recent permafrost temperature trends is linked to local conditions, regionally varying temperature trends, and the thermal state of permafrost itself. However, as discussed in Section 2.3.2.5, there is overall high confidence in the observed increases in permafrost temperature over the past three to four decades throughout the permafrost regions.

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There is medium confidence that the observed acceleration and destabilization of rock glaciers is related to warming temperatures and increase in water content at the permafrost table in recent decades (Deline et al., 2015; Cicoira et al., 2019; Marcer et al., 2019; PERMOS, 2019; Kenner et al., 2020). There is also medium confidence that observed increases in size and frequency of rock avalanches are linked to permafrost degradation in rock walls (Ravanel et al., 2017; Patton et al., 2019; Tapia Baldis and Trombotto Liaudat, 2019). In summary, there is medium confidence that mountain permafrost degradation at high altitude has increased the instability of mountain slopes in the past decade.

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The SROCC assessed with high confidence that the extent of subsea permafrost, formed before submersion on Arctic continental shelves during the last deglaciation, is much reduced compared to older studies that had estimated the entire formerly exposed Arctic shelf area to be underlain by permafrost. This is supported by observations (Shakhova et al., 2017) that show rapid thaw of recently submerged permafrost on the East Siberian Shelf. A modelling study (Overduin et al., 2019) estimates that 97% of permafrost under Arctic shelves is currently thinning.

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Based on multiple studies, there is medium confidence that widespread retreat of coastal permafrost is accelerating in the Arctic (Günther et al., 2015; Cunliffe et al., 2019; Isaev et al., 2019). There is also consistent evidence of complete permafrost thaw in areas of discontinuous and sporadic permafrost since about 1980, but this evidence is geographically scattered (Camill, 2005; Kirpotin et al., 2011; James et al., 2013; B.M. Jones et al., 2016; Borge et al., 2017; Chasmer and Hopkinson, 2017; Gibson et al., 2018). In spite of increasing evidence of landscape changes from site studies and remote sensing, quantifying permafrost extent change remains challenging because it is a subsurface phenomenon that cannot be observed directly (Jorgenson and Grosse, 2016; Trofaier et al., 2017). A modelling study for the Qinghai-Tibet Plateau between the 1960s and the 2000s (Ran et al., 2018) suggests transition from permafrost to seasonally frozen ground over an area of more than 400,000 km2. In summary, there is medium confidence that complete permafrost thaw in recent decades is a common phenomenon in discontinuous and sporadic permafrost regions. In addition, paleoclimatic evidence presented in Section 2.3.2.5 confirms a long-term sensitivity of permafrost extent to climatic variations, although an analysis of North American speleothem records over the last two glacial cycles indicates that this apparent high sensitivity could be a consequence of regional-scale variability (Batchelor et al., 2019).

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As stated in AR5 (Flato et al., 2013), coupled models contributing to CMIP5 showed large inter-model variability of permafrost extent due to deficiencies in reproducing surface characteristics and processes (Koven et al., 2013), particularly thermal properties of the ground and snow. These deficiencies led SROCC (Meredith et al., 2019) to express only medium confidence in the models’ capacity to correctly project the magnitude of future permafrost changes, in spite of high confidence in the models’ projection of a general thaw depth increase and a substantial loss of shallow permafrost. The SROCC further noted that several types of physical ‘pulse’ disturbances, in particular fire and thermokarst formation, are usually not represented in coupled climate models. This has been discussed in detail in SROCC, which assessed that there is high confidence that permafrost degradation through fire (Jones et al., 2015; Gibson et al., 2018) is currently occurring faster in some well-studied regions than during the first half of the 20th century, and medium confidence that thermokarst formation, to which about 20% of the northern permafrost region is vulnerable (Olefeldt et al., 2016), can lead to faster large-scale permafrost degradation in response to climate change.

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Since SROCC, dedicated modelling of the evolution of ice- and organic-rich permafrost in the north-east Siberian lowlands (Nitzbon et al., 2020) has shown that not representing thermokarst-inducing processes in ice-rich terrain leads to a systematic underestimation of the rapidity and magnitude of permafrost thaw. Simplified inventory-based modelling (Turetsky et al., 2020) points towards similar conclusions. Although these pulse disturbances still need to be represented in CMIP-type models, there have been many new developments to that type of model since CMIP5 and AR5. Soil freezing and its thermal and hydrological effects are now included in a large number of land-surface modules that are part of the CMIP6 ensemble (S. Chadburn et al., 2015; Hagemann et al., 2016; Cuntz and Haverd, 2018; Guimberteau et al., 2018; Yokohata et al., 2020), sometimes allowing for the effects of excess ice (Lee et al., 2014). Improved representation of snow insulation in models has led to more realistic simulated permafrost extents (e.g., Paquin and Sushama, 2015). In a post-CMIP5 ensemble of land-surface models driven by observed meteorological conditions (McGuire et al., 2016), inter-model spread was substantially reduced when the ensemble was restricted to models that appropriately represented the effect of snow insulation on the underlying soil (W. Wang et al., 2016). More detailed descriptions of high-latitude vegetation characteristics, vegetation dynamics, and snow-vegetation interactions have been included in several models since AR5 (S.E. Chadburn et al., 2015; Porada et al., 2016; Druel et al., 2017).

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A total soil column depth of at least about 10 m is required to adequately represent the dampening effect of seasonal-scale heat exchanges between shallow and deeper ground, and thus to correctly simulate ALT (Lawrence et al., 2008; Ekici et al., 2015). However, many CMIP6 models still have shallower total soil columns (Burke et al., 2020) and the proportion of models with deeper total soil columns has not increased since CMIP5 (Koven et al., 2013). Another recently identified process, usually not represented in the current (CMIP6) generation of climate models (Zhu et al., 2019), is warming-driven decomposition and burning of organic material that provides strong thermal insulation of underlying ground. Decay of the insulating organic material can lead to increased permafrost thaw, creating a positive feedback loop.

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In spite of the aforementioned structural improvements to many models, the simulated current permafrost extent from available CMIP6 models shows no substantial improvement with respect to CMIP5 (see Figure 9.22a). The extent of the region where permafrost is simulated within the top 15 m in the Northern Hemisphere for the 1979–1998 period is characterized by very large scatter in the coupled CMIP5 and CMIP6 historical simulations compared to estimates of the present permafrost extent based on multiple observational lines of evidence (Zhang et al., 1999) and models based on satellite observations and reanalyses (Gruber, 2012; Obu et al., 2019). Outliers with very low simulated permafrost extent are models that have only a very shallow soil column (leading to an underestimate of thermal inertia at depth) and do not take into account soil water phase changes. These inadequacies lead to an overestimate of seasonal thaw depth, exceeding the total thickness of the models’ soil columns (Burke et al., 2020). Excessive simulated permafrost extent can in several cases be traced to insufficient thermal insulation by the winter snow cover (Burke et al., 2020).

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Figure 9.22a also shows that the corresponding land-atmosphere simulations with prescribed observed sea surface temperatures and sea ice concentrations, and the land-only simulations with prescribed reanalysis-based meteorological forcing, do not provide an improved simulation of the current permafrost extent, although, by construction, they can be expected to exhibit lower land surface climate biases. This further points to deficiencies in the land modules as the main reason for biases, consistent with conclusions drawn from the analysis of CMIP5 output (Koven et al., 2013), as reported in SROCC and AR5.

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In spite of more realistic description of permafrost-related processes in many coupled climate models, the CMIP6 models still produce a very scattered ensemble of estimates of current permafrost extent, and there is high confidence that this is strongly linked to deficiencies of the representation of soil processes. Furthermore, current-generation climate models tend to neglect several physical disturbances that can lead to faster permafrost thaw. Because of large uncertainties in the future evolution of these drivers (see SROCC), there is limited evidence that these shortcomings lead to an underestimate of permafrost degradation rates in response to climate change in the CMIP6 ensemble. In summary, there is high confidence that coupled models correctly simulate the sign of future permafrost changes linked to surface climate changes, but only medium confidence in the amplitude and timing of the transient response.

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The AR5 (Collins et al., 2013) and SROCC (Meredith et al., 2019) (based on available CMIP5 output) both expressed high confidence that future pan-Arctic thaw depth will increase and near-surface permafrost extent will decrease under future global warming, and medium confidence in the magnitude of the simulated changes because of model deficiencies and the large spread of the results.

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The equilibrium sensitivity of permafrost extent to stabilized global mean warming has been inferred (by constraining CMIP5 output with diagnosed relationships between the observed present-day spatial distribution of permafrost and air temperature) to be about 4.0×106km2°C–1(Chadburn et al., 2017) for global surface air temperature (GSAT) changes with respect to the present below about +3°C. This equilibrium permafrost sensitivity, relevant for assessing long-term permafrost changes at a stabilized warming level, is about 20% higher than the transient centennial-scale near-surface permafrost extent sensitivity (diagnosed from seasonal thaw down to 3 m depth) suggested by direct analysis of CMIP5 output (Slater and Lawrence, 2013). Compared to these and other studies reported in AR5 and SROCC (Koven et al., 2013), the recently suggested equilibrium extent sensitivity to GSAT changes of about 1.5×106km2°C–1based on idealized ground temperature modelling (Liu et al., 2021) appears unrealistically low.

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A strong transient temperature sensitivity of the volume of perennially frozen soil in the top 3 m below the surface is consistently suggested by the available CMIP6 models (Figure 9.22b). Relative to the current volume, the transient sensitivity of the modelled permafrost volume in the top 3 m to GSAT changes (with respect to the 1995–2014 average and up to +3°C change, that is, about up to +4°C with respect to pre-industrial levels) is about 25 ± 5 % °C–1(Burke et al., 2020), but there is only medium confidence in this value and 1 standard deviation uncertainty range because of the model deficiencies discussed in 9.5.2.2. It is important to note that permafrost loss will not be limited to the top 3 m, with delayed response of deeper permafrost. The simulated transient temperature sensitivity of permafrost volume is slightly stronger in the SSP1-2.6 scenario than in other SSPs because subsurface temperature lag increases with higher atmospheric warming rates, particularly when ground ice melting induces additional delays.

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Due to the role of air temperature as a major driver of permafrost change, SROCC (Hock et al., 2019b) expressed very high confidence that permafrost in high mountain regions is expected to undergo increasing thaw and degradation during the 21st century, with stronger consequences expected for higher greenhouse gas emissions scenarios. Recently published studies (e.g., Zhao et al., 2019) support this SROCC assessment.

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In summary, based on high agreement across CMIP6 and older model projections, fundamental process understanding, and paleoclimate evidence, it is virtually certain that permafrost extent and volume will shrink as global climate warms.

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In the updated projections, a statistical relationship is applied, linking historical and future SSP global population to dam impoundment and groundwater extraction (Rahmstorf et al., 2012; Kopp et al., 2014). The population/groundwater depletion relationship is calibrated based on the same studies used in AR5 (Konikow, 2011; Wada et al., 2012), reduced by about 20% to account for water retained on land (Wada et al., 2016). The population/dam impoundment relationship is calibrated based on Chao et al. (2008). However, while historically dam impoundment has been declining with population, recent literature shows that planned dam construction considerably exceeds the historical trend (Zarfl et al., 2015; Hawley et al., 2020). Over 2020–2040, the impoundment contribution to GMSL rise based on past trends would be about –0.1 mm yr–1, compared to about –0.5 mm yr–1if all currently planned dams are built (Hawley et al., 2020) and the statistical projection is therefore augmented by an additional –0.4 to 0.0 mm yr–1over 2020–2040 to account for the possible effects of planned dam construction. As in AR5 and SROCC, climatically driven changes to land-water storage (LWS) have not been included in published sea level projections, as they are not well quantified (e.g., Jensen et al., 2019) or are considered negligible (e.g., permafrost, Section 9.5.2). This approach yields a likely global-mean land-water storage contribution (Figure 9.27, Table 9.8) that is slightly lower and narrower than the AR5 and SROCC likely ranges. Since the projections are explicitly population driven, these projections also exhibit a weak scenario dependence, with a contribution around 0.01 m higher under SSP3 than under other scenarios.

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Paquin, J.P. and L. Sushama, 2015: On the Arctic near-surface permafrost and climate sensitivities to soil and snow model formulations in climate models. Climate Dynamics, 44(1–2), 203–228, doi: 10.1007/s00382-014-2185-6.

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Patton, A.I., S.L. Rathburn, and D.M. Capps, 2019: Landslide response to climate change in permafrost regions. Geomorphology, 340, 116–128, doi: 10.1016/j.geomorph.2019.04.029.

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PERMOS, 2019: Permafrost in Switzerland 2014/2015 to 2017/2018. [Noetzli, J., C. Pellet, and B. Staub (eds.). Glaciological Report (Permafrost) No. 16–19, Cryospheric Commission of the Swiss Academy of Sciences, 104 pp., doi: 10.13093/permos-rep-2019-16-19.

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Porada, P., A. Ekici, and C. Beer, 2016: Effects of bryophyte and lichen cover on permafrost soil temperature at large scale. The Cryosphere, 10(5), 2291–2315, doi: 10.5194/tc-10-2291-2016.

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Ran, Y., X. Li, and G. Cheng, 2018: Climate warming over the past half century has led to thermal degradation of permafrost on the Qinghai–Tibet Plateau. Cryosphere, 12, 595–608, doi: 10.5194/tc-12-595-2018.

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Ravanel, L., F. Magnin, and P. Deline, 2017: Impacts of the 2003 and 2015 summer heatwaves on permafrost-affected rock-walls in the Mont Blanc massif. Science of the Total Environment, 609, 132–143, doi: 10.1016/j.scitotenv.2017.07.055.

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Raynolds, M.K. et al., 2014: Cumulative geoecological effects of 62 years of infrastructure and climate change in ice-rich permafrost landscapes, Prudhoe Bay Oilfield, Alaska. Global Change Biology, 20(4), 1211–1224, doi: 10.1111/gcb.12500.

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Romanovsky, V. et al., 2017: Changing Permafrost and its Impacts. In: Snow, Water, Ice and Permafrost in the Arctic (SWIPA) 2017. Arctic Monitoring and Assessment Programme (AMAP), Oslo, Norway, pp. 65–136, www.amap.no/documents/doc/snow-water-ice-and-permafrost-in-the-arctic-swipa-2017/1610.

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Shakhova, N. et al., 2017: Current rates and mechanisms of subsea permafrost degradation in the East Siberian Arctic Shelf. Nature Communications, 8, 15872, doi: 10.1038/ncomms15872.

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Slater, A.G. and D.M. Lawrence, 2013: Diagnosing present and future permafrost from climate models. Journal of Climate, 26(15), 5608–5623, doi: 10.1175/jcli-d-12-00341.1.

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Streletskiy, D.A. et al., 2017: Thaw Subsidence in Undisturbed Tundra Landscapes, Barrow, Alaska, 1962–2015. Permafrost and Periglacial Processes, 28(3), 566–572, doi: 10.1002/ppp.1918.

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Tapia Baldis, C. and D. Trombotto Liaudat, 2019: Rockslides and rock avalanches in the Central Andes of Argentina and their possible association with permafrost degradation. Permafrost and Periglacial Processes, 30(4), 330–347, doi: 10.1002/ppp.2024.

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Trofaier, A.M., S. Westermann, and A. Bartsch, 2017: Progress in space-borne studies of permafrost for climate science: Towards a multi-ECV approach. Remote Sensing of Environment, 203, 55–70, doi: 10.1016/j.rse.2017.05.021.

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Turetsky, M.R. et al., 2020: Carbon release through abrupt permafrost thaw. Nature Geoscience, 13(2), 138–143, doi: 10.1038/s41561-019-0526-0.

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Vieira, G. et al., 2010: Thermal state of permafrost and active-layer monitoring in the antarctic: Advances during the international polar year 2007–2009. Permafrost and Periglacial Processes, 21(2), 182–197, doi: 10.1002/ppp.685.

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Wang, W. et al., 2016: Evaluation of air–soil temperature relationships simulated by land surface models during winter across the permafrost region. The Cryosphere, 10(4), 1721–1737, doi: 10.5194/tc-10-1721-2016.

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Yokohata, T. et al., 2020: Model improvement and future projection of permafrost processes in a global land surface model. Progress in Earth and Planetary Science, 7(1), 69, doi: 10.1186/s40645-020-00380-w.

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Zhang, T., J.A. Heginbottom, R.G. Barry, and J. Brown, 2000: Further statistics on the distribution of permafrost and ground ice in the Northern Hemisphere. Polar Geography, 24(2), 126–131, doi: 10.1080/10889370009377692.

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Zhang, T., R.G. Barry, K. Knowles, J.A. Heginbottom, and J. Brown, 1999: Statistics and characteristics of permafrost and ground-ice distribution in the Northern Hemisphere. Polar Geography, 23(2), 132–154, doi: 10.1080/10889379909377670.

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Zhao, S., S. Zhang, W. Cheng, and C. Zhou, 2019: Model Simulation and Prediction of Decadal Mountain Permafrost Distribution Based on Remote Sensing Data in the Qilian Mountains from the 1990s to the 2040s. Remote Sensing, 11(2), 183, doi: 10.3390/rs11020183.

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Landslides, mudslides, rockfalls and other mass movements can lead to fatalities, destroy infrastructure and housing stock, and block critical transportation routes. Climate models cannot resolve these complex slope failure processes (nor triggering mechanisms such as earthquakes), so most studies rely on proxies or conditions conducive to slope failure (Gariano and Guzzetti, 2016; Ho et al., 2017). Common indices include precipitation intensity-duration thresholds (Brunetti et al., 2010; Khan et al., 2012; Melchiorre and Frattini, 2012) and thresholds related to antecedent wet periods and extreme rainfall intensities (Alvioli et al., 2018; Monsieurs et al., 2019). Landslides and rockfalls may also be exacerbated by permafrost thaw and receding glaciers in polar and mountain areas (Cook et al., 2016; Haeberli et al., 2017; Patton et al., 2019).

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Changes in permafrost temperature, extent and active layer thickness are metrics that track how permafrost thaw below, for example, roads, airstrips, rails and building foundations in high-latitude and mountain regions may destabilize settlements and critical infrastructure (Pendakur, 2016; Derksen et al., 2018; Duvillard et al., 2019; Olsson et al., 2019; Streletskiy et al., 2019). Warmer conditions can also affect ecosystems, built infrastructure and water resources through thawing of especially ice-rich permafrost (≥20% ice content) and by thawing of ice wedges (Shiklomanov et al., 2017; Hjort et al., 2018), creation of thermokarst ponds and increased subsurface drainage for polar and high-mountain wetlands (Walvoord and Kurylyk, 2016; Farquharson et al., 2019) and the release of water pollutants such as mercury (Burkett, 2011; Schaeffer et al., 2012; Schuster et al., 2018).

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Effective management of coastal ecosystems, cities, settlements, beaches and infrastructure requires information about coastal erosion driven by storm surge, waves and sea level rise (Dawson et al., 2009; Hinkel et al., 2013; Harley et al., 2017; Mentaschi et al., 2017). Coastal erosion is generally accompanied by shoreline retreat, which can occur as a gradual process (e.g., due to sea level rise) or as an episodic event due to storm surge and/or extreme waves, especially when combined with high tide (Ranasinghe, 2016). The most commonly used shoreline retreat index is the magnitude of shoreline retreat by a pre-determined planning horizon such as 50 or 100 years into the future. Commonly used metrics for episodic coastal erosion include the beach erosion volume due to the 100-year recurrence storm wave height, the full exceedance probability distribution of coastal erosion volume (Li et al., 2014a; Pender et al., 2015; Ranasinghe and Callaghan, 2017) and the cumulative storm energy and storm power index (Godoi et al., 2018). The destruction or overtopping of barrier islands may lead to irreversible changes in the physical system as well as in coastal ecosystems (Carrasco et al., 2016; Zinnert et al., 2019). Shoreline position change rates along inlet-interrupted coasts may also be affected by changes in river flows and fluvial sediment supply (Hinkel et al., 2013; Bamunawala et al., 2018; Ranasinghe et al., 2019). Permafrost thaw and Arctic sea ice decline also reduce natural coastal protection from wave erosion for communities and industry (Forbes, 2011; Melvin et al., 2017).

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The assessed direction of change in CIDs for Africa and associated confidence levels are illustrated in Table 12.3. No relevant literature could be found for permafrost and hail, although these phenomena may be relevant in parts of the continent.

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According to the region definitions given in Chapter 1, Asia is divided into 11 regions: the Arabian Peninsula (ARP), Western Central Asia (WCA), West Siberia (WSB), East Siberia (ESB), the Russian Far East (RFE), East Asia (EAS), East Central Asia (ECA), the Tibetan Plateau (TIB), South Asia (SAS), South East Asia (SEA) and the Russian Arctic Region (RAR). CID changes in RAR are assessed in the Polar Region section (Section 12.4.9). As assessed in previous IPCC Reports, major concerns in Asia are associated particularly with droughts and floods in all regions, heat extremes in SAS and EAS, sand-dust storms in WCA, tropical cyclones in SEA and EAS, snow cover and glacier changes in ECA and the Hindu Kush Himalaya (HKH) region, and sea ice and permafrost thawing in northern Asia.

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Landslide: The majority of non-seismic fatal landslide events were triggered by rainfall, and Asia is the dominant geographical area of landslide distribution (Froude and Petley, 2018). Floods and landslides are the most frequently occurring natural hazards in the eastern Himalayas and hilly regions, particularly caused by torrential rain during the monsoon season (Gaire et al., 2015; Syed and Al Amin, 2016). They accounted for nearly half of the events recorded in the countries of the HKH region (Vaidya et al., 2019). Intense monsoon rainfall in northern India and western Nepal in 2013, which led to landslides and one of the worst floods in history, has been linked to increased loading of GHG and aerosols (Cho et al., 2016). Due to an increase of heavy precipitation and permafrost thawing, an increase in landslides is expected in some areas of Asia, such as northern Taiwan (China), some South Korean mountains, Himalayan mountains, and permafrost territories of Siberia, and the increase is expected to be the greatest over areas covered by current glaciers and glacial lakes (medium confidence, medium evidence) (Kim et al., 2015; Kharuk et al., 2016; C.-W. Chen et al., 2019; Kirschbaum et al., 2020).

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Compared with the 1990s, the number of lakes in TIB in the 2010s decreased by 2%, whereas total lake area expanded by 25% (S. Wang et al., 2020) due to the joint effect of precipitation increase and glacier retreat. Many new lakes are predicted to form as a consequence of continued glacier retreat in the Himalaya-Karakoram region (Linsbauer et al., 2016). As many of these lakes will develop at the immediate foot of steep icy peaks with degrading permafrost and decreasing slope stability, the risk of glacier lake outburst floods and floods from landslides into moraine-dammed lakes is increasing in Asian high mountains (high confidence) (Haeberli et al., 2017; Kapitsa et al., 2017; Bajracharya et al., 2018; Narama et al., 2018; S. Wang et al., 2020).

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Permafrost : Permafrost is thawing in Asia (high confidence). Temperatures in the cold continuous permafrost of north-eastern East Siberia rose from the 1980s up to 2017, and the active layer thicknesses in Siberia and Russian Far East generally increased from late 1990s to 2017 (Romanovsky et al., 2018). The change in mean annual ground temperature for northern Siberia is about +0.1 to +0.3°C per decade since 2000 (Romanovsky et al., 2018). Ground temperature in the permafrost regions of TIB (taking 40% of TIB currently) increased (0.02–0.26°C per decade for different boreholes) during 1980 to 2018, and the active layer thickened at a rate of 19.5 cm per decade (L. Zhao et al., 2020). There is high confidence that permafrost in Asian high mountains will continue to thaw and the active layer thickness will increase (Bolch et al., 2019). The permafrost area is projected to decline by 13.4–27.7% and 60–90% in TIB (L. Zhao et al., 2020) and 32% ± 11% and 76% ± 12% in Russia (Guo and Wang, 2016) by the end of the 21st century under the RCP2.6 and RCP8.5 scenarios respectively (high confidence).

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In summary, snowpack and glaciers are projected to continue decreasing and permafrost to continue thawing in Asia (high confidence). There is medium confidence of increasing heavy snowfall in some regions, but limited evidence on future changes in hail and snow avalanches.

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Landslide: Based on local slope characteristics, lithology and seismic activity, the South Island and the eastern half of the North Island of New Zealand are vulnerable to landslide occurrence (Broeckx et al., 2020). The potential for land and rockslides increases with, amongst other factors, total precipitation rates, precipitation intensity, mountain permafrost thaw rates, glacier retreat and air temperature (Crozier, 2010; Allen and Huggel, 2013; Gariano and Guzzetti, 2016; IPCC, 2019a). Given the increase of the magnitude of these physical variables in areas that are already highly susceptible to mass movements (MfE, 2018), there is low confidence that the occurrence of landslides will increase under future climate conditions.

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Permafrost: There is limited information on the ongoing changes and projections of permafrost conditions in the region. Based on model projections under the IPCC A1B scenario, permafrost areas in the Bolivian Andes will eventually be lost, but this could take years to decades or longer depending on permafrost thickness (Rangecroft et al., 2016).

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In conclusion, glacier volume loss and permafrost thawing will continue in the Andes Cordillera under all climate scenarios (high confidence), causing important reductions in river flow and potentially high-magnitude glacial lake outburst floods.

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Permafrost: In Europe, permafrost is found in high mountains and in Scandinavia, as well as in Arctic Islands (e.g., Iceland, Novaya Zemlia or Svalbard). In recent decades permafrost has been lost (Section 9.5.2) and accelerated warming at high altitudes and latitudes has favoured an increase of permafrost temperatures of the order of 0.2 ± 0.1°C between 2007 and 2016 (Romanovsky et al., 2018; Noetzli et al., 2019). Over the 21st century, permafrost is very likely to undergo increasing thaw and degradation under all scenarios (Hock et al., 2019) and it is virtually certain that permafrost extent and volume will decrease with increase of global warming (Section 9.5.2).

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Permafrost thawing is projected to affect the frequency and magnitude of high-mountain mass wasting processes (Stoffel and Huggel, 2012). The temporal frequency of periglacial debris flows in the Alps is unlikely to change significantly by the mid-21st century but is likely to decrease during the second part of the century under the A1B scenario, especially in summer (Stoffel et al., 2011, 2014). There is medium confidence that most of the Northern Europe periglacial processes will disappear by the end of the century, even in the RCP2.6 scenario (Aalto et al., 2017). The magnitude of debris flow events might increase (Lugon and Stoffel, 2010) and the debris-flow season may last longer under the A1B scenario (Stoffel and Corona, 2018). Quantitative data for the European Alps is highly site dependent (Haeberli, 2013).

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In summary, future snow cover extent and seasonal duration will reduce (high confidence) and it is virtually certain that glaciers will continue to shrink. A reduction of glacier ice volume is projected in the European Alps and Scandinavia (high confidence). There is high confidence that permafrost will undergo increasing thaw and degradation over the 21st century. Most of the Northern Europe periglacial will disappear by the end of the century even for a lower emissions scenario (medium confidence) and the debris-flow season may last longer in a warming climate (medium confidence).

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Landslide: There is growing yet limited evidence for unique climate-driven changes in landslide and rockfall hazards in North America, even as theory suggests decreases in slope and rockface stability due to more intense rainfall, rain-on-snow events, mean warming, permafrost thaw, glacier retreat, and coastal erosion (Cloutier et al., 2017; Coe et al., 2018; Handwerger et al., 2019; Hock et al., 2019; Patton et al., 2019) although dry trends can decelerate mass movements (Bennett et al., 2016). Landslide frequency has increased in British Columbia (Canada; Geertsema et al., 2006) and is expected to increase in North-Western North America given the combination of these factors (medium confidence) (Gariano and Guzzetti, 2016). Cloutier et al. (2017)projected an increase in landslides in western Canada due to wetter overall conditions and reduced return period for extreme rainfall. Robinson et al. (2017) used scenarios based upon projection of 50-year recurrence of 7-day precipitation periods to highlight the potential for increased landslide hazards near Seattle (USA). Broad projections for the USA are more uncertain given increases in evapotranspiration that will counteract precipitation changes over much of the country (Coe, 2016).

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Permafrost: Warmer ground temperatures are expected to extend the geographical extent and depth of permafrost thaw across northern North America (very high confidence) (Section 9.5.2). Observations across Canada show that permafrost temperature is increasing and the active layer is getting thicker (Section 2.3.2.5; Derksen et al., 2018; Biskaborn et al., 2019; Romanovsky et al., 2020). Slater and Lawrence (2013) note that the RCP8.5 end-of-century period in North America only has shallow permafrost as the most probable condition in the Canadian Archipelago. Melvin et al. (2017) noted the loss of shallow permafrost in five RCP8.5 CMIP5 models across a wide swathe of southern Alaska by 2050, along with increases of active layer thickness. There is high confidence in continued reductions in mountain near-surface permafrost area with high spatial variability given local snow and temperature changes (Section 9.5.2; Peng et al., 2018; Hock et al., 2019).

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Observations and projections agree that snow and ice CIDs over North America are characterized by reduction in glaciers and the seasonality of snow and ice formation, loss of shallow permafrost, and shifts in the rain/snow transition line that alters the seasonal and geographic range of snow and ice conditions in the coming decades (very high confidence).

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Several recent climate assessments of polar regions describe robust patterns of recent and future climatic changes driving impacts and risk for polar environmental, societal, and economic assets. These have included the IPCC SROCC (Meredith et al., 2019), the Report on Snow, Water, Ice and Permafrost in the Arctic (AMAP, 2017), and national assessments for the USA (Markon et al., 2018) and Canada (Derksen et al., 2018). This section examines Greenland and Iceland, the Russian Arctic, Antarctica, and the Arctic portions of Northern Europe and North America (Figure 1.18c).

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Landslide and snow avalanche: There is a growing number of studies on mass movements in polar regions. Although there is low confidence in widespread observational trends for landslides or snow avalanches, a rise in the number of future landslides is supported by strong links to increases in heavy precipitation, glacier retreat, and thawing of ice-rich permafrost that can lead to retrogressive thaw slumps in Arctic regions (Section 2.3.2.5; Kokelj et al., 2015; Derksen et al., 2018; Lewkowicz and Way, 2019; Patton et al., 2019; Ward Jones et al., 2019).

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Aridity and drought: Recent decades have seen a general decrease in Arctic aridity, with projections indicating a continuing trend towards reduced aridity (high confidence) as increased moisture transport leads to higher precipitation, humidity and streamflow (Meredith et al., 2019) and a corresponding decrease in dry days (Khlebnikova et al., 2019a). There is low confidence overall of recent or projected drought changes in polar regions (Section 11.9) even as increasing evidence shows that drainage from permafrost thaw, higher potential evapotranspiration, and changing seasonal patterns of melt have caused lake reduction and soil moisture deficits in several areas that match with projections of future drought increase despite overall precipitation increases (Andresen and Lougheed, 2015; Bring et al., 2016; Spinoni et al., 2018a; Feng et al., 2019; Finger Higgens et al., 2019).

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Permafrost: Observations from recent decades (assessed in Section 9.5.2 and Section 2.3.2.5) show increases in permafrost temperature (very high confidence) and active layer thickness (medium confidence) across the Arctic (AMAP, 2017; Derksen et al., 2018; Markon et al., 2018; Biskaborn et al., 2019; Farquharson et al., 2019; Meredith et al., 2019; Romanovsky et al., 2020). Section 9.5.2 noted that observations of active layer thickness in Antarctica are too limited to assess long-term trends (see also Hrbáček et al., 2018; Biskaborn et al., 2019). Future projections indicate continuing increases in permafrost temperature and active layer thickness with loss of permafrost across the Arctic (Section 9.5.2). Streletskiy et al. (2019) noted that changes to Russian permafrost temperature and active layer thickness are most pronounced in areas where permafrost is continuous (underlying >90% of landmass). CMIP5 analyses by Slater and Lawrence (2013) projected that, by RCP8.5 2100, shallow (<3 m) permafrost would be most probable only in portions of the Canadian Arctic Archipelago and the Russian Arctic coastal and eastern upland regions.

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Coastal flooding and erosion: Higher sea levels and reduced coastal sea ice protection will increase future extreme sea levels in the Arctic (high confidence for Arctic Northern Europe, the Russian Arctic, and Arctic North-Western North America (medium confidence) for Greenland and Iceland and Arctic North-Eastern North America given glacial isostatic adjustment). Vousdoukas et al. (2018) project that the current 1-in-100-year extreme total water level would have median return periods of 1-in-20-years to 1-in-50-years by 2050, increasing to 1-in-5-years to 1-in-20-years by 2100 under RCP4.5 along nearly the entire Arctic coastline by 2100 (excluding GIC for which projections are not available). Projections for RCP8.5 indicate that the present-day 1-in-100-year ETWL would have median return periods of 1-in-10-years to 1-in-50-years by 2050 and would occur once every five years (or more frequently) by 2100. Arctic coastal erosion is also expected to increase with climate change (medium confidence; high agreement but limited evidence of projections), accelerated in some regions by subsurface permafrost thaw and increased wave energy (Gibbs and Richmond, 2015; Fritz et al., 2017; Oppenheimer et al., 2019; Casas-Prat and Wang, 2020). A longer ice-free season for the RCP8.5 2080s is projected to help drive more than 100 m of shoreline retreat in North-Western North America Arctic coastal communities (Melvin et al., 2017; Greenan et al., 2018; Magnan et al., 2019). Assessment of coastal flooding and erosion changes in Antarctica are limited by a lack of studies.

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Climate change has caused and will continue to induce an enhanced warming trend, increasing heat-related extremes and decreasing cold spells and frosts in the Arctic (high confidence), with similar changes in Antarctica but medium confidence for extreme heat increases and West Antarctic frost change decreases and low confidence for cold spell changes and East Antarctica frost. The water cycle is projected to intensify in polar regions, leading to more rainfall, higher river flood potential and more intense precipitation (high confidence). Projections indicate reductions in glaciers at both poles, with sea ice loss, enhanced permafrost warming, decreasing permafrost extent, and decreasing seasonal duration and extent of snow cover in the Arctic (high confidence) even as some of the coldest regions will see higher total snowfall given increased precipitation (medium confidence). Projections indicate relative sea level rises in polar regions (high confidence) , with the exception of regions with substantial land uplift including North-Eastern North America (high confidence), western Greenland, the northern Baltic Sea, and portions of West Antarctica. Higher sea levels also contribute to high confidence for projected increases of Arctic coastal flooding and higher coastal erosion (aided by sea ice loss) (medium confidence) with lower confidence for those CIDs in regions with substantial land uplift.

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Mountains cover about 30% of the land areas on Earth (not counting Antarctica) and deliver a number of vital services to humanity (WGII Cross-Chapter Paper 5; IPCC, 2019b). Climate change in high mountains was addressed in SROCC, which emphasized changes in several climatic impact-drivers. These included an observed general decline in low-elevation snow cover, glaciers and permafrost (high confidence), which induced changes in natural hazards such as decrease in slope stability (high confidence), changes to the frequency of glacial lake outbursts (limited evidence), and climate effects on other climatic impact-drivers (avalanche, rain-on-snow floods) with various degrees of confidence (Hock et al., 2019).

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Declines in low-elevation snow depth and seasonal extent are projected for all SSP-RCPs (see Sections 12.4.1–12.4.6), along with reductions in mountain glacier surface area, increases in permafrost temperature, decreases in permafrost thickness, changes in lake and river ice, changes in the amount and seasonality of streamflows and hydrologic droughts in snow-dominated and glacier-fed river basins (e.g., in Central Asia; Sorg et al., 2014; Reyer et al., 2017b) (medium confidence), and decreases in the stability of mountain slopes and snowfields. Glacier recession could lead to the creation of new glacial lakes in places like the Himalaya-Karakoram region (Linsbauer et al., 2016) and in Alaska and Canada (Carrivick and Tweed, 2016; Harrison et al., 2018) (medium confidence). With increasing temperature and precipitation these can increase the occurrence of glacier lake outburst floods and landslides over moraine-dammed lakes (high confidence) (Carey et al., 2012; Rojas et al., 2014; Iribarren Anacona et al., 2015; Cook et al., 2016; Haeberli et al., 2017; Kapitsa et al., 2017; Narama et al., 2018; Wilson et al., 2018; Drenkhan et al., 2019; S. Wang et al., 2020).

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In conclusion, mountains face complex challenges from specific climatic impact-drivers drastically influenced by climate change: regional elevation-dependent warming (high confidence), low-to-mid-altitude snow cover and sno w-sea son decrease even as some high elevations see more snow (high confidence), glacier mass reduction and permafrost thawing (high confidence), and increases in extreme precipitation and floods in most parts of major mountain ranges (medium confidence).

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AMAP, 2017: Snow, Water, Ice and Permafrost in the Arctic (SWIPA) 2017. Arctic Monitoring and Assessment Programme (AMAP), Oslo, Norway, 269 pp., www.amap.no/documents/doc/snow-water-ice-and-permafrost-in-the-arctic-swipa-2017/1610.

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Biskaborn, B.K. et al., 2019: Permafrost is warming at a global scale. Nature Communications, 10(1), 264, doi: 10.1038/s41467-018-08240-4.

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Derksen, C. et al., 2018: Changes in Snow, Ice, and Permafrost Across Canada. In: Canada’s Changing Climate Report[Bush, E. and D.S. Lemmen (eds.)]. Government of Canada, Ottawa, ON, Canada, pp. 194–260, https://changingclimate.ca/site/assets/uploads/sites/2/2018/11/CCCR-Chapter5-ChangesInSnowIcePermafrostAcrossCanada.pdf .

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Drewes, J., S. Moreiras, and O. Korup, 2018: Permafrost activity and atmospheric warming in the Argentinian Andes. Geomorphology, 323, 13–24, doi: 10.1016/j.geomorph.2018.09.005.

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Duvillard, P.A., L. Ravanel, M. Marcer, and P. Schoeneich, 2019: Recent evolution of damage to infrastructure on permafrost in the French Alps. Regional Environmental Change, 19(5), 1281–1293, doi: 10.1007/s10113-019-01465-z.

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Farquharson, L.M. et al., 2019: Climate Change Drives Widespread and Rapid Thermokarst Development in Very Cold Permafrost in the Canadian High Arctic. Geophysical Research Letters, 46(12), 6681–6689, doi: 10.1029/2019gl082187.

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Forbes, D.L. (ed.), 2011: State of the Arctic Coast 2010 – Scientific Review and Outlook. International Arctic Science Committee, Land-Ocean Interactions in the Coastal Zone, Arctic Monitoring and Assessment Programme, International Permafrost Association. Helmholtz-Zentrum, Geesthacht, Germany, 178 pp., www.arcticcoasts.org/.

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Guo, D. and H. Wang, 2016: CMIP5 permafrost degradation projection: A comparison among different regions. Journal of Geophysical Research: Atmospheres, 121(9), 4499–4517, doi: 10.1002/2015jd024108.

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Haeberli, W., 2013: Mountain permafrost – research frontiers and a special long-term challenge. Cold Regions Science and Technology, 96, 71–76, doi: 10.1016/j.coldregions.2013.02.004.

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Haeberli, W., Y. Schaub, and C. Huggel, 2017: Increasing risks related to landslides from degrading permafrost into new lakes in de-glaciating mountain ranges. Geomorphology, 293, 405–417, doi: 10.1016/j.geomorph.2016.02.009.

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Hjort, J. et al., 2018: Degrading permafrost puts Arctic infrastructure at risk by mid-century. Nature Communications, 9(1), 5147, doi: 10.1038/s41467-018-07557-4.

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Iribarren Anacona, P., A. Mackintosh, and K.P. Norton, 2015: Hazardous processes and events from glacier and permafrost areas: lessons from the Chilean and Argentinean Andes. Earth Surface Processes and Landforms, 40(1), 2–21, doi: 10.1002/esp.3524.

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Kharuk, V.I., A.S. Shushpanov, S.T. Im, and K.J. Ranson, 2016: Climate-induced landsliding within the larch dominant permafrost zone of central Siberia. Environmental Research Letters, 11(4), 45004, doi: 10.1088/1748-9326/11/4/045004.

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Kokelj, S. et al., 2015: Increased precipitation drives mega slump development and destabilization of ice-rich permafrost terrain, northwestern Canada. Global and Planetary Change, 129, 56–68, doi: 10.1016/j.gloplacha.2015.02.008.

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Noetzli, J. et al., 2019: Permafrost thermal state [in “State of the Climate in 2018”]. Bulletin of the American Meteorological Society, 100(9), S21–22, doi: 10.1175/2019bamsstateoftheclimate.1.

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Patton, A.I., S.L. Rathburn, and D.M. Capps, 2019: Landslide response to climate change in permafrost regions. Geomorphology, 340, 116–128, doi: 10.1016/j.geomorph.2019.04.029.

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Rangecroft, S., A.J. Suggitt, K. Anderson, and S. Harrison, 2016: Future climate warming and changes to mountain permafrost in the Bolivian Andes. Climatic Change, 137(1–2), 231–243, doi: 10.1007/s10584-016-1655-8.

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Romanovsky, V. et al., 2018: Terrestrial Permafrost [in “State of the Climate in 2017”]. Bulletin of the American Meteorological Society, 99(8), S161–S165, doi: 10.1175/2018bamsstateoftheclimate.1.

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Romanovsky, V.E. et al., 2020: Terrestrial permafrost [in “State of the Climate in 2019”]. Bulletin of the American Meteorological Society, 101(8), S265–S271, doi: 10.1175/bams-d-20-0086.1.

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Schuster, P.F. et al., 2018: Permafrost Stores a Globally Significant Amount of Mercury. Geophysical Research Letters, 45(3), 1463–1471, doi: 10.1002/2017gl075571.

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Shiklomanov, N.I., D.A. Streletskiy, T.B. Swales, and V.A. Kokorev, 2017: Climate Change and Stability of Urban Infrastructure in Russian Permafrost Regions: Prognostic Assessment based on GCM Climate Projections. Geographical Review, 107(1), 125–142, doi: 10.1111/gere.12214.

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Slater, A.G. and D.M. Lawrence, 2013: Diagnosing Present and Future Permafrost from Climate Models. Journal of Climate, 26(15), 5608–5623, doi: 10.1175/jcli-d-12-00341.1.

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Streletskiy, D.A., L.J. Suter, N.I. Shiklomanov, B.N. Porfiriev, and D.O. Eliseev, 2019: Assessment of climate change impacts on buildings, structures and infrastructure in the Russian regions on permafrost. Environmental Research Letters, 14(2), 025003, doi: 10.1088/1748-9326/aaf5e6.

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Walvoord, M.A. and B.L. Kurylyk, 2016: Hydrologic Impacts of Thawing Permafrost – A Review. Vadose Zone Journal, 15(6), 1–20, doi: 10.2136/vzj2016.01.0010.

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Zhao, L. et al., 2020: Changing climate and the permafrost environment on the Qinghai–Tibet (Xizang) plateau. Permafrost and Periglacial Processes, 31(3), 396–405, doi: 10.1002/ppp.2056.

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Climate change has caused widespread adverse impacts and related losses and damages to nature and people (high confidence). Losses and damages are unequally distributed across systems, regions and sectors (high confidence). Cultural losses, related to tangible and intangible heritage, threaten adaptive capacity and may result in irrevocable losses of sense of belonging, valued cultural practices, identity and home, particularly for Indigenous Peoples and those more directly reliant on the environment for subsistence. (medium confidence). For example, changes in snow cover, lake and river ice, and permafrost in many Arctic regions, are harming the livelihoods and cultural identity of Arctic residents including Indigenous populations (high confidence). Infrastructure, including transportation, water, sanitation and energy systems have been compromised by extreme and slow-onset events, with resulting economic losses, disruptions of services and impacts to well-being (high confidence). {WGII SPM B.1, WGII SPM B.1.2, WGII SPM.B.1.5, WGII SPM C.3.5, WGII TS.B.1.6; SROCC SPM A.7.1}

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Continued GHG emissions will further affect all major climate system components, and many changes will be irreversible on centennial to millennial time scales. Many changes in the climate system become larger in direct relation to increasing global warming. With every additional increment of global warming, changes in extremes continue to become larger. Additional warming will lead to more frequent and intense marine heatwaves and is projected to further amplify permafrost thawing and loss of seasonal snow cover, glaciers, land ice and Arctic sea ice (high confidence). Continued global warming is projected to further intensify the global water cycle, including its variability, global monsoon precipitation 117 , and very wet and very dry weather and climate events and seasons (high confidence). The portion of global land experiencing detectable changes in seasonal mean precipitation is projected to increase (medium confidence) with more variable precipitation and surface water flows over most land regions within seasons (high confidence).and from year to year (medium confidence). Many changes due to past and future GHG emissions are irreversible 118 on centennial to millennial time scales, especially in the ocean, ice sheets and global sea level (see 3.1.3). Ocean acidification (virtually certain), ocean deoxygenation (high confidence).and global mean sea level (virtually certain).will continue to increase in the 21st century, at rates dependent on future emissions. {WGI SPM B.2, WGI SPM B.2.2, WGI SPM B.2.3, WGI SPM B.2.5, WGI SPM B.3, WGI SPM B.3.1, . WGI SPM B.3.2, WGI SPM B.4, WGI SPM B.5, WGI SPM B.5.1, WGI SPM B.5.3, WGI Figure SPM.8}. (Figure 3.1)

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Sea level rise is unavoidable for centuries to millennia due to continuing deep ocean warming and ice sheet melt, and sea levels will remain elevated for thousands of years (high confidence). Global mean sea level rise will continue in the 21st century (virtually certain), with projected regional relative sea level rise within 20% of the global mean along two-thirds of the global coastline (medium confidence). The magnitude, the rate, the timing of threshold exceedances, and the long-term commitment of sea level rise depend on emissions, with higher emissions leading to greater and faster rates of sea level rise. Due to relative sea level rise, extreme sea level events that occurred once per century in the recent past are projected to occur at least annually at more than half of all tide gauge locations by 2100 and risks for coastal ecosystems, people and infrastructure will continue to increase beyond 2100 (high confidence). At sustained warming levels between 2°C and 3°C, the Greenland and West Antarctic ice sheets will be lost almost completely and irreversibly over multiple millennia (limited evidence). The probability and rate of ice mass loss increase with higher global surface temperatures (high confidence). Over the next 2000 years, global mean sea level will rise by about 2 to 3 m if warming is limited to 1.5°C and 2 to 6 m if limited to 2°C (low confidence). Projections of multi-millennial global mean sea level rise are consistent with reconstructed levels during past warm climate periods: global mean sea level was very likely 5 to 25 m higher than today roughly 3 million years ago, when global temperatures were 2.5°C to 4°C higher than 1850–1900 (medium confidence). Further examples of unavoidable changes in the climate system due to multi-decadal or longer response timescales include continued glacier melt (very high confidence) and permafrost carbon loss (high confidence). {WGI SPM B.5.2, WGI SPM B.5.3, WGI SPM B.5.4, WGI SPM C.2.5, WGI Box TS.4, WGI Box TS.9, WGI 9.5.1; WGII TS C.5; SROCC SPM B.3, SROCC SPM B.6, SROCC SPM B.9}. (Figure 3.4)

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In scenarios with increasing CO2 emissions, the land and ocean carbon sinks are projected to be less effective at slowing the accumulation of CO2 in the atmosphere (high confidence). While natural land and ocean carbon sinks are projected to take up, in absolute terms, a progressively larger amount of CO2 under higher compared to lower CO2 emissions scenarios, they become less effective, that is, the proportion of emissions taken up by land and ocean decreases with increasing cumulative net CO2 emissions (high confidence). Additional ecosystem responses to warming not yet fully included in climate models, such as GHG fluxes from wetlands, permafrost thaw, and wildfires, would further increase concentrations of these gases in the atmosphere (high confidence). In scenarios where CO2 concentrations peak and decline during the 21st century, the land and ocean begin to take up less carbon in response to declining atmospheric CO2 concentrations (high confidence) and turn into a weak net source by 2100 in the very low GHG emissions scenario. (medium confidence)133 . {WGI SPM B.4, WGI SPM B.4.1, WGI SPM B.4.2, WGI SPM B.4.3}

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Overshoot of a warming level results in more adverse impacts, some irreversible, and additional risks for human and natural systems compared to staying below that warming level, with risks growing with the magnitude and duration of overshoot (high confidence). Compared to pathways without overshoot, societies and ecosystems would be exposed to greater and more widespread changes in climatic impact-drivers, such as extreme heat and extreme precipitation, with increasing risks to infrastructure, low-lying coastal settlements, and associated livelihoods (high confidence). Overshooting 1.5°C will result in irreversible adverse impacts on certain ecosystems with low resilience, such as polar, mountain, and coastal ecosystems, impacted by ice-sheet melt, glacier melt, or by accelerating and higher committed sea level rise (high confidence). Overshoot increases the risks of severe impacts, such as increased wildfires, mass mortality of trees, drying of peatlands, thawing of permafrost and weakening natural land carbon sinks; such impacts could increase releases of GHGs making temperature reversal more challenging (medium confidence). {WGI SPM C.2, WGI SPM C.2.1, WGI SPM C.2.3; WGII SPM B.6, WGII SPM B.6.1, WGII SPM B.6.2; SR1.5 3.6}

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Rising temperatures and humidity are also impacting wild food storage and increasing the risk of food-borne diseases (Cozzetto et al., 2013; Nuttall, 2017; Markon et al., 2018). Changes in AT and humidity can mean that whale and fish meat no longer dry properly, or meat may spoil before hunters can get it home (Downing and Cuerrier, 2011; Nuttall, 2017). Traditional permafrost ice cellars are no longer reliable (Downing and Cuerrier, 2011; Nyland et al., 2017; Herman-Mercer et al., 2019). Climate-related environmental change compounded with social, economic, cultural and political change have had complex but overall negative impacts on wild foods (Section CCP6.4, Lujan et al., 2018) .

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Istomin, K.V. and J.O. Habeck, 2016: Permafrost and indigenous land use in the northern Urals: Komi and Nenets reindeer husbandry. Polar Sci. , 10 (3), 278–287, doi:10.1016/j.polar.2016.07.002.

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Lakes fed by glacial melt water are growing in response to climate change and glacier retreat (robust evidence, high agreement ) (Shugar et al., 2020). Water storage increases on the Tibetan Plateau (Figure 2.3a) have been attributed to changes in glacier melt, permafrost thaw, precipitation and runoff, in part as a result of climate change (Huang et al., 2011; Meng et al., 2019; Wang et al., 2020a). High confidence in attribution of these trends to climate change is supported by long-term ground survey data and observations from the Gravity Recovery and Climate Experiment (GRACE) satellite mission (Ma et al., 2010; Rodell et al., 2018; Kraemer et al., 2020).

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In the Arctic, lake area has increased in regions with continuous permafrost, and decreased in regions where permafrost is thinner and discontinuous (robust evidence, high agreement ) (See Chapter 4) (Smith et al., 2005; Andresen and Lougheed, 2015; Nitze et al., 2018; Mekonnen et al., 2021).

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Studies since AR5 have confirmed ongoing and accelerating loss of lake and river ice in the Northern Hemisphere (robust evidence, high agreement ) (Figure 2.4). In recent decades, systems have been freezing later in winter and thawing earlier in spring, reducing ice duration by >2 weeks per year and leading to an increasing numbers of years with a loss of perennial ice cover, intermittent ice cover or even an absence of ice (Adrian et al., 2009; Kirillin et al., 2012; Paquette et al., 2015; Adrian et al., 2016; Park et al., 2016; Roberts et al., 2017; Sharma et al., 2019). The global extent of river ice declined by 25% between 1984 and 2018 (Yang et al., 2020). This trend has been more pronounced at higher latitudes, consistent with enhanced polar warming (large geographic coverage) (Du et al., 2017). Empirical long-term and remote-sensing data gathered in an increasingly large number of freshwater systems supports very high confidence in attributing these trends to climate change. For the decline of glaciers, snow and permafrost, see Chapter 4 (this report) and the Special Report on the Ocean and Cryosphere in a Changing Climate (IPCC, 2019b).

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The global extent of grasslands is declining significantly because of climate change (medium confidence). In temperate and boreal zones, where about half of tree lines are shifting, they are overwhelmingly expanding poleward and upward, with an accompanying loss of montane and boreal grassland (robust evidence, high agreement ) whereas tropical tree lines have been generally stable (medium evidence, medium agreement ) (Harsch et al., 2009; Rehm and Feeley, 2015; Silva et al., 2016; Andela et al., 2017; Song et al., 2018; Aide et al., 2019; Gibson and Newman, 2019). The Eurasian steppes experienced a 1% increase in woody cover per decade since 2000 (Liu et al., 2021) and inner Mongolian grasslands in China experienced broad encroachment as well (Chen et al., 2015). Climatic drivers of woody expansion in temperature-limited grasslands, particularly alpine grasslands, are most frequently attributed to warming (robust evidence, high agreement , high confidence) (D’Odorico et al., 2012; Hagedorn et al., 2014), an increase in water and nutrient availability from thawing permafrost (medium evidence, high agreement ) (Zhou et al., 2015b; Silva et al., 2016) and rising CO2 (medium evidence, medium agreement ) (Frank et al., 2015; Aide et al., 2019). Interactions of LULCCs such as land abandonment, grazing management shifts and fire suppression with climate change are contributing factors (Liu et al., 2021)

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In high-latitude peatlands, the net effect of climate change on the permafrost peatland carbon sink capacity remains uncertain (Abbott et al., 2016; McGuire et al., 2018b; Laamrani et al., 2020; Loisel et al., 2021; Sim et al., 2021; Väliranta et al., 2021). Increasing air temperatures have been linked to permafrost degradation and altered hydrological regimes (2.3.3.2; Figure 2.4a; 2.4.3.9; Box 5.1), which have led to rapid changes in plant communities and bio-geochemical cycling (robust evidence, high agreement ) (Liljedahl et al., 2016; Swindles et al., 2016; Voigt et al., 2017; Zhang et al., 2017b; Voigt et al., 2020; Sim et al., 2021). In many instances, permafrost degradation triggers thermokarst land subsidence associated with local wetting (robust evidence, high agreement ) (Jones et al., 2013; Borge et al., 2017; Olvmo et al., 2020; Olefeldt et al., 2021). Permafrost thaw in peatland-rich landscapes can also cause local drying through increased hydrological connectivity and runoff (Connon et al., 2014). In the first decades following thaw, increases in methane, CO2 and nitrous oxide emissions have been recorded from peatland sites, depending on surface moisture conditions (Schuur et al., 2009; O’Donnell et al., 2012; Elberling et al., 2013; Matveev et al., 2016; Euskirchen et al., 2020; Hugelius et al., 2020). Conversely, some evidence suggests increased peat accumulation after thaw (Jones et al., 2013; Estop-Aragonés et al., 2018; Väliranta et al., 2021). There is also a need to consider the impact of wildfire on permafrost thaw, due to its effect on soil temperature regime (Gibson et al., 2018), as fire intensity and frequency have increased across the boreal and Arctic biomes (limited evidence, high agreement ) (Kasischke et al., 2010; Scholten et al., 2021).

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In addition to direct warming, indirect effects of climate change, first found in AR4 and AR5, continue, such as thawed permafrost, altered hydrology and enhanced nutrient cycling, and these processes are causing pronounced vegetation changes (medium evidence, medium agreement ) (Schuur et al., 2009; Natali et al., 2012). Soil moisture status influences temperature sensitivity of plant growth and canopy heights (Myers-Smith et al., 2015 ; Ackerman et al., 2017; Bjorkman et al., 2018). In tundra ecosystems, permafrost thawing can decouple below-ground plant growth dynamics from above-ground dynamics, with below-ground root growth continuing until soils re-freeze in autumn (Cross-Chapter Paper 6) (Iversen et al., 2015; Blume-Werry et al., 2016; Radville et al., 2016).

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In the Arctic tundra and boreal forest, where wildfire has naturally been infrequent, burned area showed statistically significant increases of ~50% yr -1 across Siberia, Russia, from 1996 to 2015 (Ponomarev et al., 2016) and 2% yr -1 across Canada from 1959 to 2015 (Hanes et al., 2019). Wildfire burned ~6% of the area of four extensive Arctic permafrost regions in Alaska, USA, eastern Canada and Siberia from 1999 to 2014 (Nitze et al., 2018). In boreal forest in the Northwest Territories, Canada and Alaska, USA, the area burned by wildfire increased at a statistically significant rate of 6.8% yr -1 in the period 1975–2015, (Veraverbeke et al., 2017), with smouldering below-ground fires that lasted through the winter covering ~1% of burned area in the period 2002–2016 (Scholten et al., 2021). While burned area was correlated with temperature and reduced precipitation in Siberia (Ponomarev et al., 2016; Masrur et al., 2018) and correlated with lightning, temperature and precipitation in the Northwest Territories and Alaska (Veraverbeke et al., 2017), no attribution analyses have examined relative influences of climate and non-climate factors.

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Terrestrial ecosystems contain carbon stocks: 450 GtC (range 380–540 GtC) in vegetation, 1700 ± 250 GtC in soils that are not permanently frozen and 1400 ± 200 GtC in permafrost (Hugelius et al., 2014; Batjes, 2016; Jackson et al., 2017; Strauss et al., 2017; Erb et al., 2018a; Xu et al., 2021a). Ecosystem carbon stocks, totalling 3000–4000 GtC (from the lowest and highest estimates above), substantially exceed the ~900 GtC carbon in unextracted fossil fuels (see(Canadell et al., 2021)).

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In summary, terrestrial ecosystems contain 3000–4000 GtC in vegetation, permafrost and soils, three to five times the amount of carbon in unextracted fossil fuels and 4.4 times the carbon currently in the atmosphere (robust evidence, high agreement ). Tropical deforestation, the draining and burning of peatlands and other LULCCs emit 0.9–2.3 GtC yr -1, ~15% of the global emissions from fossil fuels and ecosystems (robust evidence, high agreement ). Terrestrial ecosystems currently remove more carbon from the atmosphere (-3.4±0.9 Gt yr -1) than they emit (+1.6±0.7 Gt yr -1), a net sink of -1.9±1.1 Gt yr -1 (Friedlingstein et al., 2020) . Thus, tropical rainforests, Arctic permafrost and other ecosystems provide the global ecosystem service of naturally preventing carbon from contributing to climate change (high confidence).

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Ecosystems with high soil carbon densities include the peat bogs in Ireland with up to 3000 tonnes ha -1 (Tomlinson, 2005), the Cuvette Centrale swamp forest peatlands in Congo with an average of ~2200 tonnes ha -1 (Dargie et al., 2017), the Arctic tundra with an average of ~900 tonnes ha -1 (Tarnocai et al., 2009) and the mangrove peatlands in Kalimantan, Indonesia, with an average of 850 ± 320 tonnes ha -1 (Murdiyarso et al., 2015). Arctic permafrost contains 1400 ± 200 GtC to a depth of 3 m, the largest soil carbon stock in the world (Hugelius et al., 2014). Globally, peatlands contain 470–620 GtC (Page et al., 2011; Hodgkins et al., 2018), of which boreal and temperate peatlands contain 415 ± 150 GtC (Hugelius et al., 2020) and tropical peatlands contain 80–350 GtC (Page et al., 2011; Dargie et al., 2017; Gumbricht et al., 2017; Ribeiro et al., 2021). Other analyses increase the upper estimates for boreal and temperate peatlands to 800–1200 GtC (Nichols and Peteet, 2019; Mishra et al., 2021b).

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Tropical forests and Arctic permafrost contain the highest ecosystem carbon stocks in above-ground vegetation and soil, respectively, in the world (robust evidence, high agreement ). These ecosystems form natural sinks that prevent the emission to the atmosphere of 1400–1800 GtC that would otherwise increase the magnitude of climate change (high confidence).

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Tropical deforestation, the draining and burning of peatlands and the thawing of Arctic permafrost due to climate change have caused these ecosystems to emit more carbon to the atmosphere than they naturally remove through vegetation growth (high confidence).

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In other regions, wildfires are also burning wider areas and occurring more often. This is consistent with climate change, but analyses have not yet shown if climate change is more important than other factors. In the Amazon, deforestation by companies, farmers and herders who cut down and intentionally burn rainforests to expand agricultural fields and pastures causes wildfires even in relatively moister years. Drought exacerbates these fires. In Australia, much of the southeastern part of the continent has experienced extreme wildfire years, but analyses suggest that El Niño, a heat phenomenon that cycles up and down periodically, is more important than long-term climate change. In Indonesia, intentional burning of rainforests for oil palm plantations and El Niño seem to be more important than long-term climate change. In Mediterranean Europe, fire suppression seems to have prevented any increasing trend in burned area but the suppression and abandonment of agricultural lands have allowed fuel to build up in some areas and contribute to major fires in years of extreme heat. In Canada and Siberia, wildfires are now burning more often in permafrost areas where fire was rare, but analyses are lacking regarding the relative influence of climate change. For the world as a whole, satellite data indicate that the vast amount of land converted from forest to farmland in the period 1998–2015 actually decreased the total burned area. Nevertheless, the evidence from the forests of western North America shows that human-caused climate change has, at least on one continent, clearly driven increases in wildfire.

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Terrestrial ecosystems sequester and store globally critical stocks of carbon, but these stocks are at risk from deforestation and climate change (high confidence). Tropical deforestation and the draining and burning of peatlands produce almost all of the carbon emissions from LULCC. In the Arctic, increased temperatures have thawed permafrost at numerous sites, dried some areas and increased fires, causing net emissions of carbon from soils (high confidence) (Sections 2.4.4.4, 2.5.3.4).

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The overall effect of climate change on the extent of northern peatlands is still debated (limited evidence, low agreement ). It is expected that climate change will drive the expansion of high-latitude peatlands poleward of their present distribution due to warming, permafrost degradation and glacier retreat, which could provide new land and conditions favourable for peat development (limited evidence, medium agreement ) (Zhang et al., 2017b), as seen during the last de-glacial warming (robust evidence, high agreement ) (MacDonald et al., 2006; Jones and Yu, 2010; Ratcliffe et al., 2018). Peatland area loss (shrinking) near the southern limit of their current distribution or in areas where the climate becomes unsuitable is also expected (medium evidence, medium agreement ) (Section 2.3.4.3.2) (Finkelstein and Cowling, 2011 Gallego-Sala and Prentice, 2013; Schneider et al., 2016; Müller and Joos, 2020) (Müller and Joos, 2021), but they could persist if moisture is maintained via their capacity to self-regulate. In western Canada, a study suggests that peatlands may persist until 2100, even though the climate will be less suitable (Schneider et al., 2016). Simulations suggest that climate change-driven increases in temperature and atmospheric CO2 could drive reductions in the northern peatland area up to 18% (SSP1–2.6), 41% (SSP2–4.5) and 61% (SSP5–8.5) by 2300 (Müller and Joos, 2020). This is in contrast with the findings of northern peatland persistence and expansion under RCP2.6 and RCP6.0 scenarios in 1861–2099 by another modelling study (Qiu et al., 2020). In the Tropics, the only available study suggests peatland area will increase until 2300, mainly due to increases in precipitation and the CO2 fertilisation effect (Müller and Joos, 2020; Müller and Joos, 2021).

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The combination of changes in climate and land use represents a substantial risk to peatland carbon stocks, but full assessment is impeded because peatlands are yet to be included in ESMs (limited evidence, high agreement ) (Loisel et al., 2021). It is expected that the carbon balance of peatlands globally will switch from sink to source in the near future (2020–2100), mainly because tropical peatland emissions, together with those from climate change-driven permafrost thaw, will likely surpass the carbon gain expected from climate change-driven enhanced plant productivity in northern high latitudes (Gallego-Sala et al., 2018; Chaudhary et al., 2020; Turetsky et al., 2020; Loisel et al., 2021) which are mainly caused by groundwater drawdown (robust evidence, medium agreement ) (Hirano et al., 2014; Brouns et al., 2015; Cobb et al., 2017; Itoh et al., 2017; Evans et al., 2021). The overall northern peatland carbon sink has been simulated to persist for at least 300 years under RCP2.6, but not under RCP8.5 (Qiu et al., 2020).

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Regarding permafrost peatlands, studies differ, with some projecting a net loss and others a net gain of carbon (medium evidence, low agreement ) (Estop-Aragonés et al., 2018; Hugelius et al., 2020; Loisel et al., 2021; Väliranta et al., 2021). In some permafrost peatlands, prolonged and warmer growing seasons due to climate change (Section 2.3.4.3.1), along with increases in nitrogen deposition since 1850 (Lamarque et al., 2013), are promoting plant primary productivity. Other studies indicate that increased nitrogen-mediated sequestration could be exceeded by increased decomposition due to climate change-driven warming and fire (medium evidence, low agreement ) (Natali et al., 2012; Vonk et al., 2015; Keuper et al., 2017; Burd et al., 2018; Estop-Aragonés et al., 2018; Gallego-Sala et al., 2018; Serikova et al., 2018; Wild et al., 2019; Chaudhary et al., 2020; Hugelius et al., 2020).

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For boreal–tundra systems, AR5 projected the transformation of species composition, land cover and permafrost extent, decreasing albedo and increasing GHG emissions (medium confidence). SR1.5 classified tundra and boreal forests as particularly vulnerable to degradation and encroachment by woody shrubs (high confidence). The SROCC projected climate-related changes to arctic hydrology, wildfires and abrupt thaw (high confidence) and the broad disappearance of arctic near-surface permafrost this century, with important consequences for global climate (very high confidence). Chapter 2 of AR6 has focused on new key findings about observed and projected changes in tundra vegetation and related hydrology, with implications for feedbacks to the climate system.

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Models of vegetation response to climate project acceleration in the coming decades of observed increases in shrub dominance and boreal forest encroachment that have been driven by recent warming (Settele et al., 2014), leading to a shrinking of the area of tundra globally (medium confidence) (Mod and Luoto, 2016; Gang et al., 2017). Simulating changes in tundra vegetation is complicated by permafrost dynamics (e.g., the formation of thaw ponds and draining of existing ponds), changes in precipitation and low nutrient availability (which may promote the abundance of graminoids) (van der Kolk et al., 2016). Changes in vegetation, when combined with warming and increased precipitation effects on soil thawing and carbon cycling, are projected to modify GHG emissions and have biophysical feedbacks to regional and global climate. High uncertainty in modelled carbon cycle changes arises from differences between the vegetation models (Nishina et al., 2015; Ito et al., 2016). In addition, climate change is expected to strongly interact with other factors, such as fire, to further increase uncertainty in projections of tundra ecosystem function (Jiang et al., 2017).

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In the Arctic tundra, boreal forests and northern peatlands, including permafrost areas, climate change under the scenario of a 4°C temperature increase could triple the burned area in Canada (Boulanger et al., 2014), double the number of fires in Finland (Lehtonen et al., 2016), increase the lightning-driven burned area by 30–250% (Veraverbeke et al., 2017; Chen et al., 2021a), push half of the area of tundra and boreal forest in Alaska above the burning threshold temperature and double the burned area in Alaska (Young et al., 2017a). Thawing of Arctic permafrost due to a projected temperature of 4°C and the resultant wildfires could release 11–200 GtC which could substantially exacerbate climate change (Section 2.5.2.9).

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In summary, under a high-emission scenario that increases global temperature 4°C by 2100, climate change could increase the global burned area by 50–70% and the global mean fire frequency by ~30%, with increases on one- to two-thirds and decreases on one-fifth of global land (medium confidence). Lower emissions that would limit the global temperature increase to <2°C would reduce projected increases of burned area to ~35% and projected increases of fire frequency to ~20% (medium confidence). Increased wildfire, combined with erosion due to deforestation, could degrade water supplies (high confidence). For ecosystems with an historically low fire frequency, a projected 4°C rise in global temperature increases risks of fire, contributing to potential tree mortality and conversion of over half the Amazon rainforest to grassland and thawing of the Arctic permafrost that could release 11–200 GtC that could substantially exacerbate climate change (medium confidence).

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Enhanced carbon losses from terrestrial systems further limit the available carbon budget for global warming staying below 1.5°C (Rogelj et al., 2018). Analyses of satellite remote sensing and ground-based observations have indicated that, between 1982 and 2015, the CO2 fertilisation effect has already declined, implying a negative climate system feedback (Wang et al., 2020c). Peatlands, permafrost regions and tropical ecosystems are particularly vulnerable due to their large carbon stocks, in combination with over-proportional warming, increases in heat waves and droughts and/or a complex interplay of climate change and increasing atmospheric CO2 (Sections 2.5.2.8, 2.5.2.9, 2.5.3.2).

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Model projections suggest a reduction of permafrost extent and potentially large carbon losses for all warming scenarios (Canadell et al., 2021). Already a mean temperature increase of 2°C could reduce the total permafrost area extent by about 5–20% by 2100 (Comyn-Platt et al., 2018; Yokohata et al., 2020). Associated CO2 losses in the order of 15 Gt up to nearly 70 Gt by 2100 have been projected across a number of modelling studies (Schneider von Deimling et al., 2015; Comyn-Platt et al., 2018; Yokohata et al., 2020). Limiting the global temperature increase to 1.5°C versus 2°C could reduce projected permafrost CO2 losses by 2100 by 24.2 Gt (median, calculated for a 3-m depth) (Comyn-Platt et al., 2018). Losses are possibly underestimated in the studies that consider only the upper permafrost layers. Likewise, the actual committed carbon loss may well be larger (e.g., eventually a loss of approx. 40% of today’s permafrost area extent if climate is stabilised at 2°C above pre-industrial levels) due to the long time scale of warming in deep permafrost layers (Chadburn et al., 2017). It is not known at which level of global warming an abrupt permafrost collapse (estimated to enhance CO2 emissions by 40% in 2300 in a high-emissions scenario) compared to gradual thaw (Turetsky et al., 2020) would have to be considered an important additional risk. Large uncertainties arise also from interactions with changes in surface hydrology and/or northward migrating woody vegetation as climate warms, which could dampen or even reverse projected net carbon losses in some regions (McGuire et al., 2018a; Mekonnen et al., 2018; Pugh et al., 2018). Overall, there is low confidence on how carbon–permafrost interactions will affect future carbon cycle and climate, although net carbon losses and thus positive (amplifying) feedbacks are likely (Sections 2.5.2.10, 2.5.3.5) (Shukla et al., 2019). See also WGI AR6 (Canadell et al., 2021) for a discussion on impacts of higher-emission and warming scenarios.

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Peatland carbon is estimated as about 550–1000 Gt in northern latitudes (many of these peatlands would be found in permafrost regions) (Turetsky et al., 2015; Nichols and Peteet, 2019) and >100 Gt in tropical regions (Turetsky et al., 2015; Dargie et al., 2017). For both northern mid- and high-latitude and tropical peatlands, a shift from contemporary CO2 sinks to sources were simulated in high-warming scenarios (Wang et al., 2018a; Qiu et al., 2020). Due to the lack of large-scale modelling studies, there is low confidence for climate change impacts on peat carbon uptake and emissions. The largest risk to tropical peatlands is expected to arise from drainage and conversion to forestry or agriculture, which would outpace the impacts of climate change (Page and Baird, 2016; Leifeld et al., 2019; Cooper et al., 2020). The magnitude of possible carbon losses is uncertain, however, and depends strongly on socioeconomic scenarios (Sections 2.4.3.8, 2.4.4.2; 2.4.4.4.2, 2.5.2.8).

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Climate-induced shifts towards forests in what is currently tundra would be expected to reduce regional albedo especially in spring, but also during parts of winter when trees are snow-free (whereas tundra vegetation would be covered in snow), which amplifies warming regionally (high confidence) (Perugini et al., 2017; Jia et al., 2019). Trees would also enhance momentum absorption compared to low tundra vegetation, thus impacting surface–atmosphere mixing of latent and sensible heat fluxes (Jia et al., 2019). Boreal forests insulate and stabilize permafrost and reduce fluctuations of ground temperature: the amplitude of variation of ground surface temperatures was 28°C at a forested site, compared to 60°C in nearby grassland (Section 2.5.2.7) (Bonan, 1989; Stuenzi et al., 2021a; Stuenzi et al., 2021b). Likewise, a shift in moist tropical forests towards vegetation with drought-tolerant traits could possibly reduce evapotranspiration, increase albedo, alter heat transfer at the surface and lead to a negative feedback to precipitation (Section 2.5.2.6) (Jia et al., 2019). In savannas, restoration of woody vegetation has been shown to enhance cloud formation and precipitation in response to enhanced transpiration and turbulent mixing, leading to a positive feedback on woody cover (Syktus and McAlpine, 2016). While this has not yet been systematically explored, similar feedbacks might also emerge from a CO2-induced woody cover increase in savannas (low confidence) (Section 2.5.2.5).

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AR5 named water supply and biodiversity as freshwater ecosystem services vulnerable to climate change. We discuss the risks to these and to additional services identified by model projections based both on climate-change scenarios (Schröter et al., 2005; Boithias et al., 2014; Huang et al., 2019; Jorda-Capdevila et al., 2019) and on the Common International Classification of Ecosystem Services (high confidence) (CICES, 2018). The effects of floods, droughts, permafrost and glacier-melting on global changes in water quality, particularly with respect to contamination with pollutants, are described in Section 4.2.6.

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Key risks assessed here are interconnected. Extinction of species is an irreversible impact of climate change and has negative consequences on ecosystem integrity and functioning, and the risks increase steeply with even small rises in global temperature (Section 2.5.1.3, Figure 2.6, Figure 2.7, Figure 2.8). Continued climate change substantially increases the risk of carbon losses due to wildfires, tree mortality from drought and insect pest outbreaks, peatland drying, permafrost thaw and changes in the structure of ecosystems; these could exacerbate self-reinforcing feedbacks between emissions from high-carbon ecosystems and increasing global temperatures (medium confidence). Thawing of Arctic permafrost alone could release 11–200 GtC (medium confidence). Complex interactions of climate changes, LULCC, carbon dioxide fluxes and vegetation changes will regulate the future carbon balance of the biosphere, processes incompletely represented in ESMs. The exact timing and magnitude of climate–biosphere feedbacks and the potential tipping points of carbon loss are characterised by broad ranges of the estimates, but studies indicate that increased ecosystem carbon losses could cause extreme future temperature increases (medium confidence). (Sections 2.5.2.7, 2.5.2.8, 2.5.2.9, 2.5.3.2, 2.5.3.3, 2.5.3.4, 2.5.3.5, Figure 2.10, Figure 2.11, Table 2.4, Table 2.5, Table SM2.2, Table SM2.5)

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Stuenzi, S. M. et al., 2021b: Sensitivity of ecosystem-protected permafrost under changing boreal forest structures. Environmental Research Letters, 16 (8), 084045, doi:10.1088/1748-9326/ac153d.

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Sugimoto, A. et al., 2002: Importance of permafrost as a source of water for plants in east Siberian taiga. Ecological Research, 17 (4), 493–503, doi:10.1046/j.1440-1703.2002.00506.x.

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Swindles, G. T. et al., 2016: The long-term fate of permafrost peatlands under rapid climate warming. Scientific Reports, 5 (1), 17951, doi:10.1038/srep17951.

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Tarnocai, C. et al., 2009: Soil organic carbon pools in the northern circumpolar permafrost region. Global Biogeochemical Cycles, 23, doi:10.1029/2008gb003327.

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Turetsky, M. R. et al., 2020: Carbon release through abrupt permafrost thaw. Nature Geoscience, 13 (2), 138–143, doi:10.1038/s41561-019-0526-0.

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Väliranta, M. et al., 2021: Warming climate forcing impact from a sub-arctic peatland as a result of late Holocene permafrost aggradation and initiation of bare peat surfaces. Quaternary Science Reviews, 264, 107022, doi:10.1016/j.quascirev.2021.107022.

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van der Kolk, H.-J. et al., 2016: Potential Arctic tundra vegetation shifts in response to changing temperature, precipitation and permafrost thaw. Biogeosciences, 13 (22), 6229–6245, doi:10.5194/bg-13-6229-2016.

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Voigt, C. et al., 2020: Nitrous oxide emissions from permafrost-affected soils. Nature Reviews Earth & Environment , 1 (8), 420–434, doi:10.1038/s43017-020-0063-9.

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Voigt, C. et al., 2017: Increased nitrous oxide emissions from Arctic peatlands after permafrost thaw. Proceedings of the National Academy of Sciences, 114 (24), 6238, doi:10.1073/pnas.1702902114.

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Vonk, J. E. et al., 2015: Reviews and syntheses: Effects of permafrost thaw on Arctic aquatic ecosystems. Biogeosciences, 12 (23), 7129–7167, doi:10.5194/bg-12-7129-2015.

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Wauthy, M. et al., 2018: Increasing dominance of terrigenous organic matter in circumpolar freshwaters due to permafrost thaw. Limnology and Oceanography Letters, 3 (3), 186–198.

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Wild, B. et al., 2019: Rivers across the Siberian Arctic unearth the patterns of carbon release from thawing permafrost. Proceedings of the National Academy of Sciences, 116 (21), 10280–10285, doi:10.1073/pnas.1811797116.

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Yang, Y. et al., 2018a: Permafrost and drought regulate vulnerability of Tibetan Plateau grasslands to warming. Ecosphere, 9 (5), e02233.

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Yokohata, T. et al., 2020: Model improvement and future projection of permafrost processes in a global land surface model. Progress in Earth and Planetary Science, 7 (1), 69, doi:10.1186/s40645-020-00380-w.

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Zhou, Z. et al., 2015b: Responses of alpine grassland to climate warming and permafrost thawing in two basins with different precipitation regimes on the Qinghai-Tibetan Plateaus. Arctic Antarctic and Alpine Research, 47 (1), 125–131.

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While levels of pollutants in biota (e.g., persistent organic pollutants, mercury) have generally declined over the past decades, recent increasing levels are associated with release from reservoirs in ice, snow and permafrost, and through changing food webs and pathways for trophic amplification (medium confidence) (see Box 3.2; Ma et al., 2016; Amélineau et al., 2019; Foster et al., 2019; Bourque et al., 2020; Kobusińska et al., 2020). Also, a warmer climate, altered ocean currents and increased human activities elevate the risk of invasive species in the Arctic (medium confidence), potentially changing ecosystems in this region (high confidence) (Chan et al., 2019; Goldsmit et al., 2020). In the remote Antarctic, there is a lower risk of invasive species (limited evidence) (McCarthy et al., 2019; Holland et al., 2021).

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The Special Report on Oceans and Cryosphere in a Changing Climate (SROCC) confirmed findings from AR5, with robust evidence of declines in snow cover and negative mass balance in most glaciers globally. Glacier melting seriously threatens water supply to mountain communities and millions living downstream through water shortages, jeopardising hydropower generation, irrigation and urban water uses (Hock et al., 2019b). Additionally, Arctic hydrology will be affected by permafrost changes, negatively impacting Arctic communities’ health and cultural identity (Meredith et al., 2019).

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AR5 reported a decrease in snow cover over most of the Northern Hemisphere, decreases in the extent of permafrost and increases in its average temperature, and glacier mass loss in most parts of the world (Jiménez Cisneros et al., 2014). SROCC (IPCC, 2019c) stated with very high or high confidence (a) reduction in seasonal snow cover (snow cover extent decreased by 13.4% per decade for 1967–2018); (b) glacier mass budget of all mountain regions (excluding the Canadian and Russian Arctic, Svalbard, Antarctica, Greenland) was 490 ± 100 kg m –2 yr –1 in 2006–2015; (c) warming of permafrost (e.g., permafrost temperatures increased by 0.39°C in the Arctic for 2007–2017). Tourism and recreation activities have been negatively impacted by declining snow cover, glaciers and permafrost in high mountains (medium confidence).

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Permafrost changes mainly refer to changes in temperature and active layer thickness (ALT) (Hock et al., 2019b; Fox-Kemper et al., 2021; Gulev et al., 2021). Permafrost temperature near the depth of zero annual temperature amplitude increased globally by 0.29 ± 0.12°C during 2007–2016, by 0.39 ± 0.15°C in the continuous permafrost and by 0.20 ± 0.10°C in the discontinuous permafrost (Biskaborn et al., 2019). Thus, permafrost has been warming during the last 3–4 decades (Romanovsky et al., 2017) with a rate of 0.4°C–1.4°C per decade throughout the Russian Arctic, 0.1°C–0.8°C per decade in Alaska and Arctic Canada during 2007–2016 (Biskaborn et al., 2019) and 0.1°C–0.24°C per decade in the Tibetan plateau (Wu et al., 2015). The ALT has also been increasing in the European and Russian Arctic and high-mountain areas of Eurasia since the mid-1990s (Hock et al., 2019b; Fox-Kemper et al., 2021; Gulev et al., 2021). Unfortunately, unlike glaciers and snow, the lack of in situ observations on permafrost still cannot be compensated for by remote sensing. Still, some methodological progress on this front has been happening recently (Nitze et al., 2018).

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In summary, the cryosphere is one of the most sensitive indicators of climate change. There is high confidence that cryospheric components (glaciers, snow, permafrost) are melting or thawing since the end of the 20th and beginning of the 21st century. Widespread cryospheric changes are affecting humans and ecosystems in mid-to-high latitudes and the high-mountain regions (high confidence). These changes are already impacting irrigation, hydropower, water supply, cultural and other services provided by the cryosphere, and populations depending on ice, snow and permafrost.

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AR5 (Jiménez Cisneros et al., 2014) concluded with medium evidence and high agreement that climate change affected water quality, posing additional risks to drinking water quality and human health (Field et al., 2014b), particularly due to increased eutrophication at higher temperatures or release of contaminants due to extreme floods (Jiménez Cisneros et al., 2014). In addition, SROCC (Hock et al., 2019b; Meredith et al., 2019) assessed that glacier decline and permafrost degradation impacts water quality through increases in legacy contaminants (medium evidence, high agreement ).

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Warming temperatures and extreme weather events can potentially impact water quality (Khan et al., 2015). Water quality can be compromised through algal blooms that affect the taste and odour of recreational and drinking water and can harbour toxins and pathogens (Khan et al., 2015). Warming directly affects thermal water regimes, promoting harmful algal blooms (Li et al., 2018; Noori et al., 2018) (Section 4.3.5). Additionally, permafrost degradation leads to an increased flux of contaminants (MacMillan et al., 2015; Roberts et al., 2017; Mu et al., 2019). The increased meltwater from glaciers (Zhang et al., 2019) releases deposited contaminants and reduces water quality downstream (Zhang et al., 2017; Hock et al., 2019b).

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Between 2000 and 2010, ~10% of the global population faced adverse water quality issues (van Vliet et al., 2021). Adverse drinking water quality has been associated with extreme weather events in countries located in Asia, Africa and South and North America (Jagai et al., 2015; Levy et al., 2016; Huynh and Stringer, 2018; Leal Filho et al., 2018; Abedin et al., 2019) (medium evidence, high agreement ). Dilution factors in 635 of 1049 US streams fell extremely low during drought conditions. Additionally, the safety threshold for endocrine-disrupting compound concentration exceeded in roughly a third of streams studied (Rice and Westerhoff, 2017). Natural acid rock drainage, which can potentially release toxic substances, has experienced intensification in an alpine catchment of the Central Pyrenees due to climate change and severe droughts in the last decade. River length affected by natural acid drainage increased from 5 km in 1945 to 35 km in 2018 (Zarroca et al., 2021). Threefold increases in contaminants and fivefold increases in nutrients have been observed in water sources after wildfires (Khan et al., 2015). Due to permafrost thawing, the concentration of major ions, especially SO42− in two high Arctic lakes, has rapidly increased up to 500% and 340% during 2006–2016 and 2008–2016, respectively (Roberts et al., 2017). The exports of dissolved organic carbon (DOC), particulate organic carbon and mercury in six Arctic rivers were reported to increase with significant deepening of active layers caused by climate warming during 1999–2015 (Mu et al., 2019). Sustained warming in Lake Tanganyika in Zambia during the last 150 years reduced lake mixing, which has depressed algal production, shrunk the oxygenated benthic habitat by 38% and further reduced fish and mollusc yield (Cohen et al., 2016). From 1994 to 2010, coastal benthos at King George Island in Antarctica have observed a remarkable shift primarily linked to ongoing climate warming and the increased sediment runoff triggered by glacier retreats (Sahade et al., 2015). The recovery time of macroinvertebrates from floods was found longer in cases of pre-existing pollution problems (Smith et al., 2019a).

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Climate change impacts soil erosion and sedimentation rates both directly from increasing rainfall or snowmelt intensity (Vanmaercke et al., 2014; Polyakov et al., 2017; Diodato et al., 2018; Golosov et al., 2018; Li et al., 2020a; Li et al., 2020b) and indirectly from increasing wildfires (Gould et al., 2016; Langhans et al., 2016; DeLong et al., 2018), permafrost thawing (Schiefer et al., 2018; Lafrenière and Lamoureux, 2019; Ward Jones et al., 2019) and vegetation cover changes (Micheletti et al., 2015; Potemkina and Potemkin, 2015; Carrivick and Heckmann, 2017; Beel et al., 2018). In addition, accelerated soil erosion and sedimentation have severe societal impacts through land degradation, reduced soil productivity and water quality (Section 4.2.7), increased eutrophication and disturbance to aquatic ecosystems (Section 4.3.5), sedimentation of waterways and damage to infrastructure (Graves et al., 2015; Issaka and Ashraf, 2017; Schellenberg et al., 2017; Hewett et al., 2018; Panagos et al., 2018; Sartori et al., 2019) (medium confidence).

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The climate change impact on erosion and sediment load varies significantly over the world (Li et al., 2020b) (high confidence). There was a statistically significant correlation between sediment yield and air temperature for the non-Mediterranean region of western and central Europe (Vanmaercke et al., 2014) and northern Africa (Achite and Ouillon, 2016). Still, such correlation is yet to be found for the other European rivers (Vanmaercke et al., 2015). Increased sediment and particulate organic carbon fluxes in the Arctic regions are caused by permafrost warming (Schiefer et al., 2018; Lafrenière and Lamoureux, 2019; Ward Jones et al., 2019). Potemkina and Potemkin (2015) demonstrate that regional warming and permafrost degradation have contributed to an increased forested area over the last 40–70 years, reducing soil erosion in eastern Siberia. The sediment dynamics of small rivers in the eastern Italian Alps, depending on extreme floods, is sensitive to climate change (Rainato et al., 2017). In the northeastern Italian Alps, precipitation change during 1986–2010 affected soil wetness conditions, influencing sediment load (Diodato et al., 2018). Regional warming in northern Africa (Algeria) dramatically changed river streamflow and increased sediment load over four decades (84% more every decade compared to the previous) (Achite and Ouillon, 2016).

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AR5 showed that local changes in temperature and rainfall had altered the distribution of some water-related diseases (medium confidence), and extreme weather events disrupt water supplies, impacting morbidity, mortality and mental health (very high confidence) (Field et al., 2014b). In addition, melting and thawing of snow, ice and permafrost (Section 4.2.2) have also adversely impacted water quality, security and health (high confidence) (IPCC, 2019a) (Section 4.2.7).

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Rising temperatures have a strong impact in the arctic zone, where the southern limit of permafrost is moving north and leading to changes in the landscape (Arp et al., 2016; Minayeva et al., 2018). Thawing of the permafrost leads to increased erosion and runoff and changes in the geomorphology and vegetation of arctic peatlands (Nilsson et al., 2015; Sun et al., 2018b). Permafrost thawing has led to the expansion of lakes in the Tibetan Plateau (Li et al., 2014). As northern high-latitude peatlands store a large amount of carbon, permafrost thawing can increase methane and carbon dioxide emissions (Schuur et al., 2015; Moomaw et al., 2018). This represents a major gap in our understanding of the rates of change and their consequences for freshwater ecosystems.

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In polar areas, there is high confidence that the appearance of land previously covered by ice, changes in snow cover, and thawing permafrost are contributing to changing seasonal activities. These include changes in accessibility, abundance and distribution of culturally important plant and animal species. These changes are harming the livelihoods and cultural identity of Indigenous Peoples, local communities and traditional peoples. In northern Fennoscandia, for example, reindeer herders reported experiences of deteriorated foraging conditions due to changes in the winter climate (Forbes et al., 2019; Rasmus et al., 2020). In addition, Inuit and First Nations communities in Canada (Ford et al., 2019; Khalafzai et al., 2019) and Alaskan Natives and Native American communities in the USA (Norton-Smith et al., 2016) identified disruption to access routes to traditional hunting grounds and climate-related stresses to culturally important species.

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The terrestrial hydrological cycle is projected to intensify through a higher exchange of water between the land surface and the atmosphere. A rise of near-surface atmospheric water capacity is projected because of greater warming leading to changes in the atmospheric circulation patterns, the intensification of the convection processes, and the increased temperature of the underlying surface. Continuation of projected warming and other physical mechanisms will further accelerate the melting of snow cover and glaciers and thawing of permafrost (high confidence).

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AR5 noted that global glacier mass loss is very likely to increase further during the 21st century (Jiménez Cisneros et al., 2014). According to the SROCC (Hock et al., 2019b), it is very likely that glaciers will continue to lose mass throughout the 21st century: from 18% (by 2100, relative to 2015) for RCP2.6 to 36% for RCP8.5. AR5 (Collins et al., 2013) and SROCC (Meredith et al., 2019) reported with high confidence that permafrost would continue to thaw in the 21st century, but the projections are uncertain. Constraining warming to 1.5°C would prevent the thawing of a permafrost area of 1.5 to 2.5 million km 2 compared to thawing under 2°C (medium confidence) (IPCC, 2018b). AR5 (Collins et al., 2013) and SROCC (Meredith et al., 2019) concluded that Northern Hemisphere snow extent and mass would likely reduce by the end of the 21st century, both in plain and mountain regions. AR6 assessed with medium confidence that under RCP2.6 and RCP8.5 from 2015 to 2100, glaciers are expected to lose 18% and 36% of their early 21st-century mass, respectively (AR6 WGI, (Fox-Kemper et al., 2021)).

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There is a high agreement between the CMIP6 projections and the previous findings that permafrost will undergo increasing thaw and degradation during the 21st century worldwide (Fox-Kemper et al., 2021) . The CMIP6 models project that the annual mean frozen volume in the top 2 m of the soil could decrease by 10–40% for every degree increase of global temperature (Burke et al., 2020; Yokohata et al., 2020b). The CMIP5-based equilibrium sensitivity of permafrost extent to stabilised global mean warming is established to be about 4.0 × 106 km 2°C–1 (Chadburn et al., 2017). The southern boundary of the permafrost is projected the move to the north: 1°–3.5° northward (relative to 1986–2005) at the level of 1.5°C temperature rise (Kong and Wang, 2017).

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In summary, in most basins fed by glaciers, runoff is projected to increase initially in the 21st century and then decline (medium confidence). Projections suggest a further decrease in seasonal snow cover extent and mass in mid to high latitudes and high mountains (high confidence), though the projection spread is considerable. Permafrost will continue to thaw throughout the 21st century (high confidence). There is medium confidence that future changes in cryospheric components will negatively affect irrigated agriculture and hydropower production in regions dependent on snowmelt runoff.

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AR5 concluded that climate change was projected to reduce water quality (Jiménez Cisneros et al., 2014). SR1.5 assessed with low confidence differences in projected impacts under 1.5°C compared with 2°C of warming (Hoegh-Guldberg et al., 2018). In addition, SROCC reported water quality degradation due to the release of legacy contaminants in glaciers and permafrost (medium confidence) (Hock et al., 2019b). The AR6 WGI Report does not explicitly mention water quality issues.

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Water insecurity due to water quality degradation is projected to increase under climate change due to warming, enhanced floods and sea level rise (Arnell and Lloyd-Hughes, 2014; Dyer et al., 2014; Whitehead et al., 2015) (medium confidence). Drought-driven diminishing river and lake levels (Jeppesen et al., 2015) and continued water abstraction for irrigation (Aragüés et al., 2015) may contribute to the salinisation of soil and water. In addition, warming is projected to disrupt the historical sequestration of contaminants in permafrost in the Arctic and mountain regions (Bond and Carr, 2018).

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While there is some understanding of the potential effect of glacier and permafrost degradation on water quality, projections are lacking. Research is limited mainly in Europe and North America, and quantifying the future water quality changes is still incipient.

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In summary, soil losses mainly depend on the combined effects of climate and land use changes. Herewith, recent studies demonstrate increasing impact of the projected climate change (increase of precipitation, thawing permafrost) on soil erosion (medium confidence).

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Climate-related extreme events impact WaSH services and local water security. While not WaSH-specific, AR5 showed that more people would experience water scarcity and floods (high confidence) and identified WaSH failure due to climate change as an emergent risk (medium confidence) leading to higher diarrhoea risk (Field et al., 2014b). In addition, both SR1.5 (IPCC, 2018a) and SRCCL (IPCC, 2019b) projected the risk from droughts, heavy precipitation, water scarcity, wildfire damage and permafrost degradation to be higher at 2°C warming than 1.5°C (medium confidence), and all these could potentially impact water quality and WaSH services.

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These impacts are expected to be most noticeable where significant air temperature increases are projected, leading to local or regional population extinctions for cold-water species because of range shrinking, especially under the RCP4.5, 6.0 and 8.5 scenarios (Comte and Olden, 2017). The consequences for freshwater species are projected to be severe with local extinctions as the freshwater ecosystems dry. In the Americas, under all scenarios that have been examined, the risk of extinction of freshwater species is projected to increase above that already occurring levels due to biodiversity loss caused by pollution, habitat modification, over-exploitation and invasive species (IPBES, 2019). Freshwater ecosystems are also at risk of abrupt and irreversible change, especially those in the higher latitudes and altitudes with significant changes in species distributions, including those induced by melting permafrost systems (Moomaw et al., 2018; IPBES, 2019).

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There is high confidence that local people are adapting to the cultural impacts of climate-driven glacier retreat and decline in snow cover and ice in polar and high-mountain areas. However, there is also high confidence that such adaptation can be detrimental and disrupt local cultures. For example, in the Peruvian Andes, concerns about water availability for ritual purposes has led to restrictions on pilgrims’ removal of ice and limiting the size of ritual candles to preserve the glacier (Paerregaard, 2013; Allison, 2015). Relatedly, some local people have questioned the cosmological order and have reoriented their spiritual relationships accordingly (Paerregaard, 2013; Carey et al., 2017). In Siberia (Mustonen, 2015) and northern Finland (Turunen et al., 2016), community-led decisions among herders favour alternative routing, pasture areas and shifts in nomadic cycles in response to changing flood events and permafrost conditions (Box 13.2). However, loss of grazing land and pasture fragmentation pose adaptation limits, and some strategies such as supplementary feeding and new technologies may further affect cultural traditions of herding communities (Risvoll and Hovelsrud, 2016; Jaakkola et al., 2018).

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Roberts, K.E., et al., 2017: Climate and permafrost effects on the chemistry and ecosystems of High Arctic Lakes. Sci. Rep. , 7 (1), 13292, doi:10.1038/s41598-017-13658-9.

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Romanovsky, V., et al., 2017: Terrestrial permafrost. Bull. Am. Meteorol. Soc. , 98, S265–S269, doi:10.1175/2017BAMSStateoftheClimate.1.

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Schuur, E.A.G., et al., 2015: Climate change and the permafrost carbon feedback. Nature, 520, 171, doi:10.1038/nature14338.

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Streletskiy, D.A., et al., 2019: Assessment of climate change impacts on buildings, structures and infrastructure in the Russian regions on permafrost. Environ. Res. Lett. , 14 (2), 25003.

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Sun, L., et al., 2018b: Wetland-atmosphere methane exchange in Northeast China:a comparison of permafrost peatland and freshwater wetlands. Agric. For. Meteorol. , 249, 239–249, doi:10.1016/j.agrformet.2017.11.009.

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Wu, Q., Y. Hou, H. Yun and Y. Liu, 2015: Changes in active-layer thickness and near-surface permafrost between 2002 and 2012 in alpine ecosystems, Qinghai–Xizang (Tibet) Plateau, China. Glob. Planet. Chang. , 124, 149–155, doi:10.1016/j.gloplacha.2014.09.002.

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Yokohata, T., et al., 2020b: Model improvement and future projection of permafrost processes in a global land surface model. Prog. Earth Planet. Sci. , 7 (1), 69, doi:10.1186/s40645-020-00380-w.

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4.2.2 Observed Changes in the Cryosphere (Snow, Glaciers and Permafrost)570

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4.4.2 Projected Changes in the Cryosphere (Snow, Glaciers and Permafrost)602

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Climate characteristics in Asia are diverse covering all climate zones from tropical to polar, including mountain climate. Monsoonal winds and associated precipitation are dominant in South, Southeast and East Asia. Annual mean surface air temperature averaged over the sub-region ranges from coldest in North Asia (–3°C) to warmest in Southeast Asia (25°C) based on JRA-55 (Kobayashi et al., 2015) climatology for 1981–2010. Most of North Asia and higher altitude is underlain by permafrost. West Asia is the driest and Southeast Asia is the wettest, with the annual precipitation averaged over the sub-region ranging about ten times from 220 mm in West Asia to 2570 mm in Southeast Asia based on GPCC (Schamm et al., 2014) climatology for 1981–2010. Indonesia in Southeast Asia has the longest coastline in the world, causing this area (maritime continent) to be the wettest region (Yamanaka et al., 2018). The Hindu Kush Himalaya (HKH) region is a biodiversity hotspot (Wester et al., 2019) and also has significant impacts on the Asian climate because of its orographic and thermodynamic effects (Wu et al., 2012).

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Projections of future changes in annual mean surface air temperature in Asia are qualitatively similar to those in the previous assessments with greater warming at higher latitudes (i.e., North Asia) (high confidence) (Gutiérrez et al., 2021). Projected surface air temperature changes in the Tibetan Plateau, Central Asia and West Asia are also significant (high confidence) (Gutiérrez et al., 2021). The highest levels of warming for extremely hot days are expected to occur in West and Central Asia with increased dryness of land (high confidence) (SR1.5). Over mountainous regions, elevation-dependent warming will continue (medium confidence) (Hock et al., 2019). Glaciers will generally shrink, but rates will vary among regions (high confidence) (Wester et al., 2019). Thawing permafrost presents a problem in northern areas of Asia, particularly Siberia (Parazoo et al., 2018). Temperature rise will be strongest in winter in most regions, while it will be the strongest on summer in the northern part of West Asia and some parts of South Asia where a desert climate prevails (high confidence) (Gutiérrez et al., 2021). The wet-bulb globe temperature, which is a measure of heat stress, is likely 2 to approach critical health thresholds in West and South Asia under the RCP4.5 scenario, and in some other regions, such as East Asia, under the RCP8.5 scenario (high confidence) (Lee et al., 2021a; Seneviratne et al., 2021). The occurrence of extreme heatwaves will very likely increase in Asia. Projections show that a sizeable part of South Asia will experience heat stress conditions in the future (high confidence). It is virtually certain that cold days and nights will become fewer (Ranasinghe et al., 2021).

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Sub-regional diversity of ecosystems is high in Asia (Section 10.2.2). Climate-impact drivers of Asian terrestrial ecosystems (ATS) change are global warming, precipitation and Asian monsoon alteration, permafrost thawing and extreme events like dust storms. Observed and projected changes in ATS are affected by several interacting factors. Non-climatic human-related drivers are change of land use, change of human use of natural resources, including species and ecosystems overexploitation as well as other non-sustainable use, socioeconomic changes and direct impacts of rising greenhouse gases (GHGs). Ecosystem vulnerability has resulted from complex interactions of CIDs and non-climate drivers. Species interaction and natural variability of organisms, species and ecosystems is currently poorly understood, and much more work still needs to be done to unravel these multiple stressors (i.e., Berner et al., 2013; Brazhnik and Shugart, 2015).

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In North Asia, a shift is projected in the dominant biomes from conifers to deciduous species across Russia after 20 years of altered climate conditions (Shuman et al., 2015). In South Siberia, Brazhnik and Shugart (2015) projected a shift from the boreal forest to the steppe biome. Rumiantsev et al. (2013) also project a positive northward shift of vegetation boundaries for the greater part of West Siberia in line with warming; however, no shift for the north of West Siberia and negative shift for the southern Urals and northwest Kazakhstan are projected for 2046–2065. The replacement of forest–steppe with steppe at the lower treeline in South Siberia is projected (Brazhnik and Shugart, 2015), and retreat of larch forests from the southernmost strongholds of boreal forest in eastern Kazakhstan is expected as part of a global process of forest dieback in semiarid regions (Dulamsuren et al., 2013). In North Asia, tree growth is intertwined with permafrost, snowpack, insect outbreaks, wildfires, seed dispersal and climate (e.g., Klinge et al., 2018). It is challenging to isolate the affects of individual factors, particularly since they can interact on one another in unanticipated ways because the underlying mechanisms are not well understood (Berner et al., 2013; Brazhnik and Shugart, 2015). The accuracy of treeline-shift projections is limited because projections are based on vegetation models which do not consider all the factors (Tishkov et al., 2020). The regional vegetation model structure and parameterisation can affect model performance, and the corresponding projections can differ significantly (Shuman et al., 2015).

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The climate-change impact on different parts of freshwater ecosystems (Section 10.4.2) has affected water supply in various sub-regions of Asia. While headwater zones are susceptible to change in snow cover, permafrost and glaciers, the downstream plain areas of these river systems are vulnerable to the increasing high demand of freshwater which will affect water availability in space and time. The observed impact of climate change has also been seen in direct physical losses such as precipitation (Mekong Delta), floods (Vietnam) and saltwater intrusion leading to low agricultural productivity (Mora et al., 2018; Almaden et al., 2019a; Pervin et al., 2020).

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With climate-change impacts resulting in the shrinking and melting of snow, ice, glacier and permafrost, and correspondingly causing an increase in melt water, the incidences of flash floods, debris flow, landslides, snow avalanches, livestock diseases and other disasters in the HKH region have become more frequent and intense. Some of the key factors that get in the way of assigning confidence levels to climate-change impacts include lack of sufficient observed data on factors such as river discharges, precipitation and glacier melt (You et al., 2017). Climate-change impacts cryospheric water sources in the Hindu Kush, Karakoram and Himalayan ranges which, in turn, carry consequences for the Indus, Ganges and Brahmaputra basins.

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A recent study (Wang et al., 2021b) has shown that during 1936–2019, due largely to intensified precipitation induced by a warming climate, the streamflow of the Ob, Yenisei and Lena rivers has increased by 7.7, 7.4 and 22.0%, respectively. While rising temperatures can reduce streamflow via evapotranspiration, it can enhance groundwater discharge to rivers due to permafrost thawing. In permafrost-developed basins, the thawing permafrost will continue to result in increased streamflow. However, with further permafrost degradation in the future, the positive effect of permafrost thaw on streamflow would probably be offset by the negative effect of the increase in basin evapotranspiration. This could result in a situation where runoff reaches threshold level and then declines. This is clearly marked in the Ob River basin, which is characterised by the highest precipitation, whereas in the case of the Yenisei and Lena rivers, further research is needed.

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In Asian cities, climatic hazards such as changes in precipitation and, during the Asian monsoon, SLR, cyclones, flooding, dust storms, heatwaves and permafrost thawing (Byers et al., 2018; Hoegh-Guldberg et al., 2018; Rogelj et al., 2018; Shiklomanov, 2019), as well as non-climatic vulnerabilities such as non-climatic hazards (e.g., seismic hazards), inadequate infrastructure and services, unplanned urbanisation, socioeconomic inequalities and existing adaptation deficits (Johnson et al., 2013; Araos et al., 2016; de Leon and Pittock, 2017; Meerow, 2017; Dulal, 2019) interact to shape overall urban risk (Shaw et al., 2016a; Rumbach and Shirgaokar, 2017; Dodman et al., 2019). Caught at the intersection of high exposure, socioeconomic vulnerability and low adaptive capacities, informal settlements in urban and peri-urban areas are particularly at risk (robust evidence, high agreement ) (Meerow, 2017; Rumbach and Shirgaokar, 2017; Byers et al., 2018).

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In Northern Eurasia, observed and projected climate-change impacts are especially pronounced. On land, the presence of permafrost, which occupies substantial areas of eastern Russia, Mongolia and mountain regions of China, creates specific challenges for economic development and human activities. By 2050, it is likely that 69% of fundamental human infrastructure in the Pan Arctic will be at risk (RCP 4.5 scenario)(medium confidence), including more than 1200 settlements (Hjort et al., 2018). The majority of the population and the absolute majority (85%) of large settlements on permafrost are located in Russia, and 44% of those are expected to be profoundly affected by permafrost thaw by 2050 (Streletskiy et al., 2019; Ramage et al., 2021). Under RCP8.5, the climate-induced decrease of bearing capacity and, in regions with ice-rich permafrost, thaw subsidence, is projected to affect 54% of all residential buildings on permafrost with a combined worth of 20.7 billion USD; 20% of commercial and industrial structures and 19% in critical infrastructure with a total worth of 84.4 billion USD (Streletskiy, 2019). Transport infrastructure in Russia and China are impacted by thaw subsidence and, to a lesser degree, from frost heave, which add significant operational costs and limit accessibility to remote settlements (Porfiriev et al., 2019; Ni et al., 2021).

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Especially in Russia, significant populations and fixed infrastructure assets are located in urban centres on permafrost that is degrading significantly. Two major risks associated with permafrost degradation are loss of permafrost bearing capacity and ground subsidence (Streletskiy et al., 2015). The former determines the ability to support foundations of buildings and structures and is a vital characteristic of sustainability of the economic centres, while the latter impacts the ability of critical infrastructure (roads, railroads) to provide transportation and support accessibility of remote populations and economic centres on permafrost. The proximity of some settlements to the coasts or areas with uneven topography may further increase risks associated with permafrost degradation as ice-rich coasts characterised by high rates of coastal erosion, while settlements located on slopes may experience higher rates of mass wasting processes.

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Changes in climate have resulted in permafrost warming and increased thaw depth in undisturbed locations (Biskaborn et al., 2019), but in built up areas these transformations have been exacerbated by human activities (Grebenets et al., 2012). Norilsk, the largest city built on permafrost above the Arctic Circle (Shiklomanov et al., 2017b), was found to have one of the highest trends of near-surface permafrost warming (Streletskiy et al., 2012). Anomalous high temperatures and earlier snowmelt in 2020 may have contributed to oil storage collapse and the resulting spill of 20,000 tons of diesel fuel in Norilsk area (Rajendan et al., 2021). The ability of foundations to support structures has decreased by 10–40% relative to the 1960s in the majority of settlements on permafrost in Russia (Streletskiy et al., 2012) and is expected to further decrease by 20–33% by 2050–2059 relative to 2006–2015 (Streletskiy et al., 2019).

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Biskaborn, B.K., et al., 2019: Permafrost is warming at a global scale. Nat. Commun, 10 (1), 264, doi:10.1038/s41467-018-08240-4.

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Ding, Y., et al., 2019: Global warming weakening the inherent stability of glaciers and permafrost. Sci. Bull. , 64 (4), 245–253, doi:10.1016/j.scib.2018.12.028.

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Hjort, J., et al., 2018: Degrading permafrost puts Arctic infrastructure at risk by mid-century. Nat. Commun. , 91 (9), 1–9, doi:10.1038/s41467-018-07557-4.

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Parazoo, N.C., et al., 2018: Detecting the permafrost carbon feedback: talik formation and increased cold-season respiration as precursors to sink-to-source transitions. Cryosphere, 12 (1), 123–144.

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Schaefer, K., et al., 2014: The impact of the permafrost carbon feedback on global climate. Environ. Res. Lett. , 9 (8), 85003.

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Shiklomanov, N. I., D. A. Streletskiy, T. B. Swales, V. A. Kokorev, 2016: Climate Change and Stability of Urban Infrastructure in Russian Permafrost Regions: Prognostic Assessment based on GCM Climate Projections. Geogr. Rev. , doi:10.1111/gere.12214.

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Shiklomanov, N.I., D.A. Streletskiy, V.I. Grebenets and L. Suter, 2017a: Conquering the permafrost: urban infrastructure development in Norilsk, Russia. Polar Geogr. , 40 (4), 273–290, doi:10.1080/1088937X.2017.1329237.

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Shiklomanov, N.I., D.A. Streletskiy, T.B. Swales and V.A. Kokorev, 2017b: Climate Change and Stability of Urban Infrastructure in Russian Permafrost Regions: Prognostic Assessment based on GCM Climate Projections. Geogr. Rev. , 107 (1), 125–142, doi:10.1111/gere.12214.

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Song, C.L., et al., 2019: Importance of active layer freeze-thaw cycles on the riverine dissolved carbon export on the Qinghai-Tibet Plateau permafrost region. Peer J. , 7, doi:10.7717/peerj.7146.

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Streletskiy, D.A., A.B. Sherstiukov, O.W. Frauenfeld and F.E. Nelson, 2015: Changes in the 1963–2013 shallow ground thermal regime in Russian permafrost regions. Environ. Res. Lett. , 10 (12), 125005.

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Streletskiy, D.A., N.I. Shiklomanov and F.E. Nelson, 2012: Permafrost, infrastructure, and climate change: a GIS-based landscape approach to geotechnical modeling. Arct. Antarct. Alp. Res. , 44 (3), 368–380.

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Streletskiy, D.A., L.J. Suter, N.I. Shiklomanov, B.N. Porfiriev and D.O. Eliseev, 2019: Assessment of climate change impacts on buildings, structures and infrastructure in the Russian regions on permafrost. Environ. Res. Lett. , doi:10.1088/1748-9326/aaf5e6.

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Wang, P., et al., 2021b: Potential role of permafrost thaw on increasing Siberian river discharge. Environ. Res. Lett. , 16 (3), 34046.

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Polar regions will be profoundly different in the future. The degree and nature of that difference will depend strongly on the rate and magnitude of global climate change, which will influence adaptation responses regionally and worldwide. Future climate-induced changes in the polar oceans, sea ice, snow and permafrost will drive habitat and biome shifts, with associated changes in the ranges and abundance of ecologically important species (IPCC, 2019g). Innovative tools and practices in polar resource management and planning show strong potential in improving society’s capacity to respond to climate change. Networks of protected areas, participatory scenario analysis, decision support systems and community-based ecological monitoring that draws on local and Indigenous knowledge and self-assessments of community resilience contribute to strategic plans for sustaining biodiversity and limit risk to human livelihoods and well-being. Experimenting, assessing and continually refining practices while strengthening links with decision making has the potential to ready society for the expected and unexpected impacts of climate change (IPCC, 2019g).

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Using this understanding of limits, subsequent Special Reports have assessed relevant literature (Mechler et al., 2020). SR15 identifies several regions, sectors and ecosystems—including coral reefs, biodiversity, human health, coastal livelihoods, Small Island Developing States, and the Arctic—that are projected to experience limits at either 1.5°C or 2°C. SRCCL states that land degradation due to climate change may result in limits to adaptation being reached in coastal regions and areas affected by thawing permafrost. SROCC details that risks of climate-related changes in the ocean and cryosphere may result in limits for ecosystems and vulnerable communities in coral reef environments, urban atoll islands and low-lying Arctic locations before the end of this century in case of high-emissions scenarios.

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The term ‘residual risk’ was not assessed in detail in AR5 and was used interchangeably with other terms, including ‘residual impacts’, ‘residual loss and damage’ and ‘residual damage’. SR15 includes discussion of residual risks without an explicit definition and relates these to L oss and D amage and limits to adaptation, concluding that residual risks rise as global temperatures increase from 1.5°C to 2°C. SRCCL refers to residual risks arising from limits to adaptation related to land management. Such residual risk can emerge from irreversible forms of land degradation, such as coastal erosion when land completely disappears, collapse of infrastructure due to thawing of permafrost, and extreme forms of soil erosion. SROCC advanced the conceptualisation of residual risk and integrated it within the risk framework, defining residual risk as the risk that remains after actions have been taken to reduce hazards, exposure and/or vulnerability. Residual risk is therefore generally higher where adaptation failure, insufficient adaptation or limits to adaptation occur. We use the SROCC definition of residual risk for our assessment in the following sections and identify residual risks that are associated with limits to adaptation.

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CMIP6 climate models project drying in the Amazon—especially in June–July–August, irrespective of future forcing scenario, but which increases with GSAT/higher scenarios (Lee et al., 2021). For higher GSAT levels, Burton et al. (2021) explore different forcing scenarios and found, regardless of scenario, burned area increases markedly with GSAT. New understanding of the role of vegetation stomata will act to exacerbate this drying (Richardson et al., 2018b). A transition to high risk of savannisation for the Amazon alone was assessed to lie between 1.5°C and 3°C with a median value of 2.0°C. A mean temperature increase of 2°C could reduce Arctic permafrost area ~15% by 2100 (Comyn-Platt et al., 2018). Chapter 2 has assessed ecosystem carbon loss from tipping points in tropical forest and loss of Arctic permafrost, and finds a transition from moderate to high risk over the range 1.5°C to 3°C with a median of 2°C (medium confidence, Table SM2.5, Figure 2.11). Its assessment of the transition from high to very high risk is located over the range 3–5°C (low confidence, Table SM2.5, Figure 2.11) based on the potential for Amazon Forest dieback between 4°C and 5°C temperature increase above the pre-industrial period (Salazar and Nobre, 2010).

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Natali, S.M., et al., 2019: Large loss of CO2 in winter observed across the northern permafrost region. Nature Clim Change, 9 (11), 852–857, doi:10.1038/s41558-019-0592-8.

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Yumashev, D., et al., 2019: Climate policy implications of nonlinear decline of Arctic land permafrost and other cryosphere elements. Nat Commun, 10 (1), 1–11.

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In the Arctic, warming temperature and sea level rise constitute key risks to the loss of identity and culture of Indigenous People. This is associated with migration and relocation due to livelihood deterioration resulting from coastal erosion, permafrost thaw and reduced fisheries productivity (Roberts and Andrei, 2015; Roy et al., 2018). These risks and losses often encompass various non-economic losses, such as the loss of identity, that cannot be replaced or economically compensated (see also Section 8.3.5).

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Landslides. While geomorphological events (e.g., land subsidence from permafrost thaw at high latitudes or from groundwater extraction) and factors associated with the built environment (e.g., settlement location adjacent to steep slopes and zonation laws for building construction) are major factors determining urban landslide risk, these can also be influenced by a range of climatic variables, namely precipitation (frequency, intensity and duration), snow melt and temperature change. Some 48 million people are exposed to landslide risk in Europe alone, with the majority in smaller urban centres (Mateos et al., 2020). Travassos et al. (2020) also documented all landslide deaths in the São Paulo Macro Metropolis Region from 2016 to 2019 that occurred from extreme rainfall events in vulnerable areas prone to landslides. An increase in the number of people exposed to urban landslide risks is projected for landslide-prone settlements lying within regions projected to experience a corresponding increase in extreme rainfall (Gariano and Guzzetti, 2016). In addition, human factors such as expansion of towns onto unstable land and land use changes within settlements (e.g., road building, deforestation) are increasing human exposure to landslides and the likelihood of landslides occurring (Kirschbaum, Stanley and Zhou, 2015). Rainfall triggered landslides kill at least 5000 people per year, and at least 11.7% of these landslides occurred on road networks (Froude and Petley, 2018). Although the spatial footprint of an individual landslide might be small (i.e., < 1 km 2), the ‘vulnerability shadow’ cast over an area in terms of regional transport network disruptions can be a significant proportion of a region, and cascade to other infrastructures (Winter et al., 2016).

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Fuels Extraction and Distribution. Non-electric energy infrastructure is susceptible to many of the same impacts as electric infrastructure. Extreme weather events impact extraction (onshore and offshore) and refining operations of petroleum, oil, coal, gas and biofuels. Disruption of road, rail and shipping routes (see Section 6.2.5.2) interrupts fuel supply chains. However, there are a number of risks that are specific to these sectors. Heat can lead to expansion in oil and gas pipes, increasing the risk of rupture (Sieber, 2013), whilst heatwaves and droughts can reduce the availability of biofuel (Moiseyev et al., 2011; Schaeffer et al., 2012). Subsidence and shrinkage of soils damages underground assets such as pipes intakes (Cruz and Krausmann, 2013), while additional human activity such as extractive drilling may induce earthquakes, as observed in the northern Dutch province of Groningen (Van der Voort and Vanclay, 2015). In Alaska, USA, the thaw of permafrost and subsequent ground instability is estimated to lead to USD 33 million damages to fuel pipelines in an end-of-century RCP8.5 scenario (Melvin et al., 2017), with low-lying coastal deltas particularly vulnerable (Schmidt, 2015).

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Fixed-line ICT networks that sprawl over large areas are especially susceptible to increases in the frequency or intensity of storms that would increase the risk of wind, ice and snow damage to overhead cables and damage from wind-blown debris. More intense or longer droughts and heatwaves can cause ground shrinkage and damage underground ICT infrastructure (Fu, Horrocks and Winne, 2016). In mountain and northern permafrost regions, communications and other infrastructure networks are subject to subsidence because of warming of ice-rich permafrost (Shiklomanov et al., 2017; Li et al., 2016; Melvin et al., 2017).

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Wright et al. (2012) calculated that strengthening bridges in the USA would cost USD 140–250 billion by 2090 (or several billion dollars a year), but costs are reduced by 30% if interventions are made proactively. Koks et al. (2019) calculate a benefit–cost ratio of greater than one for over 60% of the world’s roads exposed to flooding. The greatest benefits from adaptation of the global road network are in LMICs where reductions in flood risk are typically between 40% and 80%. Pregnolato et al. (2017) showed that in the city of Newcastle upon Tyne (UK), two carefully targeted interventions at key locations to manage surface water flooding reduced the impacts of the 1-in-50 year event in 2050 by 32%. In permafrost regions, geo-reinforcement, foundation and piles can be strengthened (Trofimenko, Evgenev and Shashina, 2017), whilst passive cooling methods, including high-albedo surfacing, sun-sheds and heat drains can cool infrastructure (Doré, Niu and Brooks, 2016).

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Doré, G., F. Niu and H. Brooks, 2016: Adaptation methods for transportation infrastructure built on degrading permafrost. Permafr. Periglac. Process. , 27 (4), 352–364.

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Hjort, J., et al., 2018: Degrading permafrost puts Arctic infrastructure at risk by mid-century. Nat. Commun. , 9 (1), 5147.

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Li, G., et al., 2016: Freeze–thaw properties and long-term thermal stability of the unprotected tower foundation soils in permafrost regions along the Qinghai–Tibet Power Transmission Line. Cold. Reg. Sci. Technol. , 121, 258–274.

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Shiklomanov, N.I., D.A. Streletskiy, T.B. Swales and V.A. Kokorev, 2017: Climate change and stability of urban infrastructure in Russian permafrost regions: prognostic assessment based on GCM climate projections. Geogr. Rev. , 107 (1), 125–142.

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Trofimenko, Y.V., G. Evgenev and E. Shashina, 2017: Functional loss risks of highways in permafrost areas due to climate change. Procedia Eng. , 189, 258–264.

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Future risks for Indigenous Peoples’ health and well-being in a changing climate will result foremost from exacerbations of observed impacts. Primary and secondary health risks are expected to increase as the frequency and/or severity of climate hazards grow in many regions. As one example, melting permafrost in the Siberian Arctic is projected to lead to more outbreaks of anthrax (Bogdanova et al., 2021). Tertiary health threats are expected to persist even with strong global initiatives to mitigate greenhouse gases (GHGs) (Butler and Harley, 2010). Climate change is expected to compound non-climatic processes that lead to social exclusion and land dispossession that underlay health inequalities experienced by Indigenous Peoples (Huber et al., 2020a).

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Climate change in northern regions, including Arctic ecosystems, is causing permafrost to thaw, creating the potential for mercury (Hg) to enter the food chain (medium agreement, low evidence)as methyl mercury (MeHg), which is highly neurotoxic and nephrotoxic and bioaccumulates and biomagnifies throughout the food chain via dietary uptake of fish, seafood and mammals. Mercury methylation processes in aquatic environments have been found to be exacerbated by ocean warming, coupled with more acidic and anoxic sediments (FAO, 2020). Consumption of mercury-contaminated fish has been found to be linked to neurological disorders due to methyl mercury poisoning (i.e., Minamata disease) that is associated with climate change-contaminant interactions that alter the bioaccumulation and biomagnification of toxic and fat-soluble persistent organic pollutants and polychlorinated biphenyls (PCBs) (Alava et al., 2017) in seafood and marine mammals (medium confidence). Indigenous Peoples have a higher exposure to such risks because of the accumulation of such toxins in traditional foods (J.J. et al., 2017). Contamination of food with PCBs and dioxins has a range of adverse health impacts (Lake et al., 2015).

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The main findings of previous reports, particularly the WGII AR5 (Kovats et al., 2014) and the IPCC Special Report on 1.5°C (Hoegh-Guldberg et al., 2018), highlighted the impacts of warming and rainfall variations and their extremes on Europe, particularly SEU and mountainous areas. At 2°C GWL, 9% of Europe’s population was projected to be exposed to aggravated water scarcity, and 8% of the territory of Europe were characterised to have a high or very high sensitivity to desertification (UNEP/UNECE, 2016). These impacts are driven by changes in temperature, precipitation, irrigation developments, population growth, agricultural policies and markets (EEA, 2017a). Heat is a main hazard for high-latitude ecosystems (Kovats et al., 2014; Jacob et al., 2018; Hock et al., 2019). The majority of mountain glaciers lost mass during the past two decades, and permafrost in the European Alps and Scandinavia is decreasing (Hock et al., 2019). In Central Europe, Scandinavia and Caucasus, mountain glaciers were projected to lose 60–80% of their mass by the end of the 21st century (Hock et al., 2019). The combined impacts on tourism, agriculture, forestry, energy, health and infrastructure were suggested to make SEU highly vulnerable and increase the risks of failures and vulnerability for urban areas (Kovats et al., 2014). Previous reports stated that the adaptive capacity in Europe is high compared with other regions of the world, but that there are also limits to adaptation from physical, social, economic and technological factors. Evidence suggested that staying within 1.5°C GWL would strongly increase Europe’s ability to adapt to climate change (de Coninck et al., 2018).

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Changes in several climatic-impact drivers have already emerged in all regions of Europe: increases in mean temperature and extreme heat, and decreases in cold spells (Ranasinghe et al., 2021; Seneviratne et al., 2021). Lake and river ice has decreased in NEU, WCE and MED, and sea ice in NEUS (Fox-Kemper et al., 2021; Ranasinghe et al., 2021). With increasing warming, confidence in projections is increasing for more drivers (Figure 13.3). Mean and maximum temperatures, frequencies of warm days and nights, and heatwaves have increased since 1950, while the corresponding cold indices have decreased (high confidence) (Ranasinghe et al., 2021; Seneviratne et al., 2021). Average warming will be larger than the global mean in all of Europe, with largest winter warming in NEU and EEU and largest summer warming in MED (high confidence) (Gutiérrez et al., 2021; Ranasinghe et al., 2021). An increase in hot days and a decrease in cold days are very likely (Figure 13.4a,b). Projections suggest a substantial reduction in European ice glacier volumes and in snow cover below elevations of 1500–2000 m, as well as further permafrost thawing and degradation, during the 21st century, even at a low GWL (high confidence) (Ranasinghe et al., 2021).

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At 2°C GWL, the operation of low-altitude resorts without snowmaking will likely be discontinued, while beyond 3°C GWL, snowmaking will be necessary, but not always sufficient, for most resorts in many European mountains and parts of NEU (Pons et al., 2015; Joly and Ungureanu, 2018; Scott et al., 2019; Spandre et al., 2019). Expanding snowmaking is capital intensive and will strongly increase water and energy consumption, particularly at 3°C GWL and beyond (Spandre et al., 2019; Morin et al., 2021), adversely affecting the financial stability of small resorts (Pons et al., 2015; Falk and Vanat, 2016; Spandre et al., 2016; Joly and Ungureanu, 2018; Moreno-Gené et al., 2018; Steiger and Scott, 2020). Permafrost degradation due to rising temperatures is expected to create stability risks for ropeway transport infrastructure at high-altitude Alpine areas (Duvillard et al., 2019).

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Damm, B. and A. Felderer, 2013: Impact of atmospheric warming on permafrost degradation and debris flow initiation: a case study from the eastern European Alps. E&G Quat. Sci. J. , 62 (2), 136–149, doi:10.3285/eg.62.2.05.

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Duvillard, P.-A., L. Ravanel, M. Marcer and P. Schoeneich, 2019: Recent evolution of damage to infrastructure on permafrost in the French Alps. Reg. Environ. Change, 19 (5), 1281–1293, doi:10.1007/s10113-019-01465-z.

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Konnova, L.A. and Y.V. Lvova, 2019: Permafrost degradation in security context livelihoods in the Arctic Zone of the Russian Federation. Probl. Technosphere Risk Manag. , 3 (51), 27–33.

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Shiklomanov, N.I., D.A. Streletskiy, T.B. Swales and V.A. Kokorev, 2017: Climate change and stability of urban infrastructure in Russian permafrost regions: prognostic assessment based on GCM climate projections. Geogr. Rev. , 107 (1), 125–142, doi:10.1111/gere.12214.

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Streletskiy, D.A., et al., 2019: Assessment of climate change impacts on buildings, structures and infrastructure in the Russian regions on permafrost. Environ. Res. Lett. , 14 (2), 25003, doi:10.1088/1748-9326/aaf5e6.

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Yakubovich, A.N. and I. A. Yakubovich, 2018: Analysis of the multidimensional impact of climate change on the operation safety of the road network of the permafrost zone of Russia. Intell. Innov. Invest. , 3, 77–83.

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Indigenous Peoples are affected dramatically by climate-related disasters and other climate-related extreme environmental events (very high confidence). Indigenous Peoples face numerous threats and have already been harmed by, and are planning for, extreme weather events with associations to climate change, including hurricanes and tornadoes (Oneida Nation Pre-Disaster Mitigation Plan Steering Committee and Bay-Lake Regional Planning Commission, 2016; Emanuel, 2019; Cooley, 2021; Marks-Marino, 2021; Zambrano et al., 2021), heatwaves (Confederated Tribes of the Umatilla Indian Reservation, 2016; Wall, 2017; La Jolla Band of Luiseno Indians, 2019; Mashpee Wampanoag, 2019; Wiecks et al., 2021), ocean warming and MHWs (Hoh Indian Tribe, 2016; Port Gamble S’klallam Tribe, 2016; Port Gamble S’klallam Tribe, 2020; State of Alaska, 2020; Muckleshoot Tribal Council, 2021; Port Gamble S’klallam Tribe, 2021), wildfires (Voggesser et al., 2013; Billiot et al., 2020a; Cozzetto et al., 2021b; Gaughen et al., 2021; Morales et al., 2021; National Tribal Air Association, 2021; Zambrano et al., 2021), permafrost thaw (Haynes et al., 2018; Low, 2020), flooding (Riley et al., 2011; Ballard and Thompson, 2013; Brubaker et al., 2014; Thompson et al., 2014; Burkett et al., 2017; Quinault Indian Nation, 2017; Ristroph, 2019; Sharp, 2019; Thistlethwaite et al., 2020) and drought (Knutson et al., 2007; Chief et al., 2016; Redsteer et al., 2018; Sioui, 2019; Bamford et al., 2020; Sauchyn et al., 2020). Some Indigenous Peoples are facing climate-change impacts that generate community-led permanent relocation and resettlement as an adaptation option (Maldonado et al., 2021). Coastal erosion is one climate-change issue that is often connected to Indigenous Peoples planning to resettle, including vulnerability connected to higher sea levels and storm surges (Quinault Indian Nation, 2017; Bronen et al., 2018; Affiliated Tribes of Northwest Indians, 2020). Adapting to new settlement areas threatens the continuity of communities. In a number of cases, Indigenous Peoples’ having less access to adequate infrastructure is a driver of vulnerability to climate-related disasters and extreme weather events (Doyle et al., 2018; Patrick, 2018; Cozzetto et al., 2021a; Indigenous Climate Action et al., 2021). Disasters and extreme events are particularly severe when their impacts are compounded by inadequate infrastructure. Lack of flood protection infrastructure on Indigenous reserve communities leads to displacement, loss of homes and perpetuates disproportionate levels of risk to extreme weather events (Cunsolo et al., 2020; Fayazi et al., 2020; Yellow Old Woman-Munro et al., 2021).

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Indigenous Peoples throughout North America have experienced five centuries of territorial expropriation, loss of access to natural resources and, in many cases, barriers to the use of their sacred sites (Gabbert, 2004; Louis, 2007). The history of Indigenous struggles to preserve distinct cultural knowledge and assert autonomy in the face of colonialism has shaped land-use patterns and relationships with traditional territories (Cross-Chapter Box INDIG in Chapter 18; Alfred and Corntassel, 2005; Tuhiwai Smith, 2021). Climate change is now creating additional challenges for Indigenous Peoples. For example, increased water scarcity due to higher temperatures and diminished precipitation have led to reduced crop yields for Maya farmers in Yucatan (Sioui, 2019). Thawing permafrost in subarctic Canada (Quinton et al., 2019) has interfered with the land-based livelihoods of the Indigenous Dene Peoples (CCP6).

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In northern Canada, a fusion of leading-edge Western science and IK on permafrost informed the co-development of predictive decision-support tools and risk management strategies to inventory and manage permafrost and adapt to permafrost thaw (CCP6). Permafrost thaw in the Dehcho region of Canada is widespread and occurring at unprecedented rates (WGI). The Dehcho Collaborative on Permafrost (DCoP) aims to improve the understanding of and ability to predict and adapt to permafrost thaw 3. 1 DCoP’s collaborative approach, which places Indigenous Peoples in leadership positions, generates the new knowledge, predictive capacity and decision-support tools to manage natural resources that support Indigenous Dene Peoples’ ways of life. Indigenous–academic partnerships can enhance climate-change adaptation and mitigation capacity, and provide openings for more holistic co-management approaches that recognise and affirm the central role of Indigenous Peoples as stewards of their ancestral territories, especially as they face accelerating climate-change impacts. Academic researchers and their Indigenous partners can support climate-change resilience via mobilising IK in stewardship and adaptation; researching governance arrangements, economic relationships and other factors that hinder Indigenous efforts in these areas; proposing evidence-based policy solutions at international and national scales; and outlining culturally relevant tools for assessing vulnerability and building capacity will also support climate-change resilience. Such IK underpins successful climate-change adaptation and mitigation (very high confidence) (see Green and Raygorodetsky, 2010; Kronik and Verner, 2010; Alexander et al., 2011; Powless, 2012; Ford et al., 2016; Nakashima et al., 2018). The inclusion of IK in adaptation and mitigation not only supports Indigenous cultural survival but also enables governments to recognise the territorial sovereignty of Indigenous Peoples.

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Severe ecosystem consequences of warming and drying are well documented (very high confidence) (Table 14.2). Significant ecosystem changes are expected from projected climate change (high confidence), such as in Mexican cloud forests (Helmer et al., 2019), North American rangelands (Polley et al., 2013; Reeves et al., 2014) and montane forests (Stewart et al., 2021; Wright et al., 2021). Permafrost thaw is projected to increase in Alaska and Canada (DeBeer et al., 2016; see also Ranasinghe et al., 2021), accelerating carbon release (CCP6, see also Canadell et al., 2021) and affecting hydrology. Predicting which species or ecosystems are vulnerable is challenging (Stephenson et al., 2019), although palaeo-ecological data (e.g., pollen, tree rings) provide context from past events to better understand current and future transformations (Nolan et al., 2018).

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Climate-related food-borne disease risks vary temporally, and are influenced, in part, by food availability, accessibility, preparation and preferences (medium confidence). For example, seafood risks are more pronounced in coastal regions due to high seafood consumption (Radke et al., 2015). In Alaska and northern Canada, where locally harvested foods are critical to diet, climate change may introduce new pathogens to local food sources through wildlife range changes, warming temperatures affecting safe fermentation and drying preparation methods, and food temperature control in below-ground cold storage in or near permafrost (King and Furgal, 2014; Harper et al., 2015; Rapinski et al., 2018).

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Transportation infrastructure, including roads, bridges, rail, air, sea and pipelines, are highly vulnerable to rising temperatures, SLR, weather extremes, changing ice conditions, permafrost degradation and flooding (high confidence), resulting in damage, disruption to operations, unsafe conditions and supply chain impacts (see Box 14.5; Board and Council, 2008; Natural Resources Conservation Service; Andrey and Palko, 2017; Jacobs et al., 2018; Lemmen et al., 2021). In the Mexican states of Veracruz, Tabasco, San Luis Potosí, Chiapas and Oaxaca, 105,000 infrastructure sites, mostly major connecting roads, were found to be at risk of flooding from tropical storms (De la Peña et al. 2018). Low water levels in the Great Lakes has severely impacted US grain transport (Attavanich et al., 2013). High-intensity rain events destroyed 1000 km of roads and washed out hundreds of bridges and culverts in 2013 resulting in an estimated 6 billion CAD (considering the 2013 CAD value) in damages and recovery costs in Alberta, Canada (Palko and Lemmen, 2017). In 2019, the rail line from Winnipeg to Churchill Manitoba, which is the only ground transportation to the community and to Canada’s only deep-water Arctic port, was reopened after being closed for over 2 years due to the cumulative effects of flooding, permafrost degradation and political challenges (Lin et al., 2020). In the USA, the number of heat-related train delays has increased (Bruzek et al., 2013; Chinowsky et al., 2019) and, by the end of the century, may cause economic losses of 25–45 billion USD (RCP4.5) or 35–60 billion USD (RCP8.5) (Chinowsky et al., 2019). Sea ice reduction in the North American Arctic has led to a rapid increase in ship traffic (Huntington et al., 2015; Phillips, 2016; Pizzolato et al., 2016; Huntington et al., 2021b; Li et al., 2021) with cascading risks related to invasive species introduction, accident rates, black carbon emissions, underwater noise pollution for marine mammals and risks to subsistence harvesting activities in Indigenous communities (Ware et al., 2014; Council of Canadian Academies, 2016; Huntington, 2021; Verna et al., 2016; Chan et al., 2019).

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Permafrost thaw in northern North America will result in increased construction and reconstruction needs (medium confidence) related to direct damage to buildings, roads, airport runways and other critical infrastructure including decreased bearing capacities of building and pipeline foundations, damage to road surfaces, and deterioration of reservoirs and impoundments used for wastewater and mine tailings containment (Pendakur, 2017; Meredith et al., 2019). Ice roads have become less safe due to warming, pavement damage has increased related to seasonal thaw–freeze cycles and there have been interruptions in airport operations, water and sewage service, and school operations in the Canadian territories of Yukon and Nunavut (Canadian Western and Eastern Arctic, i.e., CA-WA and CA-EA in Figure 14.1) (Council of Canadian Academies, 2019). By the end of the century, the economic impact of projected reconstruction of Alaska’s public infrastructure due to climate change (mainly from permafrost thaw) is estimated to range from 4.2 billion USD (RCP4.5) to 5.5 billion USD (RCP8.5) (Melvin et al., 2017; Markon et al., 2018).

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Climate risks may create shocks to the trade system by damaging infrastructure and disrupting supply chains in North America (medium confidence). Sea level rise, flooding, permafrost thaw, landslides and increased frequency and magnitude of extreme weather events are projected to impact transportation infrastructure which will pose challenges to the movement of goods, especially in coastal areas (Lantuit et al., 2012; Doré et al., 2016; Hjort et al., 2018; Koks et al., 2019; Lemmen et al., 2021). Maritime ports are at the greatest risk from climate hazards (Messner et al., 2013; Slack and Comtois, 2016), followed by roads, rail and airports (Anarde et al., 2017). Due to the transnational nature of trade, extreme weather disruptions in one region are likely to lead to cascading effects in other regions (high confidence) (Lemmen et al., 2021). For example, climate change will have negative impacts for global food and energy trade where reductions in crop production and fish stocks in some regions could cause food and fish price spikes elsewhere (Figure 14.10; Sections 14.5.4 and 5.11.8; Beaugrand et al., 2015; Lam et al., 2016; IPCC, 2019a).

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Across North America, climate change poses a risk to social–ecological systems increasingly destabilised by compounding climate impacts and non-climate pressures (high confidence) (Sections 14.5.1–14.5.3) that erode the connectivity and redundancy underpinning system resilience (Sections 14.5.1–14.5.5; Xiao et al., 2017a; Koven et al., 2020; Malhi et al., 2020; Turner et al., 2020). Accelerating climate change and increasingly severe hazards and shocks may induce abrupt changes or push systems, people and species to critical points–tipping points–where a small additional change causes a disproportionately large response, triggering feedbacks that lock systems into novel regimes (Scheffer et al., 2001; Scheffer, 2010; Anderies et al., 2013; Lenton, 2013; Iglesias and Whitlock, 2020; Lenton, 2020a). Climate-change tipping points can compound and amplify climate impacts and risk, induce disparate climate burdens and benefits across human and ecological systems, and irreversibly restructure ecosystems and livelihoods (e.g., species extinctions, fisheries collapse, community-managed relocation) (Lynham et al., 2017). Examples of systems with potential tipping points in North America include (a) permafrost and sea ice loss triggering transformation of ecological and human systems (including substantial shipping opportunities) in the Arctic that are permanent and irreversible except on geological timescales, and which are potentially underway (high agreement, low evidence) (Section 14.6.2; see Box 14.3, CCP6), (b) mid-latitude forest ecosystems at low to middle elevations in western North America where wildfire and cumulative climate and non-climate pressures may restructure forests and succession in ways that promote transition to new vegetation types (Section 14.5.1) and (c) agricultural communities in northern Mexico and the southwest USA where aridification and drought may interact with water resource policies, economic opportunities and pressures, and farm practices to induce either adaptation (via changes in irrigation practices) or farm abandonment, land-use transformation and livelihood changes (due to heat stress, soil deterioration or reduced economic viability) (Sections 14.5.3, 14.5.4, CCP6, Yumashev et al., 2019; Turner et al., 2020; Heinze et al., 2021).

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Chen, Y., et al., 2021: Future increases in Arctic lightning and fire risk for permafrost carbon. Nat. Clim. Chang. , 1–7.

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Derksen, C., et al., 2019: Chapter 5: Changes in snow, ice, and permafrost across Canada. In: Canada’s Changing Climate Report [Bush, E. and D.S. Lemmen(eds.)]. Government of Canada, Ottawa, Ontario, pp. 194–260.

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Doré, G., F. Niu and H. Brooks, 2016: Adaptation methods for transportation infrastructure built on degrading permafrost. Permafr. Periglac. Process. , 27 (4), 352–364, doi:10.1002/ppp.1919.

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Haynes, K.M., R.F. Connon and W.L. Quinton, 2018: Permafrost thaw induced drying of wetlands at Scotty Creek, NWT, Canada. Environ. Res. Lett. , 13, 114001, doi:10.1088/1748-9326/aae46c.

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Hjort, J., et al., 2018: Degrading permafrost puts Arctic infrastructure at risk by mid-century. Nat. Commun. , 9 (1), 5147–5147.

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Lantuit, H., et al., 2012: The arctic coastal dynamics database: a new classification scheme and statistics on arctic permafrost coastlines. Estuaries Coasts, 35 (2), 383–400, doi:10.1007/s12237-010-9362-6.

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Low, M., 2020: The Deh Cho Aboriginal Aquatic Resources and Oceans Management Program-Linking Indigenous Peoples and Academic Researchers for Monitoring of Aquatic Resources in a Region of Rapid Permafrost loss. Vol. 2020, B123–B101.

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McGregor, R., et al., 2010b: Guidelines for Development and Management of Transportation Infrastructure in Permafrost Regions. Canada, T. A. o, Ottawa, ON. 177 pp.

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Yumashev, D., et al., 2019: Climate policy implications of nonlinear decline of Arctic land permafrost and other cryosphere elements. Nat. Commun. , 10 (1), 1–11, doi:10.1038/s41467-019-09863-x.

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Warming and drier conditions are projected through the reduction of total annual precipitation, extreme precipitation and consecutive wet days and an increase in consecutive dry days (Chou et al., 2014). Heatwaves will increase in frequency and severity in places close to the equator like Colombia (Guo et al., 2018; Feron et al., 2019), with a decrease but strong wetting in coastal areas, pluvial and river flood and mean wind increase (Mora et al., 2014). Models project a very likely 2°C GWL increase in the intensity and frequency of hot extremes and decrease in the intensity and frequency of cold extremes. Nevertheless, models project inconsistent changes in the region for extreme precipitation (low confidence) (Figure 12.6; WGI AR6 Table 12.14) (Ranasinghe et al., 2021). The main climate impact drivers in the region, like extreme heat, mean precipitation and coastal and oceanic drivers, will increase and snow, ice and permafrost will decrease with high confidence (WGI AR6 Table 12.6) (Ranasinghe et al., 2021).

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Accelerated warming is reducing tropical glaciers. Glacier volume loss and permafrost thawing will continue in all scenarios (high confidence) (Ranasinghe et al., 2021). On average, the tropical Andes have lost about 30% and more of their area since the 1980s (Basantes-Serrano et al., 2016; Mark et al., 2017; Thompson et al., 2017; Rabatel et al., 2018; Vuille et al., 2018; Reinthaler et al., 2019a; Seehaus et al., 2019; Masiokas et al., 2020). In a low-emissions scenario, by the end of the 21st century, Peru will lose about 50% of its present glacier surface, while in a high-emission scenario there will remain very small areas of only about 3–5% on the highest peaks (Schauwecker et al., 2017).

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Changing glaciers, snow and permafrost (Figure 12.13), in synergy with land use change, have implications for the occurrence, frequency and magnitude of derived floods and landslides (high confidence) (Huggel et al., 2007; Iribarren Anacona et al., 2015; Emmer, 2017; Mark et al., 2017), as well as for landscape transformation through lake formation or drying and for alterations in hydrological dynamics, with impacts on water for human consumption, agriculture, industry, hydroelectric generation, carbon sequestration and biodiversity (high confidence) (Michelutti et al., 2015; Carrivick and Tweed, 2016; Kronenberg et al., 2016; Emmer, 2017; Mark et al., 2017; Milner et al., 2017; Polk et al., 2017; Reyer et al., 2017; Young et al., 2017; Vuille et al., 2018; Cuesta et al., 2019; Drenkhan et al., 2019; Hock et al., 2019; Motschmann et al., 2020a).

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Four sets of downscaling simulations based on the Eta Regional Climate Model forced by two global climate models (Chou et al., 2014) projected warmer conditions (more than 1°C) for the entire sub-region by 2050 under the RCP4.5 scenario (medium confidence). Extremely warm December–January–February days as well as the number of heatwaves per season are expected to increase by 5–10 times in northern Chile (Feron et al., 2019), likely increasing in the intensity and frequency of hot extremes over the entire region (WGI AR6 Table 11.13) (Seneviratne et al., 2021). Drier conditions (medium confidence), by means of a decrease in total annual and extreme precipitation, are expected to increase for southern Chile, but inconsistent changes are expected in the sub-region (low confidence) (Chou et al., 2014) (WGI AR6 Table 11.14) (Seneviratne et al., 2021) with high confidence upon an increase in fire weather and a decrease in permafrost and snow extent (WGI AR6 Table 12.6, Ranasinghe et al., 2021).

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Increasing glacier lake outburst floods (GLOFs), ice and rock avalanches, debris flows and lahars from ice-capped volcanoes have been observed in SWS (Iribarren Anacona et al., 2015; Jacquet et al., 2017; Reinthaler et al., 2019b). There is low evidence on the effects of warming and degrading permafrost on slope instability and landslides in these regions (Iribarren Anacona et al., 2015).

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It is expected that an increase in the intensity of heavy precipitation, droughts and fire weather will intensify through the 21st century in SSA, but mean wind will decrease (medium confidence) (Kitoh et al., 2011; WGI AR6 Tables 11.14 and Table 11.15, Seneviratne et al., 2021). The probability of extended droughts, such as the recently experienced mega-drought (2010–2015), increases to up to 5 events/100 yr (Bozkurt et al., 2017). Snow, glaciers, permafrost and ice sheets will decrease with high confidence (WGI AR6 Table 12.6, Ranasinghe et al., 2021). The observed area and the elevation changes indicate that the Echaurren Norte glacier may disappear in the coming years if negative mass balance rates prevail (medium confidence) (Farías-Barahona et al., 2019).

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Identification and assessment of key risks are informed by observed and projected impacts in the different sub-regions of CSA (Section 12.3). Figure 12.10 shows a summary of different levels of observed and future impacts per sub-region for different sectors, based on a detailed assessment of climate-change impacts on various systems and components for the corresponding sector (Figure 12.9). This assessment is consistent with and complementary to the assessment in Section 12.3. A synthesis of these impacts (Figure 12.10) indicates the following: Climate change has or will have a major impact on the observed and future decline of Andean glaciers and snow (high confidence) and lead to the degradation of permafrost and destabilisation of related landscapes (medium evidence, high agreement ). Water quality is a major concern across the region, but there is limited evidence of impacts of climate change on water quality as well as on groundwater. Climate change has had a significant impact on terrestrial and freshwater ecosystems in the NWS, SES and SWS sub-regions and a medium impact in the other sub-regions, but the level of confidence varies across sub-regions. Projections indicate a strong impact of climate change on these ecosystems for the future (medium confidence: medium evidence, high agreement ). Many aspects and assets of ocean and coastal ecosystems (e.g., mangroves, coral reefs, saltmarshes) were identified as being strongly impacted by climate change, both for observed and future periods (high confidence) (Section 12.5.2; Figure 12.9).

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Haeberli, W., Y. Schaub and C. Huggel, 2017: Increasing risks related to landslides from degrading permafrost into new lakes in de-glaciating mountain ranges. Geomorphology, 293 (Part B), 405–417, doi:10.1016/j.geomorph.2016.02.009.

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Iribarren Anacona, P., A. Mackintosh and K.P. Norton, 2015: Hazardous processes and events from glacier and permafrost areas: lessons from the Chilean and Argentinean Andes. Earth Surf. Process. Landf. , 40 (1), 2–21, doi:10.1002/esp.3524.

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Yumashev, D. et al., 2019: Climate policy implications of nonlinear decline of Arctic land permafrost and other cryosphere elements. Nat. Commun. , 10(1) , 1900, doi:10.1038/s41467-019-09863-x.

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The assessment of the Physical Science Basis (IPCC AR6 WGI) documents sustained and widespread changes in the atmosphere, cryosphere, biosphere and ocean, providing unequivocal evidence of a world that has warmed, associated with rising atmospheric CO2 concentrations reaching levels not experienced in at least the last 2 million years. Aside from temperature, other clearly discernible, human-induced changes beyond natural variations include declines in Arctic Sea ice and glaciers, thawing of permafrost, and a strengthening of the global water cycle (AR6 WGI SPM A.2, B.3 and B.4). Oceanic changes include rising sea level, acidification, deoxygenation, and changing salinity (WGI SPM B.3). Over land, in recent decades, both frequency and severity have increased for hot extremes but decreased for cold extremes; intensification of heavy precipitation is observed in parallel with a decrease in available water in dry seasons, along with an increased occurrence of weather conditions that promote wildfires.

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Developments since AR5 and IPCC Special Reports (SR1.5, SROCC and SRCCL). Recent studies continue to report high carbon stocks in peatlands and emphasise the vulnerability of peatland carbon after conversion. The carbon stocks of tropical peatlands are among the highest of any forest, 1,211–4,257 tCO2-eq ha –1 in the Peruvian Amazon (Bhomia et al. 2019) and 1,956–14,757 tCO2-eq ha –1 in Indonesia (Novita et al. 2021). Ninety percent of tropical peatland carbon stocks are vulnerable to emission during conversion and may not be recoverable through restoration; in contrast, boreal and temperate peatlands hold similar carbon stocks (1,439–5,619 tCO2-eq ha –1) but only 30% of northern carbon stocks are vulnerable to emission during conversion and irrecoverable through restoration (Goldstein et al. 2020). A recent study shows global mitigation potential of about 0.2 GtCO2-eq yr –1 at costs up to USD100 tCO2–1 (Roe et al. 2021). Another study estimated that 72% of mitigation is achieved through avoided soil carbon impacts, with the remainder through avoided impacts to vegetation (Bossio et al. 2020). Recent model projections show that both peatland protection and peatland restoration (Section 7.4.2.7) are needed to achieve a 2°C mitigation pathway and that peatland protection and restoration policies will have minimal impacts on regional food security (Leifeld et al. 2019, Humpenöder et al. 2020). Global studies have not accounted for extensive peatlands recently reported in the Congo Basin, estimated to cover 145,500 km 2 and contain 30.6 PgC, as much as 29% of total tropical peat carbon stock (Dargie et al. 2017). These Congo peatlands are relatively intact; continued preservation is needed to prevent major emissions (Dargie et al. 2019). In northern peatlands that are underlain by permafrost roughly 50% of the total peatlands north of 23° latitude, (Hugelius et al. 2020), climate change (i.e., warming) is the major driver of peatland degradation (e.g., through permafrost thaw) (Schuur et al. 2015, Goldstein et al. 2020). However, in non-permafrost boreal and temperate peatlands, reduction of peatland conversion is also a cost-effective mitigation strategy. Peatlands are sensitive to climate change and there is low confidence about the future peatland sink globally (SRCCL, Chapter 2). Permafrost thaw may shift northern peatlands from a net carbon sink to net source (Hugelius et al. 2020). Uncertainties in peatland extent and the magnitude of existing carbon stocks, in both northern (Loisel et al. 2014) and tropical (Dargie et al. 2017) latitudes limit understanding of current and future peatland carbon dynamics (Minasny et al. 2019).

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Currently, land degradation is estimated to have reduced productivity in 23% of the global terrestrial area, and between USD235 billion and USD577 billion in annual global crop output is at risk because of pollinator loss (IPBES 2019a). The global trends reviewed above are based on data from 2000 studies. It is not clear whether the assessment included a quality control check of the studies evaluated and suffer from aggregation bias. For instance, a recent meta-analysis of global forest valuation studies noted that many studies reviewed had shortcomings such as failing to clearly mention the methodology and prices used to value the forest ecosystem services, double counting, data errors, and so on (Ninan and Inoue 2013). Furthermore, the criticisms against the paper by (Costanza et al. 1997), such as ignoring ecological feedbacks and non-linearities that are central to the processes that link all species to each other and their habitats, ignoring substitution effects may also apply to the global assessment (Smith 1997; Bockstael et al. 2000; Loomis et al. 2000). Land degradation has had a pronounced impact on ecosystem functions worldwide (IPBES 2018e). Net primary productivity of ecosystem biomass and of agriculture is presently lower than it would have been under a natural state on 23% of the global terrestrial area, amounting to a 5% reduction in total global net primary productivity (IPBES 2018e). Over the past two centuries, soil organic carbon, an indicator of soil health, has seen an estimated 8% loss globally (176 GtC) from land conversion and unsustainable land management practices (IPBES 2018e). Projections to 2050 predict further losses of 36 GtC from soils, particularly in sub-Saharan Africa. These losses are projected to come from the expansion of agricultural land into natural areas (16 GtC), degradation due to inappropriate land management (11 GtC) and the draining and burning of peatlands (9 GtC) and melting of permafrost (IPBES 2018e). Trends in biodiversity measured by the global living planet index between 1970 to 2016 indicate a 68% decline in monitored population of mammals, birds, amphibians, reptiles, and fish (WWF 2020). FAO’s recent report on the state of the world’s biodiversity for food and agriculture points to an alarming decline in biodiversity for food and agriculture including associated biodiversity such as pollination services, microorganisms which are essential for production systems (FAO 2019d). These suggest that overall ecosystem health is consistently declining with adverse consequences for good quality of life, human well-being, and sustainable development.

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The future impacts of climate change on land systems are highly uncertain, for example, the role of permafrost thaw, tipping points, increased disturbances and enhanced CO2 fertilisation (Friedlingstein et al. 2020). Further research into these mechanisms is critical to better understand the permanence of mitigation measures in land sector.

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Hugelius, G. et al., 2020: Large stocks of peatland carbon and nitrogen are vulnerable to permafrost thaw. Proc. Natl. Acad. Sci. , 117(34) , 20438–20446, doi:10.1073/pnas.1916387117.

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Schuur, E.A.G. et al., 2015: Climate change and the permafrost carbon feedback. Nature, 520(7546) , 171–179, doi:10.1038/nature14338.

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Climate change has caused widespread adverse impacts and related losses and damages to nature and people (high confidence). Losses and damages are unequally distributed across systems, regions and sectors (high confidence). Cultural losses, related to tangible and intangible heritage, threaten adaptive capacity and may result in irrevocable losses of sense of belonging, valued cultural practices, identity and home, particularly for Indigenous Peoples and those more directly reliant on the environment for subsistence. (medium confidence). For example, changes in snow cover, lake and river ice, and permafrost in many Arctic regions, are harming the livelihoods and cultural identity of Arctic residents including Indigenous populations (high confidence). Infrastructure, including transportation, water, sanitation and energy systems have been compromised by extreme and slow-onset events, with resulting economic losses, disruptions of services and impacts to well-being (high confidence). {WGII SPM B.1, WGII SPM B.1.2, WGII SPM.B.1.5, WGII SPM C.3.5, WGII TS.B.1.6; SROCC SPM A.7.1}

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Continued GHG emissions will further affect all major climate system components, and many changes will be irreversible on centennial to millennial time scales. Many changes in the climate system become larger in direct relation to increasing global warming. With every additional increment of global warming, changes in extremes continue to become larger. Additional warming will lead to more frequent and intense marine heatwaves and is projected to further amplify permafrost thawing and loss of seasonal snow cover, glaciers, land ice and Arctic sea ice (high confidence). Continued global warming is projected to further intensify the global water cycle, including its variability, global monsoon precipitation 117 , and very wet and very dry weather and climate events and seasons (high confidence). The portion of global land experiencing detectable changes in seasonal mean precipitation is projected to increase (medium confidence) with more variable precipitation and surface water flows over most land regions within seasons (high confidence).and from year to year (medium confidence). Many changes due to past and future GHG emissions are irreversible 118 on centennial to millennial time scales, especially in the ocean, ice sheets and global sea level (see 3.1.3). Ocean acidification (virtually certain), ocean deoxygenation (high confidence).and global mean sea level (virtually certain).will continue to increase in the 21st century, at rates dependent on future emissions. {WGI SPM B.2, WGI SPM B.2.2, WGI SPM B.2.3, WGI SPM B.2.5, WGI SPM B.3, WGI SPM B.3.1, . WGI SPM B.3.2, WGI SPM B.4, WGI SPM B.5, WGI SPM B.5.1, WGI SPM B.5.3, WGI Figure SPM.8}. (Figure 3.1)

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Sea level rise is unavoidable for centuries to millennia due to continuing deep ocean warming and ice sheet melt, and sea levels will remain elevated for thousands of years (high confidence). Global mean sea level rise will continue in the 21st century (virtually certain), with projected regional relative sea level rise within 20% of the global mean along two-thirds of the global coastline (medium confidence). The magnitude, the rate, the timing of threshold exceedances, and the long-term commitment of sea level rise depend on emissions, with higher emissions leading to greater and faster rates of sea level rise. Due to relative sea level rise, extreme sea level events that occurred once per century in the recent past are projected to occur at least annually at more than half of all tide gauge locations by 2100 and risks for coastal ecosystems, people and infrastructure will continue to increase beyond 2100 (high confidence). At sustained warming levels between 2°C and 3°C, the Greenland and West Antarctic ice sheets will be lost almost completely and irreversibly over multiple millennia (limited evidence). The probability and rate of ice mass loss increase with higher global surface temperatures (high confidence). Over the next 2000 years, global mean sea level will rise by about 2 to 3 m if warming is limited to 1.5°C and 2 to 6 m if limited to 2°C (low confidence). Projections of multi-millennial global mean sea level rise are consistent with reconstructed levels during past warm climate periods: global mean sea level was very likely 5 to 25 m higher than today roughly 3 million years ago, when global temperatures were 2.5°C to 4°C higher than 1850–1900 (medium confidence). Further examples of unavoidable changes in the climate system due to multi-decadal or longer response timescales include continued glacier melt (very high confidence) and permafrost carbon loss (high confidence). {WGI SPM B.5.2, WGI SPM B.5.3, WGI SPM B.5.4, WGI SPM C.2.5, WGI Box TS.4, WGI Box TS.9, WGI 9.5.1; WGII TS C.5; SROCC SPM B.3, SROCC SPM B.6, SROCC SPM B.9}. (Figure 3.4)

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In scenarios with increasing CO2 emissions, the land and ocean carbon sinks are projected to be less effective at slowing the accumulation of CO2 in the atmosphere (high confidence). While natural land and ocean carbon sinks are projected to take up, in absolute terms, a progressively larger amount of CO2 under higher compared to lower CO2 emissions scenarios, they become less effective, that is, the proportion of emissions taken up by land and ocean decreases with increasing cumulative net CO2 emissions (high confidence). Additional ecosystem responses to warming not yet fully included in climate models, such as GHG fluxes from wetlands, permafrost thaw, and wildfires, would further increase concentrations of these gases in the atmosphere (high confidence). In scenarios where CO2 concentrations peak and decline during the 21st century, the land and ocean begin to take up less carbon in response to declining atmospheric CO2 concentrations (high confidence) and turn into a weak net source by 2100 in the very low GHG emissions scenario. (medium confidence)133 . {WGI SPM B.4, WGI SPM B.4.1, WGI SPM B.4.2, WGI SPM B.4.3}

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Overshoot of a warming level results in more adverse impacts, some irreversible, and additional risks for human and natural systems compared to staying below that warming level, with risks growing with the magnitude and duration of overshoot (high confidence). Compared to pathways without overshoot, societies and ecosystems would be exposed to greater and more widespread changes in climatic impact-drivers, such as extreme heat and extreme precipitation, with increasing risks to infrastructure, low-lying coastal settlements, and associated livelihoods (high confidence). Overshooting 1.5°C will result in irreversible adverse impacts on certain ecosystems with low resilience, such as polar, mountain, and coastal ecosystems, impacted by ice-sheet melt, glacier melt, or by accelerating and higher committed sea level rise (high confidence). Overshoot increases the risks of severe impacts, such as increased wildfires, mass mortality of trees, drying of peatlands, thawing of permafrost and weakening natural land carbon sinks; such impacts could increase releases of GHGs making temperature reversal more challenging (medium confidence). {WGI SPM C.2, WGI SPM C.2.1, WGI SPM C.2.3; WGII SPM B.6, WGII SPM B.6.1, WGII SPM B.6.2; SR1.5 3.6}

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Yumashev, D. et al., 2019: Climate policy implications of nonlinear decline of Arctic land permafrost and other cryosphere elements. Nat. Commun. , 10(1) , 1900, doi:10.1038/s41467-019-09863-x.

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Carbon cycle feedbacks co-exist with climate (heat and moisture) feedbacks (Cross-Chapter Boxes 5.1 and 5.3), which together drive contemporary (Section 5.2) and future (Section 5.4) carbon–climate feedbacks (Williams et al., 2019). The excess heat generated by radiative forcing from increasing concentration of atmospheric CO2 and other GHGs is mostly taken up by the ocean (>90%) and the residual balance partitioned between atmospheric, terrestrial and ice melting (Cross-Chapter Box 9.2; Frölicher et al., 2015). The combined effect of these two large-scale negative feedbacks of CO2 and heat are reflected in the TCRE (Section 5.5 and Cross-Chapter Box 5.3), which points to a quasi-linear and quasi-emission-path independent relationship between cumulative emissions of CO2 and global warming, which is used as the basis to estimate the remaining carbon budget (Section 5.5; MacDougall and Friedlingstein, 2015; MacDougall, 2017; Bronselaer and Zanna, 2020; Jones and Friedlingstein, 2020). There is still low confidence on the relative roles and importance of the ocean and terrestrial carbon processes on TCRE variability and uncertainty on centennial time scales (MacDougall, 2016; MacDougall et al., 2017; Williams et al., 2017; Katavouta et al., 2018, 2019; Jones and Friedlingstein, 2020) (Sections 5.5.1.1, 5.5.1.2).

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Independent paleoclimatic evidence suggests with high confidence that marine and terrestrial CH4 and N2O emissions are highly sensitive to climate on (sub)centennial time scales. Limited, yet internally consistent ice core measurements indicate with medium confidence that pulsed geologic CH4 release from continental margins associated with warming remained negligible across the LDT. Multiple lines of evidence suggest with high confidence that CO2 was released from the ocean interior on centennial time scales during the LDT in response to, or associated with warming, contributing to the transition out of the last glacial stage to the current interglacial period.

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Multiple lines of evidence inferred from marine sediment proxies indicate with low to medium confidence that the millennial rates of CO2 concentration change in the atmosphere during the last 56 Myr were at least four to five times lower than during the last century (Figure 5.3). In spite of uncertainties in ice core reconstructions related to delayed enclosure of air bubbles, which tend to smooth the records, there is high confidence that the rates of atmospheric CO2 and CH4 change during the last century were at least 10 and 5 times faster, respectively, than the maximum centennial growth rate averages of those gases during the last 800 kyr (Fig. 5.4).

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Increasing atmospheric CO2 concentration enhances leaf photosynthesis and drives a partial closure of leaf stomata, leading to higher water-use efficiency (WUE) at the leaf canopy and ecosystem scales (Norby and Zak, 2011; De Kauwe et al., 2013; Fatichi et al., 2016; Knauer et al., 2017; Mastrotheodoros et al., 2017). Since AR5 (Box 6.3), a growing body of evidence from tree-ring and carbon isotopes further confirms an increase of plant water-use efficiency over decadal to centennial time scales, with some evidence for a stronger enhancement of photosynthesis compared to stomatal reductions (Frank et al., 2015; Guerrieri et al., 2019; Adams et al., 2020).

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Plant productivity is highly dependent on local climate. In cold environments, warming has generally led to an earlier onset of the growing season, and with it an increase in early season vegetation productivity (e.g., Forkel et al., 2016). However, this trend is affected by the adverse effects of climate variability, and other emerging limitations on vegetation production by water, energy and nutrients, which may gradually reduce the effects of warming (Piao et al., 2017; Buermann et al., 2018; Liu et al., 2019). At centennial time scales, boreal forest expansion may act as a climate-driven carbon sink (Pugh et al., 2018).

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The fourth and fifth methodological advancements are to explicitly account for the zero emissions commitment (ZEC; Section 5.5.2.2.4) and adjust estimates for Earth system feedbacks that are typically not represented in Earth system models (ESMs; Section 5.5.2.2.5). The central estimate of the assessed ZEC used in SR1.5 and AR6 is zero (Section 4.7.1.1). ZEC uncertainties are reported separately (Table 5.8), and the additional consideration of ZEC therefore does result in a better understanding but not in a net shift of central estimates of the remaining carbon budget compared to AR5. Furthermore, AR5 did not explicitly account for Earth system feedbacks not represented in ESMs. The SR1.5 assessed that they could reduce the remaining carbon budgets by about 100 GtCO2 over centennial time scales. This assessment has been updated in AR6, including a wider range of biogeochemical feedbacks and new evidence (Section 5.5.2.2.5). Some of these feedbacks are captured in the estimation of non-CO2 warming (see below), while the combined effect of remaining positive and negative feedbacks is assessed to reduce the remaining carbon budget estimates by 7 ± 27 PgC K–1 (1-sigma range, or 26 ± 97 GtCO2 °C–1) compared to AR5.

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Deutsch, C. et al., 2014: Centennial changes in North Pacific anoxia linked to tropical trade winds. Science, 345(6197), 665–668, doi: 10.1126/science.1252332.

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Lassey, K.R., D.M. Etheridge, D.C. Lowe, A.M. Smith, and D.F. Ferretti, 2007: Centennial evolution of the atmospheric methane budget: what do the carbon isotopes tell us?Atmospheric Chemistry and Physics, 7(8), 2119–2139, doi: 10.5194/acp-7-2119-2007.

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Marcott, S.A. et al., 2014: Centennial-scale changes in the global carbon cycle during the last deglaciation. Nature, 514(7524), 616–619, doi: 10.1038/nature13799.

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Rae, J.W.B. et al., 2018: CO2 storage and release in the deep Southern Ocean on millennial to centennial timescales. Nature, 562(7728), 569–573, doi: 10.1038/s41586-018-0614-0.

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Rhodes, R.H. et al., 2017: Atmospheric methane variability: Centennial-scale signals in the Last Glacial Period. Global Biogeochemical Cycles, 31, 575–590, doi: 10.1002/2016gb005570.

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The AR5 assessed solar variability over multiple time scales, concluding that total solar irradiance (TSI) multi-millennial fluctuations over the past 9 kyr were <1 W m–2, but with no assessment of confidence provided. For multi-decadal to centennial variability over the last millennium, AR5 emphasized reconstructions of TSI that show little change (<0.1%) since the Maunder Minimum (1645–1715) when solar activity was particularly low, again without providing a confidence level. The AR5 further concluded that the best estimate of radiative forcing due to TSI changes for the period 1750–2011 was 0.05–0.10 W m–2 (medium confidence), and that TSIvery likely changed by –0.04 [–0.08 to 0.00] W m–2 between 1986 and 2008. Potential solar influences on climate due to feedbacks arising from interactions with galactic cosmic rays are assessed in Section 7.3.4.5.

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SAOD averaged over the period 950–1250 CE (0.012) was lower than for the period 1450–1850 CE (0.017) and similar to the period 1850–1900 (0.011). Uncertainties associated with these inter-period differences are not well quantified but have little effect because the uncertainties are mainly systematic throughout the record. Over the past 100 years, SAOD averaged 14% lower than the mean of the previous 24 centuries (back to 2.5 ka), and well within the range of centennial-scale variability (Toohey and Sigl, 2017).

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Well-mixed greenhouse gases generally have lifetimes of more than several years. The AR5 assigned medium confidence to the values of atmospheric CO2 concentrations (mixing ratios) during the warm geological periods of the early Eocene and Pliocene. It concluded with very high confidence that, by 2011, the mixing ratios of CO2, CH4, and N2O in the atmosphere exceeded the range derived from ice cores for the previous 800 kyr, and that the observed rates of increase of the greenhouse gases were unprecedented on centennial timescales over at least the past 22 kyr. It reported that over 2005–2011 atmospheric burdens of CO2, CH4, and N2O increased, with 2011 levels of 390.5 parts per million (ppm), 1803.2 parts per billion (ppb) and 324.2 ppb, respectively. Increases of CO2 and N2O over 2005–2011 were comparable to those over 1996–2005, while CH4 resumed increasing in 2007, after remaining nearly constant over 1999–2006. A comprehensive process-based assessment of changes in CO2, CH4, and N2O is undertaken in Chapter 5.

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Levels were close to 1750 values during at least one prolonged interval during the Carboniferous and Permian (350–252 Ma). During the Triassic (251.9–201.3 Ma), atmospheric CO2 mixing ratios reached a maximum of between 2000–5000 ppm (200–220 Ma). During the PETM (56 Ma) CO2 rapidly rose from about 900 ppm to about 2000 ppm (Table 2.1; Schubert and Jahren, 2013; Gutjahr et al., 2017; Anagnostou et al., 2020) in 3–20 kyr (Zeebe et al., 2016; Gutjahr et al., 2017; Turner, 2018). Estimated multi-millennial rates of CO2 accumulation during this event range from 0.3–1.5 PgC yr–1 (Gingerich, 2019), at least 4–5 times lower than current centennial rates (Section 5.3.1.1). Based on boron and carbon isotope data, supported by other proxies (Hollis et al., 2019), atmospheric CO2 during the EECO (50 Ma) was between 1150 and 2500 ppm (medium confidence), and then gradually declined over the last 50 Myr at a long-term rate of about 16 ppm Myr–1 (Figure 2.3). The last time the CO2 mixing ratio was as high as 1000 ppm (the level reached by some high emissions scenarios by 2100; Annex III) was prior to the Eocene-Oligocene transition (33.5 Ma; Figure 2.3) that was associated with the first major advance of the AIS (Pearson et al., 2009; Pagani et al., 2011; Anagnostou et al., 2016; Witkowski et al., 2018; Hollis et al., 2019). The compilation of Foster et al. (2017) constrained CO2 concentration to between 290 and 450 ppm during the MPWP, based primarily on the boron-isotope data reported by Martínez-Botí et al. (2015b), consistent with the AR5 range of 300–450 ppm. A more recent high-resolution boron isotope-based study revealed that CO2 cycled during the MPWP from about 330 to about 390 ppm on orbital timescales, with a mean of about 370 ppm (de la Vega et al., 2020). Although data from other proxy types (e.g., stomatal density orδ13C of alkenones) have too low resolution to resolve the orbital-related variability of CO2 during this interval (e.g., Kürschner et al., 1996; Stoll et al., 2019) there is general agreement among the different proxy types with the boron-derived mean (e.g., Stoll et al., 2019). High-resolution sampling (about 1 sample per 3 kyr) with the boron-isotope proxy indicates mean CO2 mixing ratios for the Marine Isotope Stage KM5c interglacial were 360–420 ppm (medium confidence) (de la Vega et al., 2020).

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Records of CO2 from the AIS formed during the last glacial period and the LDT show century-scale fluctuations of up to 9.6 ppm (Ahn et al., 2012; Ahn and Brook, 2014; Marcott et al., 2014; Bauska et al., 2015; Rubino et al., 2019). Although these rates are an order of magnitude lower than those directly observed over 1919–2019 CE (Section 2.2.3.3.1), they provide information on non-linear responses of climate-biogeochemical feedbacks (Section 5.1.2). Multiple records for 0–1850 CE show CO2 mixing ratios of 274–285 ppm. Offsets among ice core records are about 1%, but the long-term trends agree well and show coherent multi-centennial variations of about 10 ppm (Ahn et al., 2012; Bauska et al., 2015; Rubino et al., 2019). Multiple records show CO2 concentrations of 278.3 ± 2.9 ppm in 1750 and 285.5 ± 2.1 ppm in 1850 (Siegenthaler et al., 2005; MacFarling Meure et al., 2006; Ahn et al., 2012; Bauska et al., 2015). CO2 concentration increased by 5.0 ± 0.8 ppm during 970–1130 CE, followed by a decrease of 4.6 ± 1.7 ppm during 1580–1700 CE. The greatest rate of change over the CE prior to 1750 is observed at about 1600 CE, and ranges from –6.9 to +4.7 ppm per century in multiple high-resolution ice core records (Siegenthaler et al., 2005; MacFarling Meure et al., 2006; Ahn et al., 2012; Bauska et al., 2015; Rubino et al., 2019). Although ice core records present low-pass filtered time series due to gas diffusion and gradual bubble close-off in the snow layer over the ice sheet (Fourteau et al., 2020), the rate of increase since 1850 CE (about 125 ppm increase over about 170 years) is far greater than implied for any 170-year period by ice core records that cover the last 800 ka (very high confidence).

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CH4 concentrations over the past 110 kyr are higher in the Northern Hemisphere (NH) than in the Southern Hemisphere (SH), but closely correlated on centennial and millennial timescales (Buizert et al., 2015). On glacial to interglacial cycles, approximately 450 ppb oscillations in CH4 concentrations have occurred (Loulergue et al., 2008). On millennial timescales, most rapid climate changes observed in Greenland and other regions are coincident with rapid CH4 changes (Buizert et al., 2015; Rhodes et al., 2015, 2017). The variability of CH4 on centennial timescales during the early Holocene does not significantly differ from that of the late Holocene prior to about 1850 (Rhodes et al., 2013; Yang et al., 2017). The LGM concentration was 390.5 ± 6.0 ppb (Kageyama et al., 2017). The global mean concentrations during 0–1850 CE varied between 625 and 807 ppb. High-resolution ice core records from Antarctica and Greenland exhibit the same trends with an inter-polar difference of 36–47 ppb (Sapart et al., 2012; L. Mitchell et al., 2013). There is a long-term positive trend of about 0.5 ppb per decade during the CE until 1750 CE. The most rapid CH4 changes prior to industrialization were as large as 30–50 ppb on multi-decadal timescales. Global mean CH4 concentrations estimated from Antarctic and Greenland ice cores are 729.2 ± 9.4 ppb in 1750 and 807.6 ± 13.8 ppb in 1850 (L. Mitchell et al., 2013).

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New records show that N2O concentration changes are associated with glacial-interglacial transitions (Schilt et al., 2014). The most rapid change during the last glacial termination is a 30 ppb increase in a 200-year period, which is an order of magnitude smaller than the modern rate (Section 2.2.3.3). During the LGM, N2O was 208.5 ± 7.7 ppb (Kageyama et al., 2017). Over the Holocene the lowest value was 257 ± 6.6 ppb during 6–8 ka, but millennial variation is not clearly detectable due to analytical uncertainty and insufficient ice core quality (Flückiger et al., 2002; Schilt et al., 2010). Recently acquired high-resolution records from Greenland and Antarctica for the last 2 kyr consistently show multi-centennial variations of about 5–10 ppb (Figure 2.4), although the magnitudes vary over time (Ryu et al., 2020). Three high temporal resolution records exhibit a short-term minimum at about 600 CE of 261 ± 4 ppb (MacFarling Meure et al., 2006; Ryu et al., 2020). It is very likely that industrial N2O increase started before 1900 CE (Machida et al., 1995; Sowers, 2001; MacFarling Meure et al., 2006; Ryu et al., 2020). Multiple ice cores show N2O concentrations of 270.1 ± 6.0 ppb in 1750 and 272.1 ± 5.7 ppb in 1850 (Machida et al., 1995; Flückiger et al., 1999; Sowers, 2001; Rubino et al., 2019; Ryu et al., 2020).

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In summary, CO2 has fluctuated by at least 2000 ppm over the last 450 Myr (medium confidence). The last time CO2 concentrations were similar to the present-day was over 2 Ma (high confidence). Further, it is certain that WMGHG mixing ratios prior to industrialization were lower than present-day levels and the growth rates of the WMGHGs from 1850 are unprecedented on centennial timescales in at least the last 800 kyr. During the glacial-interglacial climate cycles over the last 800 kyr, the concentration variations of the WMGHG were 50–100 ppm for CO2, 210–430 ppb for CH4 and 60–90 ppb for N2O. Between 1750–2019 mixing ratios increased by 131.6 ± 2.9 ppm (47%), 1137 ± 10 ppb (156%), and 62 ± 6 ppb (23%), for CO2, CH4, and N2O, respectively (very high confidence). Since 2011 (AR5) mixing ratios of CO2, CH4, and N2O have further increased by 19 ppm, 63 ppb, and 7.7 ppb, reaching in 2019 levels of 409.9 (± 0.4) ppm, 1866.3 (± 3.3) ppb, and 332.1 (± 0.4) ppb, respectively. By 2019, the combined ERF (relative to 1750) of CO2, CH4 and N2O was 2.9 ± 0.5 W m–2 (Table 2.2; Section 7.3.2).

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Ice cores allow for estimation of multi-centennial trends in mid- and high-latitude aerosol deposition, including those for sulphate and black carbon (Figure 2.9a,b). Sulphate in ice cores increased by a factor of 8 from the end of the 19th century to the 1970s in continental Europe, by a factor of 4 from the 1940s to the 1970s in Russia, and by a factor of 3 from the end of the 19th century to 1950 in the Arctic (Svalbard). In all regions studied, concentrations have declined by about a factor of 2 following their peak (around 1970 in Europe and Russia, and 1950 in the Arctic). Strong increases of black carbon (BC) were observed in the 20th century over Europe, Russia, Greenland (primarily originating from emissions from North America), and in the Arctic (Svalbard). South America exhibits a small positive trend (Figure 2.9). BC concentrations in various Antarctic ice cores were below 1 ng g–1 without a clear trend.

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New GMST reconstructions for the LGM fall near the middle of AR5’s very likely range, which was based on a combination of proxy reconstructions and model simulations. Two of these new reconstructions use marine proxies to reconstruct global SST that were scaled to GMST based on different assumptions. One indicates that GMST was 6.2 [4.5 to 8.1] °C cooler than the late Holocene average (Snyder, 2016), and the other, 5.7°C ± 0.8°C (2 SD) cooler than the average of the first part of the Holocene (10–5 ka) (Friedrich and Timmermann, 2020). A third new estimate (Tierney et al., 2020) uses a much larger compilation of marine proxies along with a data-assimilation procedure, rather than scaling, to reconstruct a GMST of 6.1°C ± 0.4°C (2 SD) cooler than the late Holocene. Assuming that the 1850–1900 reference period was 0.2°C and 0.4°C cooler than the late and first part of the Holocene, respectively (Kaufman et al., 2020a), the midpoints of these three new GMST reconstructions average –5.8°C relative to 1850–1900. The coldest multi-century period of the LGM in the J. Hansen et al. (2013) reconstruction is 4.3°C colder than 1850–1900. This compares to land- and SST-only estimates of about –6.1°C ± 2°C and –2.2°C ± 1°C, respectively (2 SD), which are based on AR5-generation studies that imply a warmer GMST than more recent reconstructions (Figure 1c in Harrison et al., 2015; Figure 7 in Harrison et al., 2016). A major new pollen-based data-assimilation reconstruction averages 6.9°C cooler over northern extratropical land (Cleator et al., 2020). LGM temperature variability on centennial scales was about four times higher globally than during the Holocene, and even greater at high latitudes (Rehfeld et al., 2018). In summary, GMST is estimated to have been 5°C–7°C lower during the LGM (around 23–19 ka) compared with 1850–1900 (medium confidence).

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Taking all lines of evidence into account, the GMST averaged over the warmest centuries of the current interglacial period (sometime between around 6 and 7 ka) is estimated to have been 0.2°C–1.0°C higher than 1850–1900 (medium confidence). It is therefore more likely than not that no multi-centennial interval during the post-glacial period was warmer globally than the most recent decade (which was 1.1°C warmer than 1850–1900; Section 2.3.1.1.3); the LIG (129–116 ka) is the next most recent candidate for a period of higher global temperature. Zonally averaged mean annual temperature reconstructions (Routson et al., 2019) indicate that MH warmth was most pronounced north of 30°N latitude, and that GMST subsequently decreased in general, albeit with multi-century variability, with greater cooling in the NH than in the SH (Kaufman et al., 2020a).

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The temperature history of the last millennium and the methods used to reconstruct it have been studied extensively, both prior to and following AR5, as summarized recently by Smerdon and Pollack (2016) and Christiansen and Ljungqvist (2017). New regional (e.g., Shi et al., 2015; Stenni et al., 2017; Werner et al., 2018), global ocean (McGregor et al., 2015), quasi-hemispheric (Neukom et al., 2014; Schneider et al., 2015; Anchukaitis et al., 2017), and global (Tardif et al., 2019) temperature reconstructions, and new regional proxy data syntheses (Lüning et al., 2019a, b) have been published, extending back 1–2 kyr. In addition, a major new global compilation of multiproxy, annually resolved paleo-temperature records for the CE (PAGES 2k Consortium, 2017) has been analysed using a variety of statistical methods for reconstructing temperature (PAGES 2k Consortium, 2019). The median of the multi-method GMST reconstruction from this synthesis (Figure 2.11a) generally agrees with the AR5 assessment, while affording more robust estimates of the following major features of GMST during the CE: (i) an overall millennial-scale cooling trend of –0.18 [–0.28 to 0.00] °C kyr–1 prior to 1850; (ii) a multi-centennial period of relatively low temperature beginning around the 15th century, with GMST averaging –0.03 [–0.30 to 0.06] °C between 1450 and 1850 relative to 1850–1900; (iii) the warmest multi-decadal period occurring most recently; and (iv) the rate of warming during the second half of the 20th century (from instrumental data) exceeding the 99th percentile of all 51-year trends over the past 2 kyr. Moreover, the new proxy data compilation shows that the warming of the 20th century was more spatially uniform than any other century-scale temperature change of the CE (medium confidence) (Neukom et al., 2019). A new independent temperature reconstruction extending back to 1580 is based on an expanded database of subsurface borehole temperature profiles, along with refined methods for inverse modelling (Cuesta-Valero et al., 2021). The borehole data, converted to GMST based on the modelled relation between changes in land versus sea surface temperature outlined previously, indicate that average GMST for 1600–1650 was 0.12°C colder than 1850–1900, which is similar to the PAGES 2k reconstruction (0.09°C colder), although both estimates are associated with relatively large uncertainties (0.8°C (95% range) and 0.5°C (90% range), respectively).

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New analyses suggest that during the Holocene, the NH mid-latitudes became increasingly wet, in phase with the strength of the latitudinal temperature and insolation gradients (Shuman and Marsicek, 2016; Routson et al., 2019). Nevertheless, there was also considerable spatial heterogeneity and variability on centennial to millennial timescales (Newby et al., 2014; Shuman and Marsicek, 2016; H. Zhang et al., 2018; Liefert and Shuman, 2020). The NH tropics and many regions of the SH deep tropics experienced wetting up until the early to mid-Holocene but drying thereafter (Shanahan et al., 2015; Nash et al., 2016; Muñoz et al., 2017; Quade et al., 2018). Evidence for the SH is limited, with a wetting trend during the Holocene in low latitudes of South America (Kanner et al., 2013; Mollier-Vogel et al., 2013) and parts of the African tropics (Schefuß et al., 2011; Chevalier and Chase, 2015) but a drying tendency over southern Australia and New Zealand (van den Bos et al., 2018; Barr et al., 2019) and South America (Quade and Kaplan, 2017; Moreno et al., 2018).

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Records prior to the instigation of quasi-global coverage by radiosondes require the use of statistical relationships to infer TCWV from historical SST observations or the evaluation of centennial-scale reanalysis products (Smith and Arkin, 2015). These approaches reveal two periods of positive trends, one from 1910 to 1940 and the other from 1975 onwards (Zhang et al., 2013; Mieruch et al., 2014; Shi et al., 2018), concurrent with periods of positive SST trends (Figure 2.11). Potential sources of errors in the SST-based estimation of TCWV include both uncertainties in historical SST and uncertainties in the parameters that define the relationship between the variables (Smith and Arkin, 2015). Trends based on 20CRv2c, ERA-20C and ERA-20CM indicate an increase in TCWV over much of the global ocean since the beginning of the 20th century, particularly over the tropics (Bordi et al., 2015; Smith and Arkin, 2015; Poli et al., 2016). TCWV trends estimated since the middle of the 20th century from radiosonde observations show significant increases over North America and large portions of Eurasia, while decreases are restricted to Australia, eastern Asia and the Mediterranean region (Y. Zhang et al., 2018). Overall, there is a significant increase in TCWV over global land areas since 1979 (Chen and Liu, 2016).

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In summary, positive trends in global total column water vapour are very likely since 1979 when globally representative direct observations began, although uncertainties associated with changes in the observing system imply medium confidence in estimation of the trend magnitudes. Low confidence in longer-term trends arises from uncertainties in the SST-TCWV relationship and current centennial scale reanalyses, particularly during the first half of the 20th century.

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From centennial-scale reanalyses, Liu et al. (2012) and D’Agostino and Lionello (2017) found divergent results on HC extent over the last 150 years, although with unanimity upon an intensification of the SH HC. A substantial discrepancy between HC characteristics in centennial-scale reanalyses and in ERA-Interim (D’Agostino and Lionello, 2017) since 1979 yields significant questions regarding their ability to capture changes in HC behaviour. Taken together with the existence of apparent non-climatic artefacts in the datasets (Nguyen et al., 2015), this implies low confidence in changes in the extent and intensity of HC derived from centennial-scale reanalyses. However, using multiple observational datasets and centennial-scale reanalyses, Bronnimann et al. (2015) identified a southward shift in the NH HC edge from 1945 to 1980 of about 0.25° latitude per decade, consistent with observed changes in global land monsoon precipitation (Section 2.3.1.4.2).

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In summary, observed trends during the last century indicate that the GM precipitation decline reported in AR5 has reversed since the 1980s, with a likely increase mainly due to a significant positive trend in the NH summer monsoon precipitation (medium confidence). However, GM precipitation has exhibited large multi-decadal variability over the last century, creating low confidence in the existence of centennial-length trends in the instrumental record. Proxy reconstructions show a likely NH monsoons weakening since the mid-Holocene, with opposite behaviour for the SH monsoons.

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The modern era reanalyses exhibit SLP increases over the SH subtropics with stronger increases in austral winter over 1979–2018. Over the NH, SLP increased over the mid-latitude Pacific in boreal winter and decreased over the eastern subtropical and mid-latitude North Atlantic in boreal summer. Discrepancies in the low-frequency variations during the first half of the 20th century exist in the centennial-scale reanalysis products (Befort et al., 2016). Overall, modern reanalysis datasets support the AR5 conclusion that there is no clear signal for trends in the strength and position of the permanent and quasi-permanent pressure centres of action since the 1950s. Instead, they highlight multi-decadal variations. Large-scale SLP is strongly associated with the changes in modes of variability (Section 2.4 and Annex IV).

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Sudden stratospheric warming (SSW), a phenomenon of rapid stratospheric air temperature increases (sometimes by more than 50°C in 1–2 days), is tightly associated with the reversal of upper stratospheric zonal winds, and a resulting collapse or substantial weakening of the stratospheric polar vortex (Butler et al., 2015; Butler and Gerber, 2018) and on average occurs approximately 6 times per decade in the NH winter (Charlton et al., 2007; Butler et al., 2015). The SSW record from all modern reanalyses is very consistent. There is a higher occurrence of major midwinter SSWs in the 1980s and 2000s with no SSW events during 1990–1997 (Reichler et al., 2012; Butler et al., 2015). An assessment of multi-decadal variability and change in SSW events is sensitive to both chosen metric and methods (Palmeiro et al., 2015). Due to the lack of assimilation of upper air data, the centennial-scale reanalyses do not capture SSW events, even for the most recent decades (Butler et al., 2015, 2017) and hence cannot inform on earlier behaviour. There has been considerably less study of trends in the SH stratosphere polar vortex strength despite the interest in the ozone hole and the potential impact of the SH stratosphere polar vortex strength on it. The occurrence of SSW events in the SH is not as frequent as in the NH, with only 3 documented events in the last 40 years (Shen et al., 2020).

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For the instrumental era, since AR5 and SROCC, new and updated OHC and ThSL observation-based analyses (Johnson et al., 2020; von Schuckmann et al., 2020) enhance an existing large ensemble of direct and indirect OHC estimates (Figure 2.26), although some rely to varying degrees upon information from ocean-climate models. Direct estimates benefit from improved: bias adjustments (e.g., Cheng et al., 2018; Leahy et al., 2018; Palmer et al., 2018; Ribeiro et al., 2018; B. Wang et al., 2018; Bagnell and DeVries, 2020; Gouretski and Cheng, 2020); interpolation methods (Kuusela and Stein, 2018; Su et al., 2020); and characterization of sources of uncertainty (e.g., Good, 2017; Wunsch, 2018; Allison et al., 2019; Garry et al., 2019; Meyssignac et al., 2019; Palmer et al., 2021), including those originating from forced and intrinsic ocean variability (Penduff et al., 2018). After 2006 direct OHC estimates for the upper 2000 m layer benefit from the near-global ARGO array with its superior coverage over 60°S–60°N (Roemmich et al., 2019). Indirect estimates include OHC and ThSL series inferred from satellite altimetry and gravimetry since 2003 (Meyssignac et al., 2019), the passive uptake of OHC (ThSL) at centennial timescales inferred from observed SST anomalies, and time-invariant circulation processes from an ocean state estimation (e.g., Zanna et al., 2019). Resplandy et al. (2019) estimate the rate of global OHC uptake over 1991–2016 from changes in atmospheric composition and physical relationships based on CMIP5 model simulations. The uncertainties are broader than from direct estimates but the estimate is qualitatively consistent.

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In summary, current multi-decadal to centennial rates of OHC gain are greater than at any point since the last deglaciation (medium confidence). At multi-centennial timescales, changes in OHC based upon proxy indicators demonstrate a tight link with surface temperature changes during the last deglaciation (high confidence), as well as during the Holocene and CE (low confidence). It is likely the global ocean has warmed since 1871, consistent with the observed increase in sea surface temperature. It is virtually certain that OHC increased between 1971 and 2018 in the upper 700 m and very likely in the 700–2000 m layer, with high confidence since 2006. It is likely the OHC below 2000 m has increased since 1992. Confidence in the assessment of multi-decadal OHC increase is further strengthened by consistent closure of both global sea level and energy budgets (Section 7.2.2.2, Box 7.2, Cross-Chapter Box 9.1).

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For the last 3 kyr, GMSL has been estimated from global databases of sea-level proxies, including numerous densely-sampled high-resolution salt-marsh records with decimetre scale vertical resolution and sub-centennial temporal resolution (Kopp et al., 2016; Kemp et al., 2018). Over the last about 1.5 kyr, the most prominent century-scale GMSL trends include average maximum rates of lowering and rising of –0.7 ± 0.5 mm yr–1 (2 SD) over 1020–1120 CE, and 0.3 ± 0.5 (2 SD) over 1460–1560, respectively. Between 1000 and 1750 CE, GMSL is estimated to have been within the range of about –0.11 to +0.09 m relative to 1900 (Kemp et al., 2018). This was followed by a sustained increase of GMSL that began between 1820 and 1860 and has continued to the present day. New analyses demonstrate that it is very likely that GMSL rise over the 20th century was faster than over any preceding century in at least 3 kyr (Kopp et al., 2016; Kemp et al., 2018) (Figure 2.28).

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In summary, positive trends for the NAM/NAO winter indices were observed between the 1960s and the early 1990s, but these indices have become less positive or even negative thereafter (high confidence). The NAO variability in the instrumental record was very likely not unusual in the millennial and multi-centennial context.

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Several studies have attempted to reconstruct the evolution of the SAM during the Holocene using proxies of the position and strength of the SH zonal winds, although with no clear consensus regarding the timing and phase of the SAM (Hernández et al., 2020). The early Holocene was dominated by SAM positive phases (Moreno et al., 2018; Reynhout et al., 2019), consistent with increasing westerly wind strength (Lamy et al., 2010), with some reconstructions showing significant centennial and millennial variability but no consistent trend after 5 ka (Hernández et al., 2020). For the CE, enhanced westerly winds occurred over 0–1000 CE, as reflected in increased burning activity in Patagonia (Turney et al., 2016a) and tree ring records from southern New Zealand (Turney et al., 2016b) imply a predominantly positive SAM phase. Pollen records and lake sediments from Tasmania, southern mainland Australia, New Zealand and southern South America, inferred the period of 1000 to 1400 CE to be characterized by anomalously dry conditions south of 40°S, implying a positive SAM (Moreno et al., 2014; Fletcher et al., 2018; Evans et al., 2019; Matley et al., 2020). Nevertheless, proxy reconstructions of the SAM based on temperature-sensitive records from tree rings, ice cores, lake sediments and corals spanning the mid-to-polar latitudes show alternating positive and negative phases (Figure 2.35).

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Before the mid-1950s, SAM indices derived from station-based datasets, and centennial reanalyses show pronounced interannual and decadal variability but no significant trends, with low correlation between SAM indices due to the diversity across different datasets and sensitivity to the definition used for the index calculation (Barrucand et al., 2018; Schneider and Fogt, 2018; J. Lee et al., 2019). Various SAM indices exhibit significant positive trends since the 1950s, particularly during austral summer and autumn (Barrucand et al., 2018; Schneider and Fogt, 2018; J. Lee et al., 2019), unprecedented for austral summer over the last 150 years (J.M. Jones et al., 2016; Fogt and Marshall, 2020). This indicates a strengthening of the surface westerly winds around Antarctica, related to both the position and intensity of the subpolar jet in the SH (Section 2.3.1.4.3; Ivy et al., 2017; IPCC, 2019). The SAM trends have slightly weakened after about 2000 (Fogt and Marshall, 2020).

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Numerous studies (J. Li et al., 2013; S. McGregor et al., 2013; Rustic et al., 2015; Hope et al., 2017; Y. Liu et al., 2017) find substantial variability in ENSO activity on multi-decadal to centennial timescales over the last 500 to 1 kyr (Figure 2.36). Different proxies show a wide spread in the specific timing and magnitude of events in the pre-instrumental period (e.g., Dätwyler et al., 2019). Most investigators find that ENSO activity in recent decades was higher than the most recent centuries prior to the instrumental period. Grothe et al. (2019) also found that ENSO variance of the last 50 years was 25% higher than the average of the last millennium, and was substantially higher than the average of the mid- to late-Holocene. S. McGregor et al., (2010, 2013) looked for common variance changes in pre-existing ENSO proxies, finding stronger ENSO variance for the 30-year period 1979–2009 compared to any 30-year period within the timespan 1590–1880 CE. This finding also holds when adding more recently developed ENSO proxies (Figure 2.36). Koutavas and Joanides (2012), Ledru et al. (2013) and Thompson et al. (2017) identify various periods within the range 1000 BCE to 1300 CE when ENSO activity was greater than in the following centuries, and more closely comparable to the mid-20th century onwards behaviour.

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Prior to the 1950s, SST observations in the tropical Pacific were much sparser and hence uncertainties in Niño indices are much larger (B. Huang et al., 2020). SOI data and some newer SST-based studies show high ENSO amplitude, comparable to the post-1950 period, in the period from the mid-late 19th century to about 1910, but proxy indicators generally indicate that the late 19th and early 20th century were less active than the late 20th century (Figure 2.36). Yu and Kim’s (2013) implementation of the ONI found a number of events with the ONI above 1.5°C between 1888 and 1905, then no such events until 1972, whilst the SOI indicates comparable or stronger events to the three strongest post-1950 events in 1896 and 1905. Giese and Ray (2011) also found a number of such events between 1890 and 1920 in the SODA ocean reanalysis, corroborated further by B. Huang et al. (2020) and Vaccaro et al. (2021), who found that the strength of the 1877–1878 event was comparable with that of the 1982–1983, 1997–1998 and 2015–2016 events. There have also been a number of strong La Niña events (e.g., 1973–1974, 1975–1976 and 2010–2011), with few clear analogues in the 1920–1970 period; the proxy-based analysis of McGregor et al. (2010) indicates that the mid-1970s La Niña period was also extreme in a multi-centennial context. There is no indication that the frequency of high-amplitude events since the 1970s reflects a long-term trend which can be separated from multi-decadal variability, given apparent presence of several high-amplitude events in the late 19th and early 20th centuries, and the relatively large uncertainty in pre-1950 SST data in the tropical Pacific region.

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There is no robust indicationof any changes in ENSO teleconnections over multi-centennial timescales (Hernández et al., 2020) despite multi-decadal variability. Shi and Wang (2018) found that teleconnections with the broader Asian summer monsoon, including the Indian and the East Asian monsoon, were generally stable since the 17th century during the developing phase of the monsoon, and showed substantial decadal variability, but no clear trend, during the decaying phase. They also found that the weakening of teleconnections between the Indian summer monsoon and ENSO in recent decades had numerous precedents over the last few centuries. Räsänen et al. (2016) also found substantial decadal variability, but little trend, in the strength of the relationship between ENSO and monsoon precipitation in South East Asia between 1650 and 2000. Dätwyler et al. (2019) identified a number of multi-decadal periods with apparently changed teleconnections at times over the last 400 years.

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The existence of the PDV in the centuries prior to the instrumental period is evidenced by a variety of proxy records based on tree rings (Biondi et al., 2001; D’Arrigo and Ummenhofer, 2015), corals (Felis et al., 2010; Deng et al., 2013; Linsley et al., 2015) and sediments (Lapointe et al., 2017; O’Mara et al., 2019). There is little coherence between the various paleo-proxy indices prior to the instrumental record, and neither these nor the instrumental records provide indications of a clearly defined spectral peak (Chen and Wallace, 2015; M. Newman et al., 2016; Henley, 2017; L. Zhang et al., 2018; Buckley et al., 2019). For instance, spectral analysis from millennia length PDV reconstructions shows spectral peaks at multi-decadal, centennial and bi-centennial time scales (Beaufort and Grelaud, 2017), while only multi-decadal oscillations can be detected in the shorter (less than 400 years into the past) paleoclimate reconstructions. A variety of proxies suggest a shift in the PDV from the early-mid Holocene, which was characterized by a persistently negative phase of the PDO (i.e., weak Aleutian Low), to the late Holocene, and more variable and more positive PDO (i.e., strong Aleutian Low) conditions. This shift at around 4.5 ka is also evident in the PDO periodicities, changing from bidecadal and pentadecadal variability in the early Holocene to only pentadecadal periodicities in the late Holocene (Hernández et al., 2020). Several proxy records indicate that the strengthening in the Aleutian Low inferred since the late 17th century is unprecedented over the last millennium (Z. Liu et al., 2017; Osterberg et al., 2017; Winski et al., 2017), in line with an increase in PDV low-frequency variability (Williams et al., 2017; Hernández et al., 2020).

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Ayache, M., D. Swingedouw, Y. Mary, F. Eynaud, and C. Colin, 2018: Multi-centennial variability of the AMOC over the Holocene: A new reconstruction based on multiple proxy-derived SST records. Global and Planetary Change, 170, 172–189, doi: 10.1016/j.gloplacha.2018.08.016.

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D’Agostino, R. and P. Lionello, 2017: Evidence of global warming impact on the evolution of the Hadley Circulation in ECMWF centennial reanalyses. Climate Dynamics, 48(9–10), 3047–3060, doi: 10.1007/s00382-016-3250-0.

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Feng, M., X. Zhang, B. Sloyan, and M. Chamberlain, 2017: Contribution of the deep ocean to the centennial changes of the Indonesian Throughflow. Geophysical Research Letters, 44(6), 2859–2867, doi: 10.1002/2017gl072577.

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Feng, M., N. Zhang, Q. Liu, and S. Wijffels, 2018: The Indonesian throughflow, its variability and centennial change. Geoscience Letters, 5(1), 3, doi: 10.1186/s40562-018-0102-2.

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Fletcher, M.-S. et al., 2018: Centennial-scale trends in the Southern Annular Mode revealed by hemisphere-wide fire and hydroclimatic trends over the past 2400 years. Geology, 46(4), 363–366, doi: 10.1130/g39661.1.

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Hirahara, S., M. Ishii, and Y. Fukuda, 2014: Centennial-Scale Sea Surface Temperature Analysis and Its Uncertainty. Journal of Climate, 27(1), 57–75, doi: 10.1175/jcli-d-12-00837.1.

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Khan, S.A. et al., 2020: Centennial response of Greenland’s three largest outlet glaciers. Nature Communications, 11(1), 5718, doi: 10.1038/s41467-020-19580-5.

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Kobashi, T. et al., 2017: Volcanic influence on centennial to millennial Holocene Greenland temperature change. Scientific Reports, 7(1), 1441, 361–368, doi: 10.1038/s41598-017-01451-7.

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Koffman, B.G. et al., 2014: Centennial-scale variability of the Southern Hemisphere westerly wind belt in the eastern Pacific over the past two millennia. Climate of the Past, 10(3), 112–125, doi: 10.5194/cp-10-1125-2014.

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Marcott, S.A. et al., 2014: Centennial-scale changes in the global carbon cycle during the last deglaciation. Nature, 514(7524), 616–619, doi: 10.1038/nature13799.

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Moreno, P.I. et al., 2014: Southern Annular Mode-like changes in southwestern Patagonia at centennial timescales over the last three millennia. Nature Communications, 5(1), 4375, doi: 10.1038/ncomms5375.

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Moreno, P.I. et al., 2018: Onset and evolution of southern annular mode-like changes at centennial timescale. Scientific Reports, 8(1), 3458, doi: 10.1038/s41598-018-21836-6.

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Newby, P.E., B.N. Shuman, J.P. Donnelly, K.B. Karnauskas, and J. Marsicek, 2014: Centennial-to-millennial hydrologic trends and variability along the North Atlantic Coast, USA, during the Holocene. Geophysical Research Letters, 41, 4300–4307, doi: 10.1002/2014gl060183.

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Rae, J.W.B. et al., 2018: CO2 storage and release in the deep Southern Ocean on millennial to centennial timescales. Nature, 562(7728), 569–573, doi: 10.1038/s41586-018-0614-0.

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Rhodes, R.H. et al., 2017: Atmospheric methane variability: Centennial-scale signals in the Last Glacial Period. Global Biogeochemical Cycles, 31(3), 575–590, doi: 10.1002/2016gb005570.

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Shuman, B.N. et al., 2018: Placing the Common Era in a Holocene context: millennial to centennial patterns and trends in the hydroclimate of North America over the past 2000 years. Climate of the Past, 14(5), 665–686, doi: 10.5194/cp-14-665-2018.

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Steinman, B.A. et al., 2014: Ocean-atmosphere forcing of centennial hydroclimate variability in the Pacific Northwest. Geophysical Research Letters, 41(7), 2553–2560, doi: 10.1002/2014gl059499.

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Interest in internal variability since the publication of AR5 stems in part from its importance in understanding the slower global surface temperature warming over the early 21st century (see Cross-Chapter Box 3.1). Evidence coming mostly from paleo studies is mixed on whether CMIP5 models underestimate decadal and multi-decadal variability in global mean temperature. Schurer et al. (2013) found good agreement between internal variability derived from paleo reconstructions, estimated as the fraction of variance that is not explained by forced responses, and modelled variability, although the subset of CMIP5 models they used may have been associated with larger variability than the full CMIP5 ensemble. PAGES 2k Consortium (2019) found that the largest 51-year trends in both reconstructions of global mean temperature and fully forced climate simulations over the period 850 to 1850 were almost identical. Zhu et al. (2019) showed agreement in the modelled and reconstructed temporal spectrum of global surface temperatures on annual to multi-millennial time scales. However, they suggest that decadal- to centennial variability is partly forced by slow orbital changes that predate the last millennium. This is consistent with Gebbie and Huybers (2019), who showed that the deep ocean has been out of equilibrium over that period. Laepple and Huybers (2014) found good agreement between modelled and proxy-derived decadal ocean temperature variability, but underestimates of variance by models by at least a factor of ten at centennial time scales because models underestimate the difference between the warm and cold periods of the last millennium. Parsons et al. (2020) found that some CMIP6 models exhibit much higher multi-decadal variability in GSAT than CMIP5 models, with indications that variability in these models is also higher than that from proxy reconstructions. CMIP6 models may not share the underestimation by CMIP5 models of variability in decadal to multi-decadal modes of variability, such as Pacific Decadal Variability (Section 3.7.6; England et al., 2014; Thompson et al., 2014; Schurer et al., 2015) and Atlantic Multi-decadal Variability (AMV), which may be partly forced, (see Section 3.7.7) but this assessment is limited by the small number of available studies. For the Southern Hemisphere, Hegerl et al. (2018) found an instance of internal variability in the early 20th century larger than that modelled, but indicated that could be an observational issue. Friedman et al. (2020) found biases in interhemispheric SST contrast in some models that may be consistent with underestimated cooling after early-20th century eruptions or underestimated Pacific Decadal Variability, but could also be due to an imperfect separation between internal variability and forced signal in the observations. Figure 3.2c, updated from PAGES 2k Consortium (2019), compares modelled temperatures to reconstructions over the last millennium. It indicates that models reproduce the observed variability well, at least for the time scales between 20 and 50 years that paleo reconstructions typically resolve and that the figure represents. In summary, decadal GMST variability simulated in CMIP6 models spans the range of residual decadal variability in large-scale reconstructions (medium evidence, low agreement).

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While some recent studies find that internal decadal GSAT variability may become weaker under GSAT warming, associated in part with reduced amplitude PDV (Section 4.5.3.5; Brown et al., 2017), the weakening is small under a realistic range of warming. A large volcanic eruption would temporarily cool GSAT (Cross-Chapter Box 4.1). Thus, there is very high confidence that reduced and increased GMST and GSAT trends at decadal time scales will continue to occur in the 21st century (Meehl et al., 2013; Roberts et al., 2015; Medhaug and Drange, 2016). However, such internal or volcanically forced decadal variations in GSAT trend have little effect on centennial warming (England et al., 2015; Cross-Chapter Box 4.1).

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Considering the bulk of evidence, it is extremely likely that human influence has contributed to observed near-surface and subsurface salinity changes across the globe since the mid-20th century. All available multi-decadal assessments have confirmed that the associated pattern of change corresponds to fresh regions becoming fresher and salty regions becoming saltier (high confidence). CMIP5 and CMIP6 models are only able to reproduce these patterns in simulations that include greenhouse gas increases (medium confidence). Changes to the coincident atmospheric water cycle and ocean-atmosphere fluxes (evaporation and precipitation) are the primary drivers of the basin-scale observed salinity changes (high confidence). This result is supported by all available observational assessments, along with a growing number of climate modelling studies targeted at assessing ocean and water cycle changes. The basin-scale changes are consistent across models and intensify on centennial scales from the historical period through to the projections of future climate (high confidence).

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The AR5 concluded that oxygen concentrations have decreased in the open ocean since 1960 and such decreases can be attributed in part to human influence with medium confidence. The decrease in ocean oxygen content in the upper 1000 m, between 1970 and 2010, is further confirmed in SROCC (medium confidence), with the oxygen minimum zone expanding in volume (see also Section 5.3.3.2). Observed oxygen declines over the last several decades (Stendardo and Gruber, 2012; Stramma et al., 2012; Schmidtko et al., 2017) match model estimates in the surface ocean (Oschlies et al., 2017) but are much larger than model derived estimates in the interior (Bopp et al., 2013; Cocco et al., 2013). Some of this difference has been interpreted as due to a lack of representation of coastal eutrophication in these models (Breitburg et al., 2018), but much of it remains unexplained. This disparity is particularly apparent in the eastern Pacific oxygen minimum zone, where some CMIP5 models showed increasing trends whereas observations show a strong decrease (Cabré et al., 2015). However, proxy reconstructions suggest that over the last century the ocean may have in fact undergone increases in oxygen in the most oxygen poor regions (Deutsch et al., 2014). As discussed in Section 5.3.1, ocean oxygen went through wide oscillations on multi-centennial time scales through the last deglaciation, with abrupt warming resulting in loss of oxygen in subsurface waters of the North Pacific (Praetorius et al., 2015). The global upper ocean oxygen inventory is negatively correlated with ocean heat content with a regression coefficient comparable to that found in ocean models (Ito et al., 2017). Variability and trends in the observed upper ocean oxygen concentration are mainly driven by the apparent oxygen utilization component with small contributions from oxygen solubility, suggesting that changing ocean circulation, mixing, and/or biochemical processes, rather than thermally induced solubility effects may be the main drivers of observed deoxygenation. The spatial distribution of the ocean deoxygenation in the interior of the ocean as well as over coastal areas is further assessed in Section 5.3.

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Despite new efforts since AR5 to reconstruct the NAO beyond the instrumental record, it is still very challenging to assess the role of external forcings in the apparent multi-decadal to centennial variability present throughout the last millennium. Large uncertainties remain in the reconstructed NAO index that are sensitive to the types of proxies and statistical methods (Trouet et al., 2012; Ortega et al., 2015; Anchukaitis et al., 2019; Cook et al., 2019; Hernández et al., 2020; Michel et al., 2020) and reconstructed NAO variations are often not reproduced using pseudo-proxy approaches in models (Lehner et al., 2012; Landrum et al., 2013). At low frequency, it remains challenging to evaluate if the observed or reconstructed signal corresponds to an actual change in the NAO intraseasonal to interannual intrinsic properties or rather to a change in the mean background atmospheric circulation changes projecting on a specific phase of the mode. Consequently, conflicting results emerge in the attribution of reconstructed long-term variations in the NAO to solar forcing, whose influence thus remains controversial (Gómez-Navarro and Zorita, 2013; Moffa-Sánchez et al., 2014; Ortega et al., 2015; Ait Brahim et al., 2018; Sjolte et al., 2018; Xu et al., 2018). Influences from major volcanic eruptions appear to be more robust (Ortega et al., 2015; Swingedouw et al., 2017) even if some modelling experiments question the amplitude of the response, which mostly projects on the positive phase of the NAM/NAO (Bittner et al., 2016). The forced response is dependent on the strength, seasonal timing and location of the eruption but may also depend on the mean climate background state (Zanchettin et al., 2013) and/or the phases of the main modes of decadal variability such as the AMV (Section 3.7.7; Ménégoz et al., 2018).

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On longer time scales, last millennium experiments from CMIP5 models fail to capture multicentennial variability evident in the reconstructions for the pre-industrial era (Abram et al., 2014; Dätwyler et al., 2018), which is also the case in those from available CMIP6 models (Figure 3.35). However, there is large uncertainty among reconstructions (Section 2.4.1.2). It is therefore unclear whether this disagreement reflects this observational uncertainty, whether forcings such as variations in the imposed insolation may be too weak, whether models are insufficiently sensitive to such variations, or whether internal variability including that associated with tropical Pacific variability is under-represented (Abram et al., 2014). The explanation could be a combination of all these factors. However, despite the aforementioned limitations of the reconstructions, Section 2.4.1.2 assesses that the recent positive trend in the SAM is likely unprecedented in at least the past millennium (medium confidence). CMIP5 and CMIP6 last-millennium simulations only capture the present anomalous state during the final decades of the simulations which are dominated by human influence; this state is also outside the range of simulated variability characteristic of pre-industrial times.

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Ait Brahim, Y. et al., 2018: Multi-decadal to centennial hydro-climate variability and linkage to solar forcing in the Western Mediterranean during the last 1000 years. Scientific Reports, 8(1), 17446, doi: 10.1038/s41598-018-35498-x.

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Deutsch, C. et al., 2014: Oceanography. Centennial changes in North Pacific anoxia linked to tropical trade winds. Science, 345(6197), 665–668, doi: 10.1126/science.1252332.

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Latif, M., T. Martin, and W. Park, 2013: Southern ocean sector centennial climate variability and recent decadal trends. Journal of Climate, 26(19), 7767–7782, doi: 10.1175/jcli-d-12-00281.1.

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Levang, S.J. and R.W. Schmitt, 2015: Centennial changes of the global water cycle in CMIP5 models. Journal of Climate, 28(16), 6489–6502, doi: 10.1175/jcli-d-15-0143.1.

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Ljungqvist, F.C. et al., 2019: Centennial-scale temperature change in last millennium simulations and proxy-based reconstructions. Journal of Climate, 32(9), 2441–2482, doi: 10.1175/jcli-d-18-0525.1.

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Roe, G.H., M.B. Baker, and F. Herla, 2017: Centennial glacier retreat as categorical evidence of regional climate change. Nature Geoscience, 10(2), 95–99, doi: 10.1038/ngeo2863.

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Since AR5, there has been growing progress in understanding the climate impacts of volcanic eruptions. Volcanic forcing is regarded as the dominant driver of forced variability in preindustrial surface air temperature (Schurer et al., 2013, 2014). Large eruptions in the tropics and high latitudes were primary drivers of interannual-to-decadal temperature variability in the Northern Hemisphere during the past 2,500 years, with cooling persisting for up to ten years after some of the largest eruptive episodes (Sigl et al., 2015). Repeated clusters of volcanic eruptions can induce a net negative radiative forcing that results in a centennial- and global-scale cooling trend via a decline in mixed-layer oceanic heat content (McGregor et al., 2015). The response to multi-decadal changes in volcanic forcing (representing clusters of eruptions) shows similar cooling in both simulations and reconstructions of NH temperature. Volcanic eruptions generally result in decreased global precipitation for up to a few years following the eruption (Iles and Hegerl, 2014, 2015; Man et al., 2014), with climatologically wet regions drying and climatologically dry regions wetting (medium confidence), which is opposite to the response under global warming (Held and Soden, 2006; Iles et al., 2013; Zuo et al., 2019a, b). El Niño-like warming appears after large volcanic eruptions, as seen in both observations (Adams et al., 2003; McGregor et al., 2010; Khodri et al., 2017) and climate model simulations (Ohba et al., 2013; Pausata et al., 2015; Colose et al., 2016; Stevenson et al., 2016; Khodri et al., 2017; Predybaylo et al., 2017; Zuo et al., 2018). The large tropical eruptions are coincident with positive Indian Ocean dipole events (Maher et al., 2015).

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Before the industrial period, explosive volcanic eruptions were the largest source of forced climate variability globally on interannual to centennial time scales (Section 2.2). While usually omitted from scenarios used for future climate projections, as they are unpredictable, volcanic eruptions have the potential to influence future climate on multi-annual to decadal time scales and affect many climatic impact drivers (as defined in Sections 12.1 and 12.3). Since AR5, more comprehensive paleo evidence and observations, as well as improved modelling have advanced understanding of the climate response to past volcanic eruptions. Building on multiple chapter assessments, this box synthesizes how volcanic eruptions affect climate and considers implications of possible future events.

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Although incomplete, proxy records show large impacts upon contemporary society from eruptions such as 1257 Samalas and 1815 Tambora, the latter resulting in ‘the year without a summer’ with multiple harvest failures across the Northern Hemisphere (e.g., Raible et al., 2016). Comparing CMIP5 multi-model simulations with observations has improved understanding of the hydrological responses to 20th century eruptions, particularly global land monsoon drying, and associated uncertainties (Section 3.3.2.3). Global mean land precipitation decreases for up to a few years following the eruption, with climatologically wet regions drying and dry regions wetting (Sections 3.3.2.3 and 4.4.4). Changes in monsoon circulations occur with a general weakening of tropical precipitation (Section 8.5.2.3) and a decrease in extreme precipitation over global monsoon regions (Section 11.4.4). Monsoon precipitation in one hemisphere tends to be enhanced by eruptions occurring in the other hemisphere or reduced if they occur in the same hemisphere (Sections 3.3.2.3 and 8.5.2.3). Volcanic eruptions have been linked to the onset of El Niñofollowed by La Niña although this connection remains contentious (Adams et al., 2003; Bradley et al., 2003; McGregor et al., 2010; Khodri et al., 2017; F. Liu et al., 2018; Sun et al., 2019;Paik et al., 2020; Predybaylo et al., 2020). Volcanic activity could drive short-term (one-to-three-year) positive changes in the annual SAM index through modulations in the extratropical temperature gradient and wave driving of the polar stratosphere (Yang and Xiao, 2018). In the cryosphere, Arctic sea ice extent increases for years to decades (Gagné et al., 2017a), and modelling indicates that sea ice/ocean feedbacks can prolong cooling long after volcanic aerosols are removed (Miller et al., 2012). On annual time scales, the ocean buffers the atmospheric response to volcanic eruptions by storing the cooling in the ocean subsurface, then feeding it back to the atmosphere. Large eruptions affect ocean heat content and thermosteric sea level over decadal-to-centennial scales (Section 9.2.2.1).

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Climate models project a weaker polar amplification in the SH than in the NH under transient warming (Figure 4.19). Model simulations (Hall, 2004; Danabasoglu and Gent, 2009; Li et al., 2013) and paleoclimate proxies indicate polar amplification in both hemispheres near equilibrium, but generally with less warming in the Antarctic than the Arctic (Section 7.4.4.1.2). The primary driver of delayed warming of the southern high latitudes is the upwelling in the Southern Ocean and associated ocean heat uptake that is then transported away from Antarctica by northward flowing surface waters (Frölicher et al., 2015; Marshall et al., 2015; Armour et al., 2016; Liu et al., 2018), although asymmetries in feedbacks between the poles also play a role (Section 7.4.4.1.1). Changes in westerly surface winds over the Southern Ocean have the potential to affect the rate of sea-surface warming, but there is currentlylow confidence in even the sign of the effect based on a diverse range of climate model responses to wind changes (Marshall et al., 2014; Ferreira et al., 2015; Kostov et al., 2017; Seviour et al., 2019). A substantial increase in freshwater input to the ocean from the Antarctic ice sheet could further slow the emergence of SH polar amplification by cooling the Southern Ocean surface (Bronselaer et al., 2018; Golledge et al., 2019; Schloesser et al., 2019), but this process is not represented in current climate models which lack dynamic ice sheets. Thus, while there is high confidence that the SH high latitudes will warm by more than the tropics on centennial time scales, there is low confidence that such a feature will emerge this century (Section 7.4.4.1).

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Kobashi, T. et al., 2017: Volcanic influence on centennial to millennial Holocene Greenland temperature change. Scientific Reports, 7(1), 1–10, doi: 10.1038/s41598-017-01451-7.

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Variations in solar forcing (Section 2.2.1) could influence regional climate through its modulation of circulation patterns, although this research field is still hampered by large observational and modelling uncertainties. The 11-year solar cycle has been suggested to affect the leading atmospheric circulation modes of the North Atlantic region in model-based studies (Gray et al., 2013; Thiéblemont et al., 2015; Sjolte et al., 2018). In particular the solar cycle has been suggested as an important source of near-term predictability of the North Atlantic Oscillation (NAO; Kushnir et al., 2019), while other studies have not found evidence for links between the solar cycle and NAO in observational records (Ortega et al., 2015; Sjolte et al., 2018; Chiodo et al., 2019). On centennial time scales, solar fluctuations were found to be correlated with the Eastern Atlantic Pattern (Sjolte et al., 2018). Possible influences on winter circulation and temperature over Eurasia (Chen et al., 2015) and North America (Liu et al., 2014; Li and Xiao, 2018) have also been identified.

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Paleo-reanalyses are enabling a new range of applications and have already provided useful information on seasonal-to-multi-decadal climate variability over past millennia. They are useful tools to study the co-variance between variables at interannual-to-centennial time scales and at regional to global spatial scales. In particular, they have highlighted the processes that can be responsible for changes in continental hydrology at multi-decadal time scales (Franke et al., 2017; Klein and Goosse, 2018; Steiger et al., 2018). Paleo-reanalyses have confirmed a large contribution of internal variability in past changes at regional scale during the pre-industrial period, superimposed on a weak common signal due to forcing changes (Goosse et al., 2012) and the absence of a globallycoherent warm period in the common era before the recent warming (Neukom et al., 2019). Reconstructions of the atmospheric state obtained in the reanalysis also provide robust evidence of a local enhancement of warming or cooling conditions due to changes in atmospheric circulation, such as for the warm conditions in some European regions around 950–1250 CE, the cooling observed in 1809/1810, or the cold and rainy 1816 summer in Europe (Cross-Chapter Box 4.1; Goosse et al., 2012; Hakim et al., 2016; Franke et al., 2017; Schurer et al., 2019).

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In recent years, the role of internal variability in the interpretation of climate projections has become clearer, particularly at the regional scale (Section 10.3.4.3). A considerable fraction of CMIP5 and CMIP6 resources has been invested in generating an ensemble of centennial or multi-centennial control simulations with constant external forcings (Pedro et al., 2016; Rackow et al., 2018). As part ofthe CMIP6 DECK (Eyring et al., 2016a) pre-industrial control (piControl) simulations have been conducted (Menary et al., 2018). Similarly, control simulations with present-day conditions (pdControl) have been performed to represent internal variability under more recent forcing conditions (Pedro et al., 2016; Williams et al., 2018). Control simulations have been used to study the role of internal variability, teleconnections and many other fundamental aspects of climate models (Z. Wang et al., 2015; Krishnamurthy and Krishnamurthy, 2016). Control simulations are also used along with large ensembles of historical or scenario simulations to assess the characteristics of the regional internal climate variability (Olonscheck and Notz, 2017).

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This section assesses how well climate models perform at realistically simulating historical regional climatic trends. Current global model ensembles reproduce global to continental-scale surface temperature trends at multi-decadal to centennial time scales (CMIP5, CMIP6), but underestimate precipitation trends (CMIP5) (Sections 3.3.1 and 3.3.2). For regional trends, AR5 concluded that the CMIP5 ensemble cannot be taken as a reliable representation of reality and that the true uncertainty can be larger than the simulated model spread (Kirtman et al., 2014). Case studies of regional trend simulations by global models can be found in Sections 10.4.1 and 10.6, and region-by-region assessments in the Atlas. A key limitation for assessing the representation of regional observed trends by single transient simulations of global models (or downscaled versions thereof) is the strong amplitude of internal variability compared to the forced signal at the regional scale (Section 10.3.4.3). Even on multi-decadal time scales, an agreement between observed and individual simulated trends would be expected to occur only by chance (Laprise, 2014).

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Lokoshchenko, M.A., 2017: Urban Heat Island and Urban Dry Island in Moscow and Their Centennial Changes. Journal of Applied Meteorology and Climatology, 56(10), 2729–2745, doi: 10.1175/jamc-d-16-0383.1.

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Saurral, R.I., I.A. Camilloni, and V.R. Barros, 2017: Low-frequency variability and trends in centennial precipitation stations in southern South America. International Journal of Climatology, 37(4), 1774–1793, doi: 10.1002/joc.4810.

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There is high confidence that the magnitude of floods over the Common Era exceeded observed records in some locations, including Central Europe and eastern Asia. Recent literature supports the AR5 assessments of floods (Masson-Delmotte et al., 2013). For example, high temporally resolved records provide evidence of Common Era floods exceeding the probable maximum flood levels in the Upper Colorado River, USA (Greenbaum et al., 2014) and peak discharges that are double gauge levels along the middle Yellow River, China (Liu et al., 2014). Further studies demonstrate pre-instrumental or early instrumental differences in flood frequency compared to the instrumental period, including reconstructions of high and low flood frequency in the European Alps (e.g., Swierczynski et al., 2013; Amann et al., 2015) and Himalayas (Ballesteros Cánovas et al., 2017). The combination of extreme historical flood episodes determined from documentary evidence also increases confidence in the determination of flood frequency and magnitude, compared to using geomorphological archives alone (Kjeldsen et al., 2014). In regions, such as Europe and China, that have rich historical flood documents, there is strong evidence of high-magnitude flood events over pre-instrumental periods (Kjeldsen et al., 2014; Benito et al., 2015; Macdonald and Sangster, 2017). A key feature of paleoflood records is variability in flood recurrence at centennial timescales (Wilhelm et al., 2019), although constraining climate-flood relationships remains challenging. Pre-instrumental floods often occurred in considerably different contexts in terms of land use, irrigation, and infrastructure, and may not provide direct insight into modern river systems, which further prevents long-term assessments of flood changes being made based on these sources.

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There is medium confidence that periods of both more and less tropical cyclone activity (frequency or intensity) than observed occurred over the Common Era in many regions. Paleotempest studies cover a limited number of locations that are predominantly coastal, and hence provide information on specific locations that cannot be extrapolated basin-wide (see Muller et al., 2017). In some locations, such as the Gulf of Mexico and the New England, USA, coast, similarly intense storms to those observed recently have occurred multiple times over centennial timescales (Donnelly et al., 2001; Bregy et al., 2018). Further research focused on the frequency of tropical storm activity. Extreme storms occurred considerably more frequently in particular periods of the Common Era, compared to the instrumental period in north-east Queensland, Australia (Nott et al., 2009; Haig et al., 2014), and the Gulf Coast (e.g., Brandon et al., 2013; Lin et al., 2014).

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Identifying past trends in TC metrics remains a challenge due to the heterogeneous character of the historical instrumental data, which are known as ‘best-track’ data (Schreck et al., 2014). There is low confidence in most reported long-term (multi-decadal to centennial) trends in TC frequency- or intensity-based metrics due to changes in the technology used to collect the best-track data. This should not be interpreted as implying that no physical (real) trends exist, but rather as indicating that either the quality or the temporal length of the data is not adequate to provide robust trend detection statements, particularly in the presence of multi-decadal variability.

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Funk, C. et al., 2015b: The Centennial Trends Greater Horn of Africa precipitation dataset. Scientific Data, 2, 150050, doi: 10.1038/sdata.2015.50.

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Park Williams, A. et al., 2017: The 2016 Southeastern U.S. Drought: An Extreme Departure From Centennial Wetting and Cooling. Journal of Geophysical Research: Atmospheres, 122(20), 10888–10905, doi: 10.1002/2017jd027523.

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Saurral, R.I., I.A. Camilloni, and V.R. Barros, 2017: Low-frequency variability and trends in centennial precipitation stations in southern South America. International Journal of Climatology, 37(4), 1774–1793, doi: 10.1002/joc.4810.

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Quantification of changes in groundwater storage from GRACE is currently constrained by uncertainty in the estimation of changes in other terrestrial water stores using uncalibrated, global-scale Land Surface Models (Döll et al. , 2014; Scanlon et al. , 2018) and the limited duration of the period of GRACE observations (2002 to 2016). Centennial-scale piezometry in north-west India reveals that recent groundwater depletion traced by GRACE (Rodell et al., 2009; Chen et al., 2014), follows more than a century of groundwater accumulation through canal leakage (MacDonald et al., 2016). Further, groundwater depletion is often localized occurring below the footprint (200,000 km2) of GRACE, as has been well demonstrated by detailed modelling studies in the California Central Valley (Scanlon et al., 2012) and North China Plain (Cao et al., 2016).

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Isotope records from caves in the central Peruvian Andes show that the late Holocene (<3000 years ago) was characterized by multi-decadal and centennial-scale periods of significant decline in intensity of the SAmerM (Bird et al., 2011a; Vuille et al., 2012). This could be partly due to a reduction in the zonal SST gradient of the Pacific Ocean, favouring El Niño-like conditions (Kanner et al., 2013). Other studies suggest increased SAmerM precipitation amount during the Late Holocene, in association with the expansion of the tropical forest (Smith and Mayle, 2018). Well-dated equilibrium lines of glaciers during the deglaciation suggest that the AMOC enhances Atlantic moisture sources and precipitation amount increase over the tropical and southern Andes (Beniston et al., 2018).

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The AR5 indicated low confidence in long-term changes in the intensity of extratropical cyclones (ETC) over the 20th century derived from centennial reanalyses and storminess proxies based upon sea level pressure. This was confirmed by the SREX assessment that the main Northern Hemisphere (NH) and Southern Hemisphere (SH) extratropical storm tracks likely experienced a poleward shift during the last 50 years (Seneviratne et al., 2012) with low confidence, and inconsistencies within reanalysis datasets remain.

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Analysis of storm track activity over longer periods suffers from uncertainties associated with changing data assimilation and observations before and during the satellite era, resulting in in homogeneities and discontinuities in centennial reanalyses (Krueger et al. , 2013; X.L. Wang et al. , 2013, 2016; Chang and Yau, 2016; Varino et al. , 2019). Feser et al. (2015) reviewed multiple storm track records for the Atlantic-European sector and demonstrated growing storm activity north of 55°N from the 1970s to the mid-1990s with declining trend thereafter, sugesting strong inter-decadal variability in storm track activity. This was also confirmed by Krueger et al. (2019) from the analysis of geostrophic winds derived from sea level pressure gradients.

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Poleward deflection of mostly oceanic winter storm tracks since 1979 was reported in both the North Atlantic and North Pacific (Tilinina et al., 2013; J. Wang et al., 2017). This large-scale tendency has regional variations and may be seasonally dependent. Wise and Dannenberg (2017) reported a southward shift in the east Pacific storm track from the 1950s to mid-1980s followed by northward deflection in the later decades. (King et al., 2019) reported an association of Atlantic storm track migrations with SSW events with Central and South European precipitation anomalies. Over centennial time scales, Gan and Wu (2014) reported an intensification of storm tracks in the poleward and downstream regions of the North Pacific and North Atlantic upper troposphere using the NOAA–CIRES–DOE Twentieth Century Reanalysis. Poleward migration of the SH storm tracks (Grise et al., 2014; X.L. Wang et al., 2016; Dowdy et al., 2019) was identified during the austral summer and is closely associated with cyclone-associated frontal activity (Solman and Orlanski, 2014, 2016) and cloud cover (Bender et al., 2012; Norris et al., 2016).

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Over the 20th century, CMIP5 models show a realistic magnitude of decadal precipitation variability, if not a slight overestimation in some regions (Knutson and Zeng, 2018). However, the relatively short and human-influenced instrumental record limits our ability to quantify the magnitude of internal variability in the water cycle, particularly over long time scales (decadal and beyond). Global extended reanalyses (Section 1.5.2) have been used to derive long-term variability in the regional water cycle components (Caillouet et al., 2017), merged with historical meteorological and hydrological local observations (Bonnet et al., 2017; Devers et al., 2020). Specific assessment of these types of methodology and related uncertainties is provided in Chapter 10 (Sections 10.2 and 10.3). Paleoclimate archives (tree rings, corals, ice core, speleothems, lake and ocean sediments) provide extended reconstructions of key water cycle metrics and large-scale circulation features. Some studies have suggested that CMIP5 models underestimate internal variability at decadal and longer time scales, and therefore may be missing important processes in the climate system (Ault et al. , 2012, 2013; Bunde et al. , 2013; Franke et al. , 2013; Cheung et al. , 2017; Hope et al. , 2017; Kravtsov, 2017; Cassou et al. , 2018). However, recent assessments using paleoclimate records have found that CMIP5 models are able to reproduce decadal-to-centennial variability, including the severity, persistence and spatial extent of megadroughts (Coats et al., 2015; Stevenson et al., 2015; PAGES Hydro2K Consortium, 2017), once signal reddening (autocorrelation) in proxy archives is accounted for (Deeet al., 2017; PAGES Hydro2K Consortium, 2017). Implementation of proxy system models, that is, functions that transform model variables into proxy units, has reduced model–proxy disagreement, although some differences in the magnitude of internal variability remain, particularly at centennial time scales (Deeet al., 2017; Parsons et al., 2017). It is unclear whether remaining discrepancies represent limitations of the climate models, or limitations of the proxy system models. Therefore, there is medium to high confidence (i.e., depending on the region) that climate models do not underestimate water cycle internal variability.

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Cheung, R.C.W. et al., 2018: Decadal- to Centennial-Scale East Asian Summer Monsoon Variability Over the Past Millennium: An Oceanic Perspective. Geophysical Research Letters, 45(15), 7711–7718, doi: 10.1029/2018gl077978.

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D’Agostino, R. and P. Lionello, 2017: Evidence of global warming impact on the evolution of the Hadley Circulation in ECMWF centennial reanalyses. Climate Dynamics, 48(9–10), 3047–3060, doi: 10.1007/s00382-016-3250-0.

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Fletcher, M.-S. et al., 2018: Centennial-scale trends in the Southern Annular Mode revealed by hemisphere-wide fire and hydroclimatic trends over the past 2400 years. Geology, 46(4), 363–366, doi: 10.1130/g39661.1.

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Gan, B. and L. Wu, 2014: Centennial trends in Northern Hemisphere winter storm tracks over the twentieth century. Quarterly Journal of the Royal Meteorological Society, 140(683), 1945–1957, doi: 10.1002/qj.2263.

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Levang, S.J. and R.W. Schmitt, 2015: Centennial changes of the global water cycle in CMIP5 models. Journal of Climate, 28(16), 6489–6502, doi: 10.1175/jcli-d-15-0143.1.

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Novello, V.F. et al., 2016: Centennial-scale solar forcing of the South American Monsoon System recorded in stalagmites. Scientific Reports, 6(1), 1–8, doi: 10.1038/srep24762.

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Saurral, R.I., I.I.A. Camilloni, and V.R. Barros, 2017: Low-frequency variability and trends in centennial precipitation stations in southern South America. International Journal of Climatology, 37, 1774–1793, doi: 10.1002/joc.4810.

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Shi, F., K. Fang, C. Xu, Z. Guo, and H.P. Borgaonkar, 2017: Interannual to centennial variability of the South Asian summer monsoon over the past millennium. Climate Dynamics, 49(7), 2803–2814, doi: 10.1007/s00382-016-3493-9.

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Paleoclimate records also show centennial- to millennial-scale variations, particularly during the ice ages, which indicate rapid or abrupt changes of the Atlantic Meridional Overturning Circulation (AMOC; Section 9.2.3.1) and the occurrence of a ‘bipolar seesaw’ (opposite-phase surface temperature changes in both hemispheres; Section 2.3.3.4.1; Stocker and Johnsen, 2003; EPICA Community Members, 2006; WAIS Divide Project Members et al., 2015; Lynch-Stieglitz, 2017; Pedro et al., 2018; Weijer et al., 2019). This process suggests that instabilities and irreversible changes could be triggered if critical thresholds are passed (Section 1.4.4.3). Several other processes involving instabilities are identified in climate models (Drijfhout et al., 2015), some of which may now be close to critical thresholds (Section 1.4.4.3; see also Chapters 5, 8 and 9 regarding tipping points; Joughin et al., 2014).

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With a heat capacity about 1000 times greater than that of the atmosphere, Earth’s ocean stores the vast majority of energy retained by the planet. Ocean currents transport the stored heat around the globe and, over decades to centuries, from the surface to its greatest depths. The ocean’s thermal inertia moderates faster changes in radiative forcing on land and in the atmosphere, reaching full equilibrium with the atmosphere only after hundreds to thousands of years (Yang and Zhu, 2011). The earliest subsurface measurements in the open ocean date to the 1770s (Abraham et al., 2013). From 1872–76, the research ship HMS Challenger measured global ocean temperature profiles at depths up to 1700 m along its cruise track. By 1900, research ships were deploying instruments such as Nansen bottles and mechanical bathythermographs (MBTs) to develop profiles of the upper 150 m in areas of interest to navies and commercial shipping (Abraham et al., 2013). Starting in 1967, eXpendable BathyThermographs (XBTs) were deployed by scientific and commercial ships along repeated transects to measure temperature to 700 m (Goni et al., 2019). Ocean data collection expanded in the 1980s with the Tropical Ocean Global Experiment (TOGA; Gould, 2003). Marine surface observations for the globe, assembled in the mid-1980s in the International Comprehensive Ocean-Atmosphere Data Set (ICOADS; Woodruff et al., 1987, 2005), were extended to 1662–2014 using newly recovered marine records and metadata (Woodruff et al., 1998; Freeman et al., 2017). The Argo submersible float network, developed in the early 2000s, provided the first systematic global measurements of the 700–2000 m layer. Comparing the HMS Challengerdata to data from Argo submersible floats revealed global subsurface ocean warming on the centennial scale (Roemmich et al., 2012). The AR5 WGI assessed with high confidence that ocean warming accounted for more than 90% of the additional energy accumulated by the climate system between 1971 and 2010 (IPCC, 2013b). In comparison, warming of the atmosphere corresponds to only about 1% of the additional energy accumulated over that period (IPCC, 2013a). Chapter 2 summarizes the ocean heat content datasets used in AR6 (Section 2.3.3.1 and Table 2.7).

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Natural drivers include changes in solar irradiance, ocean currents, naturally occurring aerosols, and natural sources and sinks of radiatively active gases such as water vapour, CO2, CH4, and sulphur dioxide (SO2). Detailed global measurements of surface-level solar irradiance were first conducted during the 1957–1958 International Geophysical Year (Landsberg, 1961), while top-of-atmosphere irradiance has been measured by satellites since 1959 (House et al., 1986). Measured changes in solar irradiance have been small and slightly negative since about 1980 (Matthes et al., 2017). Water vapour is the most abundant radiatively active gas, accounting for about 75% of the terrestrial greenhouse effect, but because its residence time in the atmosphere averages just 8–10 days, its atmospheric concentration is largely governed by temperature (van der Ent and Tuinenburg, 2017; Nieto and Gimeno, 2019). As a result, non-condensing GHGs with much longer residence times serve as ‘control knobs’, regulating planetary temperature, with water vapour concentrations as a feedback effect (Lacis et al., 2010, 2013). The most important of these non-condensing gases is CO2 (a positive driver), released naturally by volcanism at about 637 MtCO2 yr–1 in recent decades, or roughly 1.6% of the 37 GtCO2 emitted by human activities in 2018 (Burton et al., 2013; Le Quéré et al., 2018). Absorption by the ocean and uptake by plants and soils are the primary natural CO2 sinks on decadal to centennial time scales (Section 5.1.2 and Figure 5.3).

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ECS is typically characterized as most relevant on centennial time scales, while TCR was long seen as a more appropriate measure of the 50–100-year response to gradually increasing CO2. However, recent studies have raised new questions about how accurately both quantities are estimated by GCMs and ESMs (Grose et al., 2018; Meehl et al., 2020; Sherwood et al., 2020). Further, as climate models evolved to include a full-depth ocean, the time scale for reaching full equilibrium became longer and new methods to estimate ECS had to be developed (Gregory et al., 2004; Meehl et al., 2020; Meinshausen et al., 2020). Because of these considerations, as well as new estimates from observation-based, paleoclimate, and emergent-constraints studies (Sherwood et al., 2020), the AR6 definition of ECS has changed from previous reports; it now includes all feedbacks except those associated with ice sheets. Accordingly, unlike previous reports, the AR6 assessments of ECS and TCR are not based primarily on GCM and ESM model results (see Section 7.5.5 and Box. 7.1 for a full discussion).

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New datasets as well as recent data compilations and syntheses of sea level over the last millennia (Kopp et al., 2016; Kemp et al., 2018), the last 20 kyr (Khan et al., 2019), the last interglacial period (Section 2.3.3.3: Dutton et al., 2015), and the Pliocene (Cross-Chapter Box 2.4; Dumitru et al., 2019; Grant et al., 2019) help constrain sea level variability and its relationship to global and regional temperature variability, and to estimates of contributions to sea level change from different sources on centennial to millennial time scales (Section 9.6.2).

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Becker, A. et al., 2013: A description of the global land-surface precipitation data products of the Global Precipitation Climatology Centre with sample applications including centennial (trend) analysis from 1901–present. Earth System Science Data, 5(1), 71–99, doi: 10.5194/essd-5-71-2013.

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Marcott, S.A. et al., 2014: Centennial-scale changes in the global carbon cycle during the last deglaciation. Nature, 514(7524), 616–619, doi: 10.1038/nature13799.

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Since AR5, the link between GCR and new particle formation has been more thoroughly studied, particularly by experiments in the CERN CLOUD chamber (Cosmics Leaving OUtdoor Droplets; Dunne et al., 2016; Kirkby et al., 2016; Pierce, 2017). By linking the GCR-induced new particle formation from CLOUD experiments to CCN, Gordon et al. (2017) found that the CCN concentration for low-clouds differed by 0.2–0.3% between solar maximum and solar minimum. Combined with relatively small variations in the atmospheric ion concentration over centennial time scales (Usoskin et al., 2015), it is therefore unlikely that cosmic ray intensity affects present-day climate via nucleation (Yu and Luo, 2014; Dunne et al., 2016; Pierce, 2017; Lee et al., 2019).

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In global climate models, the feedback parameters α x in global warming conditions are often estimated as the mean differences in the radiative fluxes between atmosphere-only simulations in which the change in SST is prescribed (Cess et al., 1990), or as the regression slope of change in radiation flux against change in GSAT using atmosphere–ocean coupled simulations with abrupt CO2 changes (abrupt 4xCO2 ) for 150 years (Box 7.1; Gregory et al., 2004; Andrews et al., 2012; Caldwell et al., 2016). Neither method is perfect, but both are useful and yield consistent results (Ringer et al., 2014). In the regression method, the radiative effects of land warming are excluded from the ERF due to doubling of CO2Section 7.3.2), which may overestimate feedback values by about 15%. At the same time, the feedback calculated using the regression over years 1–150 ignores its state-dependence on multi-centennial time scales (Section 7.4.3), probably giving an underestimate of α by about 10% (Rugenstein et al., 2019). These effects are both small and approximately cancel each other in the ensemble mean, justifying the use of regression over 150 years as an approximation to feedbacks in ESMs.

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The surface-albedo feedback estimates using centennial changes have been shown to be highly correlated to those using seasonal regional changes for NH land snow (Qu and Hall, 2014) and Arctic sea ice (Thackeray and Hall, 2019). For the NH land snow, because the physics underpinning this relationship are credible, this opens the possibility to use it as an emergent constraint (Qu and Hall, 2014). Considering only the eight models whose seasonal cycle of albedo feedback falls within the observational range does not change the multi-model mean contribution to global α A(0.08 W m–2°C–1) but decreases the inter-model spread by a factor of two (from ±0.03 to ±0.015 W m–2°C–1; Qu and Hall, 2014). For Arctic sea ice, Thackeray and Hall (2019) show that the seasonal cycle also provides an emergent constraint, at least until mid-century when the relationship degrades. They find that the CMIP5 multi-model mean of the Arctic sea ice contribution to α A is 0.13 W m–2°C–1and that the inter-model spread is reduced by a factor of two (from ±0.04 to ±0.02 W m–2°C–1) when the emergent constraint is used. This model estimate is smaller than observational estimates (Pistone et al., 2014; Cao et al., 2015) except those of Donohoe et al. (2020). This can be traced to CMIP5 models generally underestimating the rate of Arctic sea ice loss during recent decades (Section 9.3.1; Stroeve et al., 2012; Flato et al., 2013), though this may also be an expression of internal variability, since the observed behaviour is captured within large ensemble simulations (Notz, 2015). CMIP6 models better capture the observed Arctic sea ice decline (Section 3.4.1). In the SH the opposite situation is observed. Observations show relatively flat trends in SH sea ice over the satellite era (Section 2.3.2.1) whereas CMIP5 models simulate a small decrease (Section 3.4.1). SH α A is presumably larger in models than observations but only contributes about one quarter of the global α A. Thus, we assess that α Aestimates are consistent, at global scale, in CMIP5 and CMIP6 models and satellite observations, though hemispheric differences and the role of internal variability need to be further explored.

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The magnitude of the radiative feedback associated with changes to ice sheets can be quantified by comparing the global mean long-term equilibrium temperature response to increased CO2 concentrations in simulations that include interactive ice sheets with that of simulations that do not include the associated ice sheet–climate interactions (Swingedouw et al., 2008; Vizcaíno et al., 2010; Goelzer et al., 2011; Bronselaer et al., 2018; Golledge et al., 2019). These simulations indicate that on multi-centennial time scales, ice-sheet mass loss leads to freshwater fluxes that can modify ocean circulation (Swingedouw et al., 2008; Goelzer et al., 2011; Bronselaer et al., 2018; Golledge et al., 2019). This leads to reduced surface warming (by about 0.2°C in the global mean after 1000 years; Section 7.4.4.1.1; Goelzer et al., 2011), although other work suggests no net global temperature effect of ice-sheet mass loss (Vizcaíno et al., 2010). However, model simulations in which the Antarctic Ice Sheet is removed completely in a paleoclimate context indicate a positive global mean feedback on multi-millennial time scales due primarily to the surface-albedo change (Goldner et al., 2014a; Kennedy-Asser et al., 2019); in (Chapter 9 Section 9.6.3) it is assessed that such ice-free conditions could eventually occur given 7°C–13°C of warming. This net positive feedback from ice-sheet mass loss on long time scales is also supported by model simulations of the mid-Pliocene Warm Period (MPWP; Cross-chapter Box 2.1) in which the volume and area of the Greenland and West Antarctic ice sheets are reduced in model simulations in agreement with geological data (Chandan and Peltier, 2018), leading to surface warming. As such, overall, on multi-centennial time scales the feedback parameter associated with ice sheets is likely negative (medium confidence), but on multi-millennial time scales by the time the ice sheets reach equilibrium, the feedback parameter is very likely positive (high confidence) (Table 7.10). However, a relative lack of models carrying out simulations with and without interactive ice sheets over centennial to millennial time scales means that there is currently not enough evidence to quantify the magnitude of these feedbacks, or the time scales on which they act.

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The large-scale patterns of surface warming in observations since the 19th century (Section 2.3.1) and climate model simulations (Section 4.3.1 and Figure 7.12a) share several common features. In particular, surface warming in the Arctic is greater than for the global average and greater than in the Southern Hemisphere (SH) high latitudes; and surface warming is generally greater over land than over the ocean. Observations and climate model simulations also show some notable differences. ESMs generally simulate a weakening of the equatorial Pacific Ocean zonal (east–west) SST gradient on multi-decadal to centennial time scales, with greater warming in the east than the west, but this trend has not been seen in observations (Section 9.2.1 and Figure 2.11b).

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Southern Ocean SSTs have been slow to warm over the instrumental period, with cooling since about 1980 owing to a combination of upper-ocean freshening from ice-shelf melt, intensification of surface westerly winds from ozone depletion, and variability in ocean convection (Section 9.2.1). This stands in contrast to the equilibrium warming pattern either inferred from the proxy record or simulated by ESMs under CO2 forcing. There is high confidence that the SH high latitudes will warm more than the tropics on centennial time scales as the climate equilibrates with radiative forcing and Southern Ocean heat uptake is reduced. However, there is onlylow confidence that this feature will emerge this century.

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A weakening of the equatorial Pacific Ocean east–west SST gradient, with greater warming in the east than the west, is a common feature of the climate response to greenhouse gas forcing as projected by ESMs on centennial and longer time scales (e.g., Figure 7.14b; see (Section 4.5.1). There are thought to be several factors contributing to this pattern. In the absence of any changes in atmospheric or oceanic circulations, the east–west surface temperature difference is theorized to decrease owing to weaker evaporative damping, and thus greater warming in response to forcing, where climatological temperatures are lower in the eastern Pacific cold tongue (Xie et al., 2010; Luo et al., 2015). Within atmospheric ESMs coupled to a mixed-layer ocean, this gradient in damping has been linked to the rate of change with warming of the saturation specific humidity, which is set by the Clausius–Clapeyron relation (Merlis and Schneider, 2011). Gradients in low-cloud feedbacks may also favour eastern equatorial Pacific warming (DiNezio et al., 2009).

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Research published since AR5 (Burls and Fedorov, 2014b; Fedorov et al., 2015; Erfani and Burls, 2019) has built on an earlier theory (Liu and Huang, 1997; Barreiro and Philander, 2008) linking the east–west temperature gradient to the north–south temperature gradient. In particular, model simulations suggest that a reduction in the equator-to-pole temperature gradient (polar amplification) increases the temperature of water subducted in the extra-tropics, which in turn is upwelled in the eastern Pacific. Thus, polar amplified warming, with greater warming in the mid-latitudes and subtropics than in the deep tropics, is expected to contribute to the weakening of the east–west equatorial Pacific SST gradient on decadal to centennial time scales.

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Overall the observed pattern of warming over the instrumental period, with a warming minimum in the eastern tropical Pacific Ocean (Figure 7.14a), stands in contrast to the equilibrium warming pattern either inferred from the MPWP proxy record or simulated by ESMs under CO2 forcing. There is medium confidence that the observed strengthening of the east–west SST gradient is temporary and will transition to a weakening of the SST gradient on centennial time scales. However, there is onlylow confidence that this transition will emerge this century owing to a low degree of agreement across studies about the factors driving the observed strengthening of the east–west SST gradient and how those factors will evolve in the future. These trends in tropical Pacific SST gradients reflect changes in the climatology, rather than changes in ENSO amplitude or variability, which are assessed in (Chapter 4 Section 4.3.3).

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While there are not yet direct observational constraints on the magnitude of the pattern effect, satellite measurements of variations in TOA radiative fluxes show strong co-variation with changing patterns of SSTs, with a strong dependence on SST changes in regions of deep convective ascent (e.g., in the western Pacific warm pool; Loeb et al., 2018a; Fueglistaler, 2019). Cloud and TOA radiation responses to observed warming patterns in atmospheric models have been found to compare favourably with those observed by satellite (Section 7.2.2.1 and Figure 7.3; Zhou et al., 2016; Loeb et al., 2020). This observational and modelling evidence indicates the potential for a strong pattern effect in nature that will only be negligible if the observed pattern of warming since pre-industrial levels persists to equilibrium – an improbable scenario given that Earth is in a relatively early phase of transient warming and that reaching equilibrium would take multiple millennia (C. Li et al., 2013). Moreover, paleoclimate proxies, ESM simulations, and process understanding indicate that strong warming in the eastern equatorial Pacific Ocean (with medium confidence) and Southern Ocean (with high confidence) will emerge on centennial time scales as the response to CO2 forcing dominates temperature changes in these regions (Sections 7.4.4.1, 7.4.4.2 and 9.2.1). However, there is low confidence that these features, which have been largely absent over the historical record, will emerge this century (Sections 7.4.4.1, 7.4.4.2 and (Section 9.2.1). This leads to high confidence that radiative feedbacks will become less negative as the CO2-forced pattern of surface warming emerges ( α > 0 W m–2°C–1), but low confidence that these feedback changes will be realized this century. There is also substantial uncertainty in the magnitude of the net radiative feedback change between the present warming pattern and the projected equilibrium warming pattern in response to CO2 forcing owing to the fact that its quantification currently relies solely on ESM results and is subject to uncertainties in historical SST patterns. Thus, based on the pattern of warming since 1870, α is estimated to be in the range 0.0 to 1.0 W m–2°C–1but with a low confidence in the upper end of this range. A value of α = +0.5 ± 0.5 W m–2°C–1is used to represent this range in Box 7.2 and (Section 7.5.2, which respectively assess the implications of changing radiative feedbacks for Earth’s energy imbalance and estimates of ECS based on the instrumental record. The value of α is larger if quantified based on the observed pattern of warming since 1980 (Figure 2.11b) which is more distinct from the equilibrium warming pattern expected under CO2 forcing (high confidence) (similar to CMIP6 projections shown in Figure 7.12a; Andrews et al., 2018).

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Wohland, J., D. Brayshaw, H. Bloomfield, and M. Wild, 2020: European multidecadal solar variability badly captured in all centennial reanalyses except CERA20C. Environmental Research Letters, 15(10), 104021, doi: 10.1088/1748-9326/aba7e6.

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It is virtually certain that SST will continue to increase in the 21st century, at a rate depending on future emissions scenarios. The future global mean SST increase projected by CMIP6 models for the period 1995–2014 to 2081–2100 is 0.86 [5–95% range: 0.43–1.47] °C under SSP1-2.6, 1.51 [1.02 to 2.19] °C under SSP2-4.5, 2.19 [1.56 to 3.30] °C under SSP3-7.0, and 2.89 [2.01 to 4.07] °C under SSP5-8.5 (Figure 9.3). While under SSP1-2.6, the CMIP6 ensemble consistently projects that it is very likely at least 83% of the world ocean surface will have warmed by 2100, and under SSP5-8.5, at least 98% of the world ocean surface will have warmed. The spatial pattern of future change is consistent with observed SST change over the 20th century, though with notable regional differences (Figure 9.3). Long-term change in SST patterns is important for regional impacts but also affects radiative feedbacks, and therefore long-term change in climate sensitivity (Section 7.4.4.3). In the Southern Ocean, CMIP6 models project that SSTs will eventually consistently increase in the 21st century, at a rate dependent on future scenarios (Figure 9.3 and Section 9.2.3.2; Bracegirdle et al., 2020). Yet, there is only low confidence that this Southern Ocean warming will emerge by the end of the century (Section 7.4.4.1), due to the inconsistent historical and near-term simulations and observations over the 20th century (Figure 9.3). Furthermore, the equilibrium SST pattern from proxy records or simulated by climate models under CO2 forcing stand in contrast with the cooling trends in the Southern Ocean observed over the past decades (Section 7.4.4.1.2). Similarly, the SST change pattern observed in the tropical Pacific Ocean will transition on centennial time scales to a mean pattern resembling the El Niño pattern (medium confidence) (Annex IV). However, it is difficult to delineate a climate change trend ressembling an El Niño pattern and El Niño variability (Wittenberg, 2009; Collins et al., 2010) without large ensembles (Kay et al., 2015). Several Pliocene SST reconstructions indicate enhanced warming in the centre of the eastern Pacific equatorial cold tongue upwelling region, consistent with reconstruction of enhanced subsurface warming and enhanced warming in coastal upwelling regions (Section 7.4.4.2.2). The North Atlantic subpolar gyre is projected to continue to warm more slowly than surrounding regions (Suo et al., 2017), as the Gulf Stream concurrently warms rapidly (Figure 9.3; Cheng et al., 2013) and the Atlantic Meridional Overturning Circulation further declines under greenhouse gas forcing, although models disagree about the rate of change (Figure 9.3 and Section 9.2.3.1). In summary, CMIP6 models show a future pattern of SST change comparable to historical trends with intensity depending on future emissions scenario, and some of the observed cooling trends over the 20th century will eventually transition to a warming SST on centennial time scales, in particular in the Southern Ocean (high confidence) and in the equatorial Pacific (medium confidence), while the North Atlantic subpolar gyre will continue to warm more slowly than the global average (high confidence).

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Major volcanic eruptions have caused interannual to decadal cooling phases within the marked long-term increase in global OHC – Mount Agung in 1963, El Chichón in 1982 and Mount Pinatubo in 1991 (Cross-Chapter Box 4.1; Church et al., 2005; Fasullo et al., 2016; Stevenson et al., 2016; Fasullo and Nerem, 2018). In the first few years following an eruption, heat exchange with the subsurface ocean allows atmospheric cooling to be sequestered into the seasonal thermocline, therefore reducing the magnitude of the peak atmospheric temperature anomaly (Gupta and Marshall, 2018). However, while explosive volcanic eruptions only disturb the Earth’s radiative budget and surface fluxes for a few years, the ocean preserves an anomaly in OHC in the upper 500 m (also affecting thermosteric sea level) many years after the eruption (Gupta and Marshall, 2018; Bilbao et al., 2019). The anomaly affects the atmosphere through air–sea heat fluxes with surface conditions returning to normal only after several decades (Gupta and Marshall, 2018; Bilbao et al., 2019), or on centennial time scales in the case of repeated eruptions (G.H. Miller et al., 2012; Atwood et al., 2016; Gupta and Marshall, 2018). In summary, there is medium confidence that oceanic mechanisms buffer the atmospheric response to volcanic eruptions on annual time scales by storing volcanic cooling in the subsurface ocean, affecting OHC and thermosteric sea level on decadal to centennial time scales.

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CMIP5 and CMIP6 models simulate OHC changes that are consistent with the updated observational and improved estimates of OHC over the period 1960 to 2018 (Figures 9.6, 9.7 and 9.8), and they replicate the vertical partitioning of OHC change for the industrial era, although with a tendency to underestimate OHC gain shallower than 2000 m and overestimate it deeper than 2000 m (Section 3.5.1.3). The AR5 (Flato et al., 2013) assessed that climate models transport heat downward more than the real ocean. Since AR5, studies have shown that increasing the horizontal resolution of ocean models tends to increase agreement of vertical heat transport with observations as the dependency on ad-hoc choices of eddy parametrizations is relaxed (Griffies et al., 2015; Chassignet et al., 2020). The magnitude of the AMOC and Indonesian Throughflow affect future OHC change – for example, through overestimated modelled downward heat pumping (Kostov et al., 2014) – and there are indications of greater model consistency in these transports at higher resolution (Figure 9.10; Chassignet et al., 2020; L.C. Jackson et al., 2020). Climate models tend to reproduce the observed added heat, but redistributed heat is less well represented (Figure 9.8; Bronselaer and Zanna, 2020; Dias et al., 2020; Couldrey et al., 2021). Since redistributed heat dominates historical OHC change, historical simulations poorly reproduce regional patterns, but as future OHC change will become dominated by added heat, more skill in future modelled OHC patterns is expected (Bronselaer and Zanna, 2020). In summary, climate models have more skill in representing OHC change from added heat than from ocean circulation change (high confidence). Since added heat dominates over redistributed heat on a centennial scale (especially under high-emissions scenarios) confidence in future modelled OHC patterns at the end of the 21st century is greater than at decadal scale.

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Globally the mean salinity contrast at near-surface between high- and low-salinity regions increased 0.14 [0.07 to 0.20] from 1950 to 2019 (Section 2.3.3.2). At regional scale, SROCC (Meredith et al., 2019) assessed an Arctic liquid freshwater trend of 600 ± 300 km3yr–1(600 ± 200 Gt yr–1) between 1992 and 2012, reflecting changes associated with continental freshwater imports that affect ocean mass (land ice, rivers) as well as changes in sea ice volume. Since AR5, regional observation-based analyses not assessed in SROCC further confirm the long-term, large-scale and regional patterns of salinity change, both at the ocean surface and in the subsurface ocean, including almost 120 years of changes in the North Atlantic (Friedman et al., 2017) and 60 years of monitoring in the subpolar North Pacific (Cummins and Ross, 2020). These longer time series also provide context to detect large multi-annual change from 2012 to 2016 in the subpolar North Atlantic, unprecedented over the centennial record (Holliday et al., 2020). In summary, there is high confidence that salinity trends have extended for more than 60 to 100 years in the regions with long historical observation records, such as the North Pacific and the North Atlantic basin.

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The SROCC (Meredith et al., 2019) assessed that the global volume of Antarctic Bottom Water (AABW) had decreased and warmed since the 1980s, most noticeably near Antarctica. The SROCC also noted freshening in the Indian and Pacific sectors of the Southern Ocean and a higher rate of freshening in the Indian Sector from the 2000s to 2010s than from the 1990s to 2000s (low confidence). Since SROCC, freshening of Indian Ocean AABW from 1974 to 2016 has been revealed (Aoki et al., 2020). Additionally, interannual to decadal variability in AABW has been quantified to be larger than previously thought in terms of temperature, salinity and thickness, and in volume transport (Abrahamsen et al., 2019; Purkey et al., 2019; Gordon et al., 2020; Silvano et al., 2020). Multi-decadal to centennial modes of variability could have driven the observed trends of the lower cell over the past decades via the opening of a Weddell Sea Polynya (L. Zhang et al., 2019), although other studies find it contributed minimally to the observed abyssal warming (Zanowski et al., 2015; Zanowski and Hallberg, 2017). Therefore, there is limited evidence and low agreement in the role of open ocean polynyas in driving past decadal observed trends of AABW. Beyond variability, all observational, theoretical, and numerical evidence supports SROCC assessment that formation and export of AABW will continue to decrease due to warming and freshening of surface source waters near the Antarctic continent. Consistent with Section 9.2.3.2, confidence in this assessment is increased to medium confidence compared to SROCC.

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Both AR5(Collins et al., 2013)and SROCC (Collins et al., 2019) assessed that an abrupt collapse of AMOC before 2100 was very unlikely , but SROCC added that, by 2300, an AMOC collapse was as likely asnot for high-emissions scenarios. The SROCC also assessed that model bias may considerably affect the sensitivity of the modelled AMOC to freshwater forcing. Tuning towards stability and model biases (Valdes, 2011; Liu et al., 2017; Mecking et al., 2017; Weijer et al., 2019) provides CMIP models a tendency toward unrealistic stability (medium confidence). By correcting for existing salinity biases, Liu et al. (2017) demonstrated that AMOC behaviour may change dramatically on centennial to millennial time scales, and that the probability of a collapsed state increases. None of the CMIP6 models features an abrupt AMOC collapse in the 21st century, but they neglect meltwater release from the Greenland Ice Sheet. Also, a recent process study reveals that a collapse of AMOC can be induced, even by small-amplitude changes in freshwater forcing (Lohmann and Ditlevsen, 2021). As a result, we change the assessment of an abrupt collapse before 2100 to medium confidence that it will not occur.

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For the lower cell overturning circulation, SROCC assessed that a slowdown of its transport is consistent with the observed decrease in volume (medium confidence) of AABW in the global ocean (Section 9.2.2.3). Additional evidence since SROCC strengthens confidence that increased glacial meltwater flux will reduce the density of bottom waters during the 21st century. It will eventually reach a point where deep convection will be curtailed, and shelf water will become too buoyant to sink to the ocean interior, thereby slowing the lower cell overturning circulation (Bronselaer et al., 2018; Golledge et al., 2019; Lago and England, 2019; Moorman et al., 2020). While such changes are consistent with the observed freshening and decreased volume of the AABW layer reported in SROCC (as discussed in Section 9.2.2.3), new observation-based studies have highlighted how the lower cell overturning can episodically increase as a response to climate anomalies, temporally counteracting the tendency for melt to reduce AABW formation (Abrahamsen et al., 2019; Castagno et al., 2019; Gordon et al., 2020; Silvano et al., 2020). In addition, while the opening of open ocean polynyas can affect the lower cell on decadal to centennial time scales, there is limited evidence and low agreement in the role of open ocean polynyas in driving observed trends of the lower cell in the last decade (Section 9.2.2.3). Based on CMIP5 models, SROCC reported with low confidence that formation and export of AABW associated with the lower overturning cell will decrease in the 21st century, and there is no new evidence to revisit that assessment from climate models. However, additional paleo evidence from marine sediments suggests that AABW formation/ventilation was vulnerable to freshwater fluxes during past interglacials (Hayes et al., 2014; Huang et al., 2020; Turney et al., 2020) and that AABW formation was strongly reduced (Skinner et al., 2010; Gottschalk et al., 2016; Jaccard et al., 2016) or possibly totally curtailed (Huang et al., 2020) during the Last Glacial Maximum (LGM) and transient cold intervals of marine isotope stages 2 and 3 (MIS2 and MIS3). Specifically, sedimentary reconstructions show a transient reduction in AABW ventilation in the Atlantic sector of the Southern Ocean during MIS5e, which is assessed to have been warmer than modern climate (Thomas et al., 2020). However, long multi-centennial or millennial model runs under higher-than-pre-industrial CO2 concentrations show that, after 500–1000 years, ventilation in the Southern Ocean resumes, and possibly overshoots with enhanced convection in the Weddell and Ross seas, leading to enhanced bottom water ventilation globally (Yamamoto et al., 2015; Frölicher et al., 2020). AABW ventilation increased at the onset of the last deglacial transition, promoting the release of previously sequestered CO2 to the atmosphere on centennial to millennial time scales (Bauska et al., 2016; Jaccard et al., 2016; Rae et al., 2018), concomitant with a southward shift of the Southern Hemisphere westerly wind belt (Denton et al., 2010; Jaccard et al., 2016) and reduced sea ice cover (Ferrari et al., 2014; Stein et al., 2020). In summary, the combination of observational, numerical and paleoclimate evidence provides us with medium confidence that the lower cell will continue decreasing in the 21st century as a result of increased basal melt from the Antarctic Ice Sheet.

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The effect of tropical modes of variability on climate and their long-term changes are reviewed in detail in Annex IV, while changes to the tropical ocean are assessed throughout the report and briefly summarized here. Section 2.4 concludes that a sustained shift beyond multi-centennial variability has not been observed for El Niño–Southern Oscillation (ENSO) (medium confidence) and that there is limited evidence and limited agreement about the long-term behaviour of other tropical modes. Section 3.7 assesses with high confidence that human influence has not affected the principal tropical modes of interannual climate variability and their associated regional teleconnections beyond the range of internal variability. Section 4.3.3.2 assesses with medium confidence that there is no consensus from models for a systematic change in the amplitude of ENSO sea surface temperature variability over the 21st century. The related change in tropical SSTs is covered in Section 9.2.1.1. The projected changes in SST have implications for marine heat wave characteristics, which are assessed in Box 9.2. SST changes in the tropics are related to changes in the atmospheric circulation, including surface equatorial easterly trade winds and Walker Circulation (Section 4.5.3.2), and the weakening Indonesian Throughflow and strengthening Agulhas Extension and leakage (Section 9.2.3.4). Weakening trade winds under climate change (Vecchi and Soden, 2007) will tend to decrease upwelling, along isopycnals in the eastern Pacific and diapycnal upwelling in the central Pacific, and thus the meridional temperature gradients that drive tropical instability waves (Terada et al., 2020), along with a weakening, flattening and shoaling of the tropical thermocline and equatorial undercurrent (Luo and Rothstein, 2011). A weak or absent equatorial undercurrent (Kuntz and Schrag, 2020) and a too-diffuse and incorrectly sloped tropical thermocline (Zhu et al., 2020) remain issues in most CMIP6 models. In summary, while future changes in tropical modes of variability remain unclear, change in atmospheric and ocean circulation will drive continued change in tropical ocean temperature in the 21st century (medium confidence), with part of the region experiencing drastic marine heat wave conditions (high confidence).

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The SROCC (Collins et al., 2019) concluded with high confidence that Indonesian Throughflow (ITF) transport from the Pacific Ocean to the Indian Ocean has increased in the past two decades as a result (medium confidence) of an unprecedented intensification of the equatorial Pacific trade wind system. Section 2.3.3.4 assesses that there is high confidence that the increase in the ITF over the past two decades is linked to multi-decadal scale variability rather than a longer-term trend. Consistently, in the future, as winds change under increased radiative forcing, most models project a decline of the ITF on the centennial time scale (Figure 9.11). One of the clearest changes of ocean current transport simulated by climate models is a weakening of the Indonesian Throughflow, projected in CMIP5 simulations under RCP4.5 and RCP8.5 scenarios (Sen Gupta et al., 2016; Stellema et al., 2019), and in CMIP6 simulations under the SSP5-8.5 scenario (high confidence, Figure 9.11).

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Shelf-deep ocean exchanges involve eddying, tidal, or turbulent motions and small-scale topography such as submarine canyons; high-resolution observations and models are needed to capture these effects (Greenberg et al., 2007; Capet et al., 2008; Allen and Durrieu de Madron, 2009; Colas et al., 2012; Trotta et al., 2017). Example coastal processes that introduce uncertainty into large-scale projections are exchange of CDW across the Antarctic shelf-break, which affects AABW formation and Antarctic ice-shelf–ocean interaction (Sections 9.2.2.3 and 9.2.3.2; Stewart and Thompson, 2013, 2015), river and estuarine plumes and their responses to water level and hydrology change (Banas et al., 2009; Sun et al., 2017), fjord dynamics linked to glacial outflows (Straneo and Cenedese, 2015; Torsvik et al., 2019), and changing formation of water masses in marginal seas (Kim et al., 2001; Greene and Pershing, 2007; Giorgi and Lionello, 2008; Renner et al., 2009). Downscaling projections to the local level allows process detail (Foreman et al., 2014; Mathis and Pohlmann, 2014; Meier, 2015; Tinker et al., 2016). Some processes can only be simulated when coastal models are forced by larger-scale models of the atmosphere, cryosphere, or hydrosphere (Seo et al., 2007, 2008; Somot et al., 2008; Oerder et al., 2015; Renault et al., 2016; Y. Zhang et al., 2016; Wåhlin et al., 2020), including the addition of tides (Janeković and Powell, 2012; Timko et al., 2013; Tinker et al., 2015; Pickering et al., 2017; Hausmann et al., 2020). Due to coastal process complexity and small scale, linking the effects of coastal ocean changes to global ocean changes requires high-resolution modelling (Holt et al., 2017, 2018), two-way nesting, or local mesh refinement (Fringer et al., 2006; Zhang and Baptista, 2008; Mason et al., 2010; Dietrich et al., 2012; Hellmer et al., 2012; Ringler et al., 2013; Q. Wang et al., 2014; Zängl et al., 2015; Y.J. Zhang et al., 2016; Soto-Navarro et al., 2020). Coarse climate models and HighResMIP models do not represent some coastal phenomena such as cross-shelf exchanges and sub-mesoscale eddies, which require 1 km or finer resolution. Thus, there is low confidence in projecting centennial scale coastal climate change where regional downscaling or refinement is lacking. There is high confidence in the ability of regional coupled models to improve coastal climate change process understanding and provide regional information (Section 12.4), but many sites globally await such projections.

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Reconstructions of Arctic sea ice coverage put the satellite period changes into centennial context. Direct observational data coverage (Walsh et al., 2017) and model reconstructions (Brennan et al., 2020) warrant high confidence that the low Arctic sea ice area of summer 2012 is unprecedented since 1850, and that the summer sea ice loss is significant in all Arctic regions except for the Central Arctic (Cai et al., 2021). Direct winter observational data coverage before 1953 is too sparse to reliably assess Arctic sea ice area. Since 1953, the years 2015 to 2018 had the four lowest values of maximum Arctic sea ice area, which usually occurs in March (high confidence) (Figure 2.20). Reconstructions of Arctic sea ice area before 1850 remain sparse, and as in SROCC, there remains medium confidence that the current sea ice levels in late summer are unique during the past 1 kyr (Section 2.3.2.1.1; Kinnard et al., 2011; De Vernal et al., 2013b).

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The analysis and understanding of the long-term evolution of the Antarctic sea ice cover is hindered by the scarcity of observational records before the satellite period, and the scarcity of paleorecords (see Section 2.3.2.1.2 for further details). Such long records are particularly relevant given that the Southern Ocean response to external forcing takes longer than the length of the available direct observational record (Goosse and Renssen, 2001; Armour et al., 2016). There is only limited evidence for large-scale decadal fluctuations in sea ice coverage caused by large-scale temperature and wind forcing. Sparse direct pre-satellite observations suggest a decrease in sea ice coverage from the 1950s to the 1970s (Fan et al., 2014). Paleo-proxy data indicate that, on multi-decadal to multi-centennial time scales, sea ice coverage of the Southern Ocean follows large-scale temperature trends (e.g., Crosta et al., 2018; Chadwick et al., 2020; Lamping et al., 2020), for example linked to fluctuations in the El Niño–Southern Oscillation and Southern Annular Mode (Crosta et al., 2021), and that during the Last Glacial Maximum, Antarctic sea ice extended to about the polar front latitude in most regions during winter, whereas the extent during summer is less well understood (e.g., Benz et al., 2016; Xiao et al., 2016; Nair et al., 2019).

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Regionally, proxy data from ice cores consistently indicate that the increase of sea ice area in the Ross Sea and the decrease of sea ice area in the Bellingshausen Sea are part of longer centennial trends and exceed internal variability on multi-decadal time scales (medium confidence) (e.g., Thomas et al., 2019; Tesi et al., 2020). These centennial trends are consistent with simulations from CMIP5 models (Hobbs et al., 2016b; J.M. Jones et al., 2016; Kimura et al., 2017).

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The role of the elevation–mass feedback for future projections of Greenland can be assessed from paleo simulations. Ice-sheet model simulations of the Laurentide (Gomez et al., 2015; Gregoire et al., 2016) and Eurasian (Alvarez-Solas et al., 2019) ice sheets invoke at least some contribution to last glacial termination mass loss from SMB reduction, as a consequence of an elevation–mass balance feedback (Levermann and Winkelmann, 2016). In a model spanning Meltwater Pulse 1A, this mechanism increased mass loss by approximately 66% (Gregoire et al., 2016) but in Last Interglacial simulations, the effect of this feedback is shown to depend on the surface scheme of the climate model employed (Plach et al., 2019). Given the agreement between theoretical analyses and paleo-ice-sheet model experiments, there is high confidence that the elevation–mass balance feedback is most relevant at multi-centennial and millennial time scales, consistent with future-focused studies (Aschwanden et al. 2019, Le Clec’h et al., 2019, Gregory et al., 2020).

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The SROCC noted limited evidence from geological records and ice-sheet modelling, suggesting that parts of the AIS experienced rapid (centennial) retreat likely due to MISI between 20,000 and 9,000 years ago, and also described more uncertain evidence for the Last Interglacial (LIG) and mid-Pliocene Warm Period (MPWP). Recent support for past MISI is provided by model simulations of the WAIS during the LIG (Clark et al., 2020), the British Ice Sheet during the last termination (Gandy et al., 2018) and the Laurentide Ice Sheet during the Younger Dryas (Pico et al., 2019), which show progressive retreat despite declining temperatures, indicative of a true (ice dynamic) instability. Direct observational evidence of rapid paleo ice-sheet grounding line retreat is rare but, on the Larsen continental shelf, retreat rates of >10 km yr–1during the deglaciation have been estimated (Dowdeswell et al., 2020). MISI has also been inferred from sedimentological evidence of ice loss from Wilkes Subglacial Basin, East Antarctica (Bertram et al., 2018; Wilson et al., 2018; Blackburn et al., 2020) but these reconstructions cannot unambiguously identify unstable from progressive retreat. Therefore, there is limited evidence to identify the operation of instability mechanisms such as MISI in paleo ice-sheet retreat.

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The SROCC assessed that ice-sheet interactions with the solid Earth are not expected to substantially slow sea level rise from marine-based ice in Antarctica over the 21st century (medium confidence), but that these processes could become important on multi-century and longer time scales. More recent modelling of deglaciation of the Ross Embayment by Lowry et al. (2020) is consistent with this assessment. However, new projections for Pine Island Glacier (Kachuck et al., 2020) support previous work (Barletta et al., 2018) suggesting that lower mantle viscosity in this region leads to a negative feedback on decadal time scales. Grounding line stabilization by the solid Earth response may therefore occur over the 21st century in the Amundsen Sea Embayment, where most mass loss is occurring (Section 9.4.2.1), but more generally occurs over multi-centennial to millennial time scales (medium confidence).

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Section 2.3.2.3 assesses that the rate and global character of glacier retreat in the latter part of 20th century, and finds that the first decades of the 21st century appear to be unusual in the context of the Holocene (medium confidence) and the global glacier recession in the beginning of the 21st century to be unprecedented in the last 2000 years (medium confidence). These assessments are supported by regional evidence. New reconstructions of the Patagonian Ice Sheet suggest that 20th-century glacial recession occurred faster than at any time during the Holocene (Davies et al., 2020). The reconstructions of glacier variations show that the glaciers in some regions are now smaller than previously recorded: since the mid-16th century in the Mont Blanc and Grindelwald regions of the European Alps (Nussbaumer and Zumbühl, 2012), since the 9th century in Norway (Nesje et al., 2012), and for the past 1800 years in north-west Iceland (Harning et al., 2016, 2018). In Arctic Canada and Svalbard, many glaciers are now smaller than they have been in at least 4000 years (Lowell et al., 2013; Miller et al., 2013, 2017; Schweinsberg et al., 2017, 2018) and more than 40,000 years in Baffin Island (Pendleton et al., 2019). Although the millennial glacier length variation records are incomplete and discontinuous, and glacier fluctuations depend on multiple factors (e.g., temperature, precipitation, topography, internal glacial dynamics), there is a coherent relationship between rising temperatures, negative mass balance and glacier retreat on centennial time scales across most of the world. Glaciological and geodetic observations show that the rates of early 21st-century mass loss are the highest since 1850 (Zemp et al., 2015). For all regions with long-term observations, glacier mass in the decade 2010–2019 was the smallest since at least the beginning of the 20th century (medium confidence).

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Seasonal snow cover, by definition, has a clear annual cycle with usually complete disappearance in spring and summer and re-formation in autumn or winter. Therefore, there is very high confidence that the current and projected changes to seasonal snow cover are reversible (Verfaillie et al., 2018). In the case of global or regional cooling, abrupt large-scale snow-cover changes, with a transition from seasonal to persistent snow cover due to a strong snow-albedo feedback, are a typical feature of glacial inceptions (e.g., Baum and Crowley, 2003; Calov et al., 2005), and these can be irreversible on centennial or longer time scales because of this feedback. In summary, based on physical understanding and the absence of occurrence of such events in climate model projections, abrupt future changes of seasonal snow cover on large scales in the absence of concomitant abrupt atmospheric change as a driver appear very unlikely in the context of current and projected warming.

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The SROCC (Oppenheimer et al., 2019) reported variations in storm surge not related to changes in RSL, and concluded with high confidence that consideration of localized storm surge processes was essential to monitor trends in ESL. SL events driven by storm surge are a response to tropical and extratropical cyclones. While historical trends in extra-tropical cyclones are less clear (Section 11.7.2.1), there is mounting evidence for an increasing proportion of stronger tropical cyclones globally, with an associated poleward migration (Section 11.7.1.2). These changes are captured in the ESL record, for example, via increasing intensity and poleward shift in the location of typhoon-driven storm surges reported across 64 years (1950–2013) in the western North Pacific (Oey and Chou, 2016). Along the east coast of the USA, there has been an increase in frequency of ESL events due to tropical cyclone changes since 1923 that can be statistically linked to changes in global average temperature (Grinsted et al., 2013), and the signal is projected to emerge around 2030 (Lee et al., 2017). At century and longer time scales, geological proxies such as overwash deposits in coastal lagoons or sinkholes can be used to reconstruct past changes in storm activity (e.g., Brandon et al., 2013; Lin et al., 2014) and put recent events into historical perspective (e.g., Brandon et al., 2015). However, there is low confidence in the current ability to quantitatively compare geological proxies with gauge data. Historical storm surge activity is being increasingly assessed with use of hydrodynamic model simulations and data-driven global reconstructions to supplement tide gauge observations to investigate historical changes at centennial to millennial time scales (e.g., Ji et al., 2020; Muis et al., 2020; Tadesse et al., 2020). Large regional variations and limited observational data lead to low confidence in observed trends in the surge contribution to increasing ESL.

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Bulthuis, K., M. Arnst, S. Sun, and F. Pattyn, 2019: Uncertainty quantification of the multi-centennial response of the Antarctic ice sheet to climate change. Cryosphere, 13, 1349–1380, doi: 10.5194/tc-13-1349-2019.

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Chan, P. et al., 2017: Multicentennial record of Labrador Sea primary productivity and sea-ice variability archived in coralline algal barium. Nature Communications, 8, 15543, doi: 10.1038/ncomms15543.

centennialresources/ipcc/cleaned_content/wg1/Chapter09/html_with_ids.html#references_p554

Hörner, T., R. Stein, and K. Fahl, 2017: Evidence for Holocene centennial variability in sea ice cover based on IP25biomarker reconstruction in the southern Kara Sea (Arctic Ocean). Geo-Marine Letters, 37(5), 515–526, doi: 10.1007/s00367-017-0501-y.

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Khan, S.A. et al., 2020: Centennial response of Greenland’s three largest outlet glaciers. Nature Communications, 11(1), 5718, doi: 10.1038/s41467-020-19580-5.

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Rae, J.W.B. et al., 2018: CO2 storage and release in the deep Southern Ocean on millennial to centennial timescales. Nature, 562(7728), 569–573, doi: 10.1038/s41586-018-0614-0.

centennialresources/ipcc/cleaned_content/syr/longer-report/html_with_ids.html#4.3_p2

Global warming will continue to increase in the near term (20212040) mainly due to increased cumulative CO2 emissions in nearly all considered scenarios and pathways. In the near term, every region in the world is projected to face further increases in climate hazards (medium tohigh confidence , depending on region and hazard), increasing multiple risks to ecosystems and humans (very high confidence). In the near term, natural variability 149 will modulate human-caused changes, either attenuating or amplifying projected changes, especially at regional scales, with little effect on centennial global warming. Those modulations are important to consider in adaptation planning. Global surface temperature in any single year can vary above or below the long-term human-induced trend, due to natural variability. By 2030, global surface temperature in any individual year could exceed 1.5°C relative to 1850–1900 with a probability between 40% and 60%, across the five scenarios assessed in WGI (medium confidence). The occurrence of individual years with global surface temperature change above a certain level does not imply that this global warming level has been reached. If a large explosive volcanic eruption were to occur in the near term 150 , it would temporarily and partially mask human-caused climate change by reducing global surface temperature and precipitation, especially over land, for one to three years (medium confidence). {WGI SPM B.1.3, WGI SPM B.1.4, WGI SPM C.1, WGI SPM C.2, WGI Cross-Section Box TS.1, WGI Cross-Chapter Box 4.1; WGII SPM B.3, WGII SPM B.3.1; WGIII Box SPM.1 Figure 1}

centennialresources/ipcc/cleaned_content/wg2/Chapter03/html_with_ids.html#references_p515

Dupont, N. and D.L. Aksnes, 2013: Centennial changes in water clarity of the Baltic Sea and the North Sea. Estuar. Coast. Shelf Sci. , 131, 282–289, doi:10.1016/j.ecss.2013.08.010.

centennialresources/ipcc/cleaned_content/wg2/Chapter03/html_with_ids.html#references_p790

Hallin, C., M. Larson and H. Hanson, 2019: Simulating beach and dune evolution at decadal to centennial scale under rising sea levels. PLoS ONE, 14 (4), e0215651, doi:10.1371/journal.pone.0215651.

centennialresources/ipcc/cleaned_content/wg2/Chapter03/html_with_ids.html#references_p1285

McCoy, S.J., et al., 2018: A mineralogical record of ocean change: decadal and centennial patterns in the California mussel. Glob. Change Biol. , 24 (6), 2554–2562, doi:10.1111/gcb.14013.

centennialresources/ipcc/cleaned_content/wg2/Chapter10/html_with_ids.html#references_p69

Ali, J., et al., 2018: Centennial heat wave projections over Pakistan using ensemble NEX GDDP data set. Earth Syst. Environ. , 2 (3), 437–454.

centennialresources/ipcc/cleaned_content/wg2/Chapter12/html_with_ids.html#references_p1347

Saurral, R.I., I. A. Camilloni and V.R. Barros, 2017: Low-frequency variability and trends in centennial precipitation stations in southern South America. Int. J. Climatol. , 37 (4), 1774–1793, doi:10.1002/joc.4810.

centennialresources/ipcc/cleaned_content/wg3/Chapter11/html_with_ids.html#references_p382

Liu, G. and D. B. Müller, 2013: Centennial evolution of aluminum in-use stocks on our aluminized planet. Environ. Sci. Technol. , 47(9) , 4882–4888, doi:10.1021/es305108p.

centennialresources/ipcc/cleaned_content/wg3/Chapter12/html_with_ids.html#12.3.1.3_p2

One natural mechanism of carbon transfer from the atmosphere to the deep ocean is the ocean biological pump, which is driven by the sinking of organic particles from the upper ocean. These particles derive ultimately from primary production by phytoplankton and most of them are remineralised within the upper ocean with only a small fraction reaching the deep ocean where the carbon can be sequestered on centennial and longer timescales . Increasing nutrient availability would stimulate uptake of CO2 through phytoplankton photosynthesis producing organic matter, some of which would be exported into the deep ocean, sequestering carbon. In areas of the ocean where macronutrients (nitrogen, phosphorus) are available in sufficient quantities (about 25% of the total area), the growth of phytoplankton is limited by the lack of trace elements such as iron. Thus, OF CDR can be based on two implementation options to increase the productivity of phytoplankton (Minx et al. 2018): macronutrient enrichment and micronutrient enrichment. A third option, highlighted in GESAMP (2019), is based on fertilisation for fish stock enhancement, for instance, as naturally occurs in eastern boundary current systems. Iron fertilisation is the best-studied OF option to date, but knowledge so far is still inadequate to predict global ecological and biogeochemical consequences.

centennialresources/ipcc/syr/longer-report/html_with_ids.html#4.3_p2

Global warming will continue to increase in the near term (20212040) mainly due to increased cumulative CO2 emissions in nearly all considered scenarios and pathways. In the near term, every region in the world is projected to face further increases in climate hazards (medium tohigh confidence , depending on region and hazard), increasing multiple risks to ecosystems and humans (very high confidence). In the near term, natural variability 149 will modulate human-caused changes, either attenuating or amplifying projected changes, especially at regional scales, with little effect on centennial global warming. Those modulations are important to consider in adaptation planning. Global surface temperature in any single year can vary above or below the long-term human-induced trend, due to natural variability. By 2030, global surface temperature in any individual year could exceed 1.5°C relative to 1850–1900 with a probability between 40% and 60%, across the five scenarios assessed in WGI (medium confidence). The occurrence of individual years with global surface temperature change above a certain level does not imply that this global warming level has been reached. If a large explosive volcanic eruption were to occur in the near term 150 , it would temporarily and partially mask human-caused climate change by reducing global surface temperature and precipitation, especially over land, for one to three years (medium confidence). {WGI SPM B.1.3, WGI SPM B.1.4, WGI SPM C.1, WGI SPM C.2, WGI Cross-Section Box TS.1, WGI Cross-Chapter Box 4.1; WGII SPM B.3, WGII SPM B.3.1; WGIII Box SPM.1 Figure 1}

aerosolsresources/ipcc/cleaned_content/wg1/Chapter05/html_with_ids.html#5.6.3.1_p2

SRM-mediated sunlight changes directly affect the carbon cycle. In particular, SAI would reduce the sunlight reaching the Earth’s surface, but also increase the fraction of sunlight that is diffuse. These changes in the quantity and quality of the sunlight have opposing effects on the photosynthesis of land plants. On their own, reductions in photosynthetically active radiation (PAR) will reduce photosynthesis. However, diffuse light is more effective than direct light in accessing the light-limited leaves within plant canopies, leading to the so-called ‘diffuse-radiation’ fertilization effect (Mercado et al., 2009). The estimated balance between the negative impacts of reducing PAR and the positive impacts of increasing diffuse fraction differ between models (Kalidindi et al., 2015; Xia et al., 2016; C.-E. Yang et al., 2020) and across different ecosystems. The change in the absolute amount of direct and diffuse radiation could also depend on the height of the additional sulphate aerosol layer in the stratosphere and the hygroscopic growth of aerosols (Krishnamohan et al., 2019, 2020).

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Kalidindi, S., G. Bala, A. Modak, and K. Caldeira, 2015: Modeling of solar radiation management: a comparison of simulations using reduced solar constant and stratospheric sulphate aerosols. Climate Dynamics, 44(9–10), 2909–2925, doi: 10.1007/s00382-014-2240-3.

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Kalliokoski, T. et al., 2020: Mitigation Impact of Different Harvest Scenarios of Finnish Forests That Account for Albedo, Aerosols, and Trade-Offs of Carbon Sequestration and Avoided Emissions. Frontiers in Forests and Global Change, 3, 112, doi: 10.3389/ff gc.2020.562044.

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Krishnamohan, K.-P.S.-P., G. Bala, L. Cao, L. Duan, and K. Caldeira, 2019: Climate system response to stratospheric sulfate aerosols: sensitivity to altitude of aerosol layer. Earth System Dynamics, 10(4), 885–900, doi: 10.5194/esd-10-885-2019.

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Krishnamohan, K.-P.S.-P., G. Bala, L. Cao, L. Duan, and K. Caldeira, 2020: The Climatic Effects of Hygroscopic Growth of Sulfate Aerosols in the Stratosphere. Earth’s Future, 8(2), e2019EF001326, doi: 10.1029/2019ef001326.

albedoresources/ipcc/cleaned_content/wg1/Chapter02/html_with_ids.html#2.2.2_p4

Direct observations of volcanic gas-phase sulphur emissions (mostly SO2), sulphate aerosols, and their radiative effects are available from a variety of sources (Kremser et al., 2016). New estimates of SO2 emissions from explosive eruptions have been derived from satellite (beginning in 1979) and in situ measurements (Höpfner et al., 2015; Carn et al., 2016; Neely III and Schmidt, 2016; Brühl, 2018). Satellite observations of aerosol extinction after recent eruptions have uncertainties of about 15–25% (Vernier et al., 2011; Bourassa et al., 2012). Additional uncertainties occur when gaps in the satellite records are filled by complementary observations or using statistical methods (Thomason et al., 2018). Merged datasets (Thomason et al., 2018) and sparse ground-based measurements (Stothers, 1997) allow for volcanic forcing estimates back to 1850. In contrast to the CMIP5 historical volcanic forcing datasets (Ammann et al., 2003), updated time series (Figure 2.2d; Luo, 2018) feature a more comprehensive set of optical properties including latitude-, height- and wavelength-dependent aerosol extinction, single scattering albedo and asymmetry parameters. A series of small-to-moderate eruptions since 2000 resulted in perturbations in SAOD of 0.004–0.006 (Andersson et al., 2015; Schmidt et al., 2018).

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To conclude, atmospheric aerosols sampled by ice cores, influenced by northern mid-latitude emissions, show positive trends from 1700 until the last quarter of the 20th century and decreases thereafter (high confidence), but there is low confidence in observations of systematic changes in other parts of the world in these periods. Satellite data and ground-based records indicate that AOD exhibits predominantly negative trends since 2000 over NH mid-latitudes and SH continents, but increased over South Asia and East Africa (high confidence). A globally deceasing aerosol abundance is thus assessed with medium confidence. This implies increasing net positive ERF, since the overall negative aerosol ERF has become smaller.

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The AR5 concluded that changes in climate drivers over the industrial period corresponded to a positive ERF which increased more rapidly after 1970 than before. There was very high confidence in the positive ERF due to WMGHG, with CO2 the single largest contributor. The AR5 concluded that there was high confidence that aerosols have offset a substantial portion of the WMGHG forcing.

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The net effect of aerosols (Sections 2.2.6 and 6.4) on the radiation budget, including their effect on clouds, and cloud adjustments, as well as the deposition of black carbon on snow (Section 7.3.4.3), was negative throughout the industrial period (high confidence). The net effect strengthened (becoming more negative) over most of the 20th century, butmore likely than not weakened (becoming less negative) since the late 20th century. These trends are reflected in measurements of surface solar radiation (Section 7.2.2.3) and the Earth’s energy imbalance (Section 7.2.2.1). The relative importance of aerosol forcing compared to other forcing agents has decreased globally in the most recent 30 years (medium confidence) and the reduction of the negative forcing in the 21st century enhances the overall positive ERF.

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Booth, B.B.B., N.J. Dunstone, P.R. Halloran, T. Andrews, and N. Bellouin, 2012: Aerosols implicated as a prime driver of twentieth-century North Atlantic climate variability. Nature, 484(7393), 228–232, doi: 10.1038/nature10946.

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Dahutia, P., B. Pathak, and P.K. Bhuyan, 2018: Aerosols characteristics, trends and their climatic implications over Northeast India and adjoining South Asia. International Journal of Climatology, 38(3), 1234–1256, doi: 10.1002/joc.5240.

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Sogacheva, L. et al., 2018: Spatial and seasonal variations of aerosols over China from two decades of multi-satellite observations – Part 1: ATSR (1995–2011) and MODIS C6.1 (2000–2017). Atmospheric Chemistry and Physics, 18(15), 11389–11407, doi: 10.5194/acp-18-11389-2018.

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Stjern, C.W., A. Stohl, and J.E. Kristjánsson, 2011: Have aerosols affected trends in visibility and precipitation in Europe?Journal of Geophysical Research: Atmospheres, 116(D2), D02212, doi: 10.1029/2010jd014603.

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CMIP5 models reproduce the mean state of the Walker circulation with reasonable fidelity, evidenced by the spatial pattern correlations of equatorial zonal mass stream function between models and observations being larger than 0.88 (Ma and Zhou, 2016). CMIP5 historical simulations on average simulate a significant weakening of the Pacific Walker circulation over the 20th century (DiNezio et al., 2013; Sandeep et al., 2014; Kociuba and Power, 2015), which is also seen in CMIP6 (Figure 3.16d). This weakening is accompanied by a reduction of convective activity over the Maritime Continent and an enhancement over the central equatorial Pacific (DiNezio et al., 2013; Sandeep et al., 2014; Kociuba and Power, 2015). In the CMIP6 simulations, greenhouse gas forcing induces this weakening (Figure 3.16d), which is consistent with theories based on radiative-convective equilibrium (Vecchi et al., 2006; Vecchi and Soden, 2007) and thermodynamic air-sea coupling (Xie et al., 2010), but inconsistent with a theory highlighting the ocean dynamical effect which suggests a strengthening in response to greenhouse gas increases (Clement et al., 1996; Seager et al., 2019; see also Section 7.4.4.2.1). Seager et al. (2019) attributed this inconsistency to equatorial Pacific SST biases in the models (Section 3.5.1.2.1). However, observational and reanalysis datasets disagree on the sign of trends in the Walker Circulation strength over the 1901–2010 period (Figure 3.16d), and Section 2.3.1.4.1 assesses low confidence in observed long-term Walker Circulation trends. The observational uncertainty remains high in the trends since the 1950s (Tokinaga et al., 2012; L’Heureux et al., 2013), though both CMIP5 and CMIP6 historical simulations span trends of all but one observational data set (Figure 3.16e). For this period, external influence simulated in CMIP6 is insignificant due to a partial compensation of forced responses to greenhouse gases and aerosols and large internal decadal variability (Figure 3.16e). It is notable that while AMIP simulations on average show strengthening over both the periods, those simulations are forced by one reconstruction of SST, which itself is subject to uncertainty before the 1970s (Deser et al., 2010; Tokinaga et al., 2012).

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Factors affecting model performance include resolution, the type of dynamical core (spectral, finite difference or finite volume), physics parameters and parameterisations, model structure, for example, many of the coupled HighResMIP models (Haarsma et al., 2016) use the NEMO ocean model, affecting model diversity, and the range and degree of process realism (e.g., for aerosols, atmospheric chemistry and other Earth System components). This section particularly explores the influence of model resolution and of complexity on model performance (see also Section 8.5.1).

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In CMIP6 a number of Earth system models have increased the realism by which key biogeochemical aspects of the coupled Earth system are represented, affecting, for example, the carbon and nitrogen cycles, aerosols, and atmospheric chemistry (e.g., Cao et al., 2018; Gettelman et al., 2019; Lin et al., 2019; Mauritsen et al., 2019; Séférian et al., 2019; Sellar et al., 2019; Sidorenko et al., 2019; Swart et al., 2019; Dunne et al., 2020; Seland et al., 2020; Wu et al., 2020; Ziehn et al., 2020). In addition to increased process realism, the level of coupling between the physical climate and biogeochemical components of the Earth system has also been enhanced in some models (Mulcahy et al., 2020) as well as across different biogeochemical components (see Section 5.4 for a discussion and Table 5.4 for an overview). For example, the nitrogen cycle is now simulated in several ESMs (Zaehle et al., 2015; Davies-Barnard et al., 2020). This advance accounts for the fertilization effect nitrogen availability has on vegetation and carbon uptake, reducing uncertainties in the simulations of the carbon uptake responses to physical climate change (Section 3.6.1) and to CO2 increases (Arora et al., 2020), thus improving confidence in the simulated airborne fraction of CO2 emissions (Jones and Friedlingstein, 2020) and better constraining remaining carbon budgets (Section 5.5). Such advances also allow investigation of land-based climate change mitigation options (e.g., through changes in land management and associated terrestrial carbon uptake (Mahowald et al., 2017; Pongratz et al., 2018)) or interactions between different facets of the managed Earth system, such as interactions between mitigation efforts targeting climate warming and air quality (West et al., 2013). A number of developments also explicitly target improved simulation of the past.

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Amiri-Farahani, A., R.J. Allen, K.F. Li, and J.E. Chu, 2019: The Semidirect Effect of Combined Dust and Sea Salt Aerosols in a Multimodel Analysis. GeophysicalResearch Letters, 46(17–18), 10512–10521, doi: 10.1029/2019gl084590.

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Ayash, T., S. Gong, and C.Q. Jia, 2008: Direct and Indirect Shortwave Radiative Effects of Sea Salt Aerosols. Journal of Climate, 21(13), 3207–3220, doi: 10.1175/2007jcli2063.1.

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Boo, K.-O. et al., 2015: Influence of aerosols in multidecadal SST variability simulations over the North Pacific. Journal of Geophysical Research: Atmospheres, 120(2), 517–531, doi: 10.1002/2014jd021933.

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Booth, B.B.B., N.J. Dunstone, P.R. Halloran, T. Andrews, and N. Bellouin, 2012: Aerosols implicated as a prime driver of twentieth-century North Atlantic climate variability. Nature, 484(7393), 228–232, doi: 10.1038/nature10946.

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Chylek, P., C. Folland, J.D. Klett, and M.K. Dubey, 2020: CMIP5 Climate Models Overestimate Cooling by Volcanic Aerosols. Geophysical Research Letters, 47(3), e2020GL087047, doi: 10.1029/2020gl087047.

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Collins, W.J. et al., 2017: AerChemMIP: quantifying the effects of chemistry and aerosols in CMIP6. Geoscientific Model Development, 10(2), 585–607, doi: 10.5194/gmd-10-585-2017.

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Dong, L., T. Zhou, and X. Chen, 2014a: Changes of Pacific decadal variability in the twentieth century driven by internal variability, greenhouse gases, and aerosols. Geophysical Research Letters, 41(23), 8570–8577, doi: 10.1002/2014gl062269.

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Giannini, A. and A. Kaplan, 2019: The role of aerosols and greenhouse gases in Sahel drought and recovery. Climatic Change, 152(3–4), 449–466, doi: 10.1007/s10584-018-2341-9.

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Jiang, B., D. Wang, X. Shen, J. Chen, and W. Lin, 2019: Effects of sea salt aerosols on precipitation and upper troposphere/lower stratosphere water vapour in tropical cyclone systems. Scientific Reports, 9(1), 15105, doi: 10.1038/s41598-019-51757-x.

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Murphy, D.M., 2013: Little net clear-sky radiative forcing from recent regional redistribution of aerosols. Nature Geoscience, 6, 258, doi: 10.1038/ngeo1740.

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Undorf, S., M.A. Bollasina, B.B.B. Booth, and G.C. Hegerl, 2018a: Contrasting the Effects of the 1850–1975 Increase in Sulphate Aerosols from North America and Europe on the Atlantic in the CESM. Geophysical Research Letters, 45(21), 11930–11940, doi: 10.1029/2018gl079970.

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Wang, Z., L. Bi, B. Yi, and X. Zhang, 2019: How the Inhomogeneity of Wet Sea Salt Aerosols Affects Direct Radiative Forcing. Geophysical Research Letters, 46(3), 1805–1813, doi: 10.1029/2018gl081193.

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Zhang, R. et al., 2013a: Have Aerosols Caused the Observed Atlantic Multidecadal Variability?Journal of the Atmospheric Sciences, 70(4), 1135–1144, doi: 10.1175/jas-d-12-0331.1.

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This chapter draws on model simulations from CMIP6 (Eyring et al., 2016) using a new range of scenarios based on Shared Socio-economic Pathways (SSPs; O’Neill et al., 2016). The set of SSPs is described in detail in Chapter 1 (Section 1.6) and recognizes that global radiative forcing levels can be achieved by different pathways of CO2, non-CO2 greenhouse gases (GHGs), aerosols (Amann et al., 2013; Rao et al., 2017) and land use; the set of SSPs therefore establishes a matrix of global forcing levels and socio-economic storylines. ScenarioMIP (O’Neill et al., 2016) identifies four priority (tier-1) scenarios that participating modelling groups are asked to perform, SSP1-2.6 for sustainable pathways, SSP2-4.5 for middle-of-the-road, SSP3-7.0 for regional rivalry, and SSP5-8.5 for fossil fuel-rich development. This chapter focuses its assessment on these, plus the SSP1-1.9 scenario, which is directly relevant to the assessment of the 1.5°C Paris Agreement goal. Further, this chapter discusses these scenarios and their extensions past 2100 in the context of the very long-term climate change in Section 4.7.1. Projections of short-lived climate forcers (SLCFs) are assessed in more detail in Chapter 6 (Section 6.7).

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ScenarioMIP simulations include advances in techniques to better harmonize with historical forcings relative to CMIP5. For example, projected changes in the solar cycle include long-term modulation rather than a repeating solar cycle (Matthes et al., 2017). Background natural aerosols are ramped down to an average historical level used in the control simulation by 2025 and background volcanic forcing is ramped up from the value at the end of the historical simulation period (2015) over 10 years to the same constant value prescribed for the pre-industrial control (piControl) simulations in the DECK, and then kept fixed – both changes are intended to avoid inconsistent model treatment of unknowable natural forcing to affect the near-term projected warming.

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Complete backward comparability between CMIP5 and CMIP6 scenarios cannot be established for detailed regional assessments, because the SSP scenarios include regional forcings – especially from land use and aerosols – that are different from the CMIP5 RCPs. Even at a global level, a quantitative comparison is challenging between corresponding SSP and RCP radiative forcing levels due to differing contributions to the forcing (Meinshausen et al., 2020) and evidence of differing model responses (Section 4.6.2.2; Wyser et al., 2020). The RCP scenarios assessed in AR5 all showed similar, rapid reductions in SLCFs and emissions of SLCF precursor species over the 21st century; the CMIP5 projections hence did not sample a wide range of possible trajectories for future SLCFs (Chuwah et al., 2013). The SSP scenarios assessed in the AR6 offer more scope to explore SLCF pathways as they sample a broader range of air quality policy options (Gidden et al., 2019) and relationships of CO2 to non-CO2 greenhouse gases (Meinshausen et al., 2020). Section 4.6.2.2 assesses RCP and SSP differences. Other MIPs (see Section 4.2.1) have been designed to explicitly explore some of the implications of the different socio-economic storylines for a given radiative forcing level.

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Further developments of the AR5 approach have since explored the role of aerosols in modifying regional climate responses at a specific degree of global warming and also the effect of different GCMs and scenarios on the scaled spatial patterns (Frieler et al., 2012; Levy et al., 2013). Furthermore, the modified forcing-response framework (Kamae and Watanabe, 2012, 2013; Sherwood et al., 2015), which decomposes the total climate change into fast adjustments and slow response, identifies the fast adjustment as forcing-dependent and the slow response as forcing-independent, scaling with the change in GSAT.

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Uncertainties in emissions of greenhouse gases and aerosols that affect future radiative forcings are represented by selected SSP scenarios (Sections 1.6.1 and 4.2.2). In addition to emission uncertainties, SSPs represent uncertainties in land use changes (van Vuuren et al., 2011; Ciais et al., 2013; O’Neill et al., 2016; Christensen et al., 2018). Additional uncertainty comes from climate carbon-cycle feedbacks and the residence time of atmospheric constituents, and are at least partly accounted for in emissions-driven simulations as opposed to concentration-driven simulations (Friedlingstein et al., 2014; Hewitt et al., 2016). The climate carbon-cycle feedbacks affect the transient climate response to cumulative CO2 emissions (TCRE). Constraining this uncertainty is crucial for the assessment of remaining carbon budgets consistent with global mean temperature levels (Millar et al., 2017; IPCC, 2018a) and is covered in Chapter 5 of this Report. Finally, there are uncertainties in future solar and volcanic forcing (Cross-Chapter Box 4.1).

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The ERF patterns from aerosols and well-mixed GHGs are distinct (Chapter 7), and warming patterns therefore depend on the precise mix of forcing agents in the scenarios. The spatial efficacies – the change in surface temperature per unit ERF – for CO2, sulphate and black carbon aerosols and solar forcing have been recently evaluated in climate models (Modak et al., 2016, 2018; Duan et al., 2018; Modak and Bala, 2019; Richardson et al., 2019). On average, the spatial patterns of near-surface warming are largely similar for different external drivers (Xie et al., 2013; Richardson et al., 2019; Samset et al., 2020), despite the patterns of forcing being different and despite the large spread across different models (Richardson et al., 2019).

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Building on CMIP6 results for the effects of reducing SLCF emissions from a baseline of SSP3-7.0, the overall contribution of SLCFs to GSAT changes in the marker SSPs are now quantified using a simple climate model emulator. For consistency with Section 6.7.2 and Figure 6.22, the basket of SLCF compounds considered includes aerosols, ozone, methane, black carbon on snow and hydrofluorocarbons (HFCs) with lifetimes of less than 50 years. In the five marker SSPs considered, the net effect of SLCFs contributes to a higher GSAT in the near, mid- and long term (Table 4.6 and Section 6.7.2). In the SSP1-1.9 and SSP1-2.6 scenarios, SLCFs contribute to a higher GSAT by a central estimate of around 0.3°C compared to 1995–2014 across the three-time horizons. In the long-term, the 0.3C warming due to SLCFs in SSP1-2.6 can be compared to the assessed very likely GSAT change for this period of 0.5°C–1.5°C (Section 4.3.4 and Table 4.5). The SSP2-4.5, SSP3-7.0 and SSP5-8.5 scenarios all show a larger SLCF effect on GSAT in the long term relative to the near term. In SSP3-7.0, the long-term warming due to SLCFs by 0.7°C can be compared with the assessed very likely GSAT anomaly for this period of 2.0°C –3.7°C (Section 4.3.4). In summary, it is very likely that changes in SLCFs contribute to an overall warmer GSAT over the near, mid- and long term in the five SSP scenarios considered (Table 4.6, Section 6.7.2 and Figure 6.22).

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Due to the direct radiative effect of volcanic stratospheric aerosols, large volcanic eruptions lead to an overall decrease of GSAT, which can extend to multi-decadal or century time scales in the case of clustered volcanism (Section 3.3.1.1; Schurer et al., 2013; McGregor et al., 2015; Sigl et al., 2015; Kobashi et al., 2017; Zambri et al., 2017; Brönnimann et al., 2019; Neukom et al., 2019). Large eruptions also increase the frequency of extremely cold individual years and the likelihood of cooling trends occurring in individual decades (Cross-Chapter Box 3.1 and Section 4.4.4; Paik and Min, 2018). Re-dating of ice core chronologies now confirms that the coldest decades of the past approximately 2000 years are the outcome of volcanic eruptions (Sigl et al., 2015; Büntgen et al., 2016; Toohey et al., 2016; Neukom et al., 2019). CMIP5 and CMIP6 models reproduce the decreased GSAT that follows periods of intense volcanism. New reconciliations between simulations and proxy-based reconstructions of past eruptions have been achieved through better Earth System Model representation of volcanic plume chemical compositions (Legrande et al., 2016; Marshall et al., 2020; F. Zhu et al., 2020). Yet, remaining disagreements reflect differences in the volcanic forcing datasets used in the simulations (medium confidence) (Section 3.3.1.1 and Figure 3.2c).

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The SR1.5 stated low confidence regarding changes in global monsoons at 1.5°C versus 2°C of global warming, as well as differences in monsoon responses at 1.5°C versus 2°C. Generally, statistically significant changes in regional annual average precipitation are expected at a global mean warming of 2.5°C–3°C or more (Tebaldi et al., 2015). Over the austral-winter rainfall regions of south-western South America, South Africa and Australia, projected decreases in mean annual rainfall showhigh agreement across models and a strong climate change signal even under 1.5°C of global warming, with further amplification of the signal at higher levels of global warming (high confidence) (Mindlin et al., 2020). This is a signal evident in observed rainfall trends over these regions (Sections 2.3.1.3 and 8.3.1.6 ). Also, over the Asian monsoon regions, increases in rainfall will occur at 1.5°C and 2°C of global warming (Chevuturi et al., 2018). At warming levels of 1.5°C and 2°C, the changes in global monsoons are strongly dependent on the modelling strategies used, such as fully coupled transient, fully coupled quasi-equilibrium, and atmosphere-only quasi-equilibrium simulations. In particular, the differences of regional monsoon changes among model setups are dominated by strategy choices such as transient versus quasi-equilibrium set-up, prescription of SST, and treatment of aerosols (Zhang and Zhou, 2021).

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The radiative forcing labels on SSP and RCP scenarios is approximate and enables the multiple climate forcings within the scenario to be characterized by a single number. While the scenarios are similar in terms of the stratospheric adjusted radiative forcing (Tebaldi et al., 2021), they differ more in their effective radiative forcing (ERF). The combination of component forcings (CO2, non-CO2 greenhouse gases, aerosols) within the scenario also differ (Meinshausen et al., 2020). The ERF levels in the RCP and SSP scenarios have been calculated by sampling uncertainty in forcing from a range of different GHG species and aerosols (see 7.SM.1.4 for details). Figure 4.35 shows the time evolution and 2081–2100 mean across the families of scenarios and how this affects projections of GSAT. That the ERFs differ between corresponding SSP and RCP scenarios makes a comparison between CMIP6 and CMIP5 projections challenging (Tebaldi et al., 2021). Wyser et al. (2020) find the EC-Earth3-Veg model exhibits stronger radiative forcing and substantially greater warming under SSP5-8.5 than RCP8.5, and similar, but smaller additional warmings for SSP2-4.5and SSP1-2.6 compared with RCP4.5 and RCP2.6, respectively. In addition to the global response, climate can vary regionally due to non-CO2 components of forcing (Samset et al., 2016; Richardson et al., 2018a, b).

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Most SRM approaches, including stratospheric aerosol injection (SAI), marine cloud brightening (MCB), and surface albedo enhancements (Table 4.7), aim to cool the Earth by deflecting more solar radiation to space. Although cirrus cloud thinning (CCT) aims to cool the planet by increasing the longwave emission to space, it is included in the portfolio of SRM options (Table 4.7) for consistency with AR5 (Boucher et al., 2013) and SR1.5 (de Coninck et al., 2018). Other approaches such as injection of sulphate aerosols into the Arctic troposphere and sea ice albedo enhancements for moderatingregional warming have also been suggested (MacCracken, 2016; Field et al., 2018). As noted in SR1.5 (de Coninck et al., 2018), SRM is only considered as a potential supplement to deep mitigation, for example in overshoot scenarios (MacMartin et al., 2018).

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The cooling potential of SAI using sulphate aerosols depends on many factors (Visioni et al., 2017) including the amount of injection (Niemeier and Timmreck, 2015), aerosol microphysics (Krishnamohan et al., 2020), the spatial and temporal pattern of injection (Tilmes et al., 2017), response of stratospheric dynamics and chemistry (Richter Jadwiga et al., 2018), and aerosol effect on cirrus clouds (Visioni et al., 2018). A negative radiative forcing of a few W m–2 (ranging from one to eight W m–2) could be achieved depending on the amount and location of SO2 injected into the stratosphere (Aquila et al., 2014; Pitari et al., 2014; Niemeier and Timmreck, 2015; Kravitz et al., 2017; Kleinschmitt et al., 2018; Tilmes et al., 2018a). The simulated efficacy of SAI by emission of SO2 (radiative forcing per mass of injection rate) generally decreases with the increase in injection rate because of the growth of larger particles (about 0.5 microns) through condensation and coagulation reducing the mass scattering efficiency (Niemeier and Timmreck, 2015; Kleinschmitt et al., 2018). However, efficacy changes little for total injection rate up to about 25 Tg sulphur per year when SO2 is injected at multiple locations simultaneously (Kravitz et al., 2017; Tilmes et al., 2018a). Differences in model representation of aerosol microphysics, evolution of particle size, stratospheric dynamics and chemistry, and aerosol microphysics–radiation–circulation interactions all contribute to the uncertainty in simulated cooling efficiency of SAI. Compared to sulphate aerosols, injection of non-sulphate particles would result in different cooling efficacy, but understanding is limited (Pope et al.,2012; Weisenstein et al., 2015; A.C. Jones et al., 2016).

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Under present-day climate, cirrus clouds exerts a net positive radiative forcing of about 5 W m–2 (Gasparini and Lohmann, 2016; Hong et al., 2016), indicating a maximum cooling potential of the same magnitude if all cirrus cloud were removed from the climate system. However, modelling results show a much smaller cooling effect of CCT. For the optimal ice nuclei seeding concentration and globally non-uniform seeding strategy, a net negative cloud radiative forcing of about 1 to 2 W m–2 is achieved (Storelvmo and Herger, 2014; Gasparini et al., 2020). A few studies find that no seeding strategy could achieve a significant cooling effect, owing to complex microphysical mechanisms limiting robust climate responses to cirrus seeding (Penner et al., 2015; Gasparini and Lohmann, 2016). A higher than optimal concentration of ice nucleating particles could also result in over-seeding that increases rather than decreases cirrus optical thickness (Storelvmo et al., 2013; Gasparini and Lohmann, 2016). Thus, there is low confidence in the cooling effect of CCT, due to limited understanding of cirrus microphysics, its interaction with aerosols, and the complexity of seeding strategy.

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Boucher, O. et al., 2013: Clouds and Aerosols. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change[Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 571–657, doi: 10.1017/cbo9781107415324.016.

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Collins, W.J. et al., 2017: AerChemMIP: quantifying the effects of chemistry and aerosols in CMIP6. Geoscientific Model Development, 10(2), 585–607, doi: 10.5194/gmd-10-585-2017.

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Ferraro, A.J., E.J. Highwood, and A.J. Charlton-Perez, 2011: Stratospheric heating by potential geoengineering aerosols. Geophysical Research Letters, 38(24), 1–6, doi: 10.1029/2011gl049761.

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Ferraro, A.J., A.J. Charlton-Perez, and E.J. Highwood, 2015: Stratospheric dynamics and midlatitude jets under geoengineering with space mirrors and sulfate and titania aerosols. Journal of Geophysical Research: Atmospheres, 120(2), 414–429, doi: 10.1002/2014jd022734.

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Kalidindi, S., G. Bala, A. Modak, and K. Caldeira, 2015: Modeling of solar radiation management: a comparison of simulations using reduced solar constant and stratospheric sulphate aerosols. Climate Dynamics, 44(9–10), 2909–2925, doi: 10.1007/s00382-014-2240-3.

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Kasoar, M., D. Shawki, and A. Voulgarakis, 2018: Similar spatial patterns of global climate response to aerosols from different regions. npj Climate and Atmospheric Science, 1(1), 12, doi: 10.1038/s41612-018-0022-z.

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Keith, D.W., 2010: Photophoretic levitation of engineered aerosols for geoengineering. Proceedings of the National Academy of Sciences, 107(38), 16428–16431, doi: 10.1073/pnas.1009519107.

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Krishnamohan, K.-P.S.-P., G. Bala, L. Cao, L. Duan, and K. Caldeira, 2019: Climate system response to stratospheric sulfate aerosols: sensitivity to altitude of aerosol layer. Earth System Dynamics, 10(4), 885–900, doi: 10.5194/esd-10-885-2019.

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Krishnamohan, K.-P.S.-P., G. Bala, L. Cao, L. Duan, and K. Caldeira, 2020: The Climatic Effects of Hygroscopic Growth of Sulfate Aerosols in the Stratosphere. Earth’s Future, 8(2), e2019EF001326, doi: 10.1029/2019ef001326.

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Modak, A. and G. Bala, 2019: Efficacy of black carbon aerosols: the role of shortwave cloud feedback. Environmental Research Letters, 14(8), 084029, doi: 10.1088/1748-9326/ab21e7.

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Niemeier, U. and H. Schmidt, 2017: Changing transport processes in the stratosphere by radiative heating of sulfate aerosols. Atmospheric Chemistry and Physics, 17(24), 14871–14886, doi: 10.5194/acp-17-14871-2017.

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Shawki, D., A. Voulgarakis, A. Chakraborty, M. Kasoar, and J. Srinivasan, 2018: The South Asian Monsoon Response to Remote Aerosols: Global and Regional Mechanisms. Journal of Geophysical Research: Atmospheres, 123(20), 11585–11601, doi: 10.1029/2018jd028623.

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Visioni, D. et al., 2020a: Reduced Poleward Transport Due to Stratospheric Heating Under Stratospheric Aerosols Geoengineering. Geophysical Research Letters, 47(17), e2020GL089470, doi: 10.1029/2020gl089470.

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Natural aerosols include mineral dust, volcanic aerosol and sea salt. The feedback processes between climate and mineral dust as well as sea salt are assessed in Section 6.4, while the volcanic aerosol is dealt with in Cross-Chapter Box 4.1. Mineral dust created by wind erosion of arid and semi-arid surfaces dominates the aerosol load over some areas. The major sources of contemporary dust are located in the arid topographic basins of northern Africa, Middle East, Central and south-west Asia, the Indian subcontinent, and East Asia (Prospero et al., 2002; Ginoux et al., 2012) and emissions are controlled by changes in surface winds, precipitation, and vegetation (Ridley et al., 2014; W. Wang et al., 2015; DeFlorio et al., 2016; Evan et al., 2016; Pu and Ginoux, 2018). Dust both scatters and absorbs radiation and serves as a nuclei of warm and cold clouds (Chapter 6). The surface direct radiative effect is likely negative over land and ocean, especially when the assumed solar absorption by dust is large (Miller et al., 2014; Strong et al., 2015). Surface temperature and precipitation adjust to the direct radiative effect with both sign and magnitude depending on the dust absorptive properties. Dust often cools the surface, but in regions such as the Sahara surface air temperature increases as the shortwave absorption by dust is increased, leading to increases of surface temperature over the major reflective dust sources (Miller et al., 2014; Solmon et al., 2015; Strong et al., 2015; Jin et al., 2016; Sharma and Miller, 2017).

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Volcanic eruptions load the atmosphere with large amounts of sulphur, which is transformed through chemical reactions and micro-physics processes into sulphate aerosols (Cross-Chapter Box 4.1; Stoffel et al., 2015; LeGrande et al., 2016). If the plume reaches the stratosphere, sulphate aerosols can remain there for months or years (about two to three for large eruptions) and can then be transported to other areas by the Brewer-Dobson circulation. If the eruption occurs in the tropics, its plume is dispersed across the Earth in a few years, while if the eruption occurs in the high latitudes, aerosols mainly remain in the same hemisphere (Pausata et al., 2015). The global temperature response observed after the last five major eruptions of the last two centuries is estimated to be around –0.2°C (Swingedouw et al., 2017), in association with a general decrease of precipitation (Iles and Hegerl, 2017). Nevertheless, the statistical significance of the regional response remains difficult to evaluate over the historical era (Bittner et al., 2016; Swingedouw et al., 2017) due to the small sampling of large volcanic eruptions over this period and the fact that the signal is superimposed upon relatively large internal variability (Gao and Gao, 2018; Dogar and Sato, 2019). Evidence from paleoclimate observations is therefore crucial to obtain a sufficient signal-to-noise ratio (Sigl et al., 2015). Reconstructed modes of climate variability based on proxy records allow evaluation of the influence on those modes (Zanchettin et al., 2013; Ortega et al., 2015; Sjolte et al., 2018; Michel et al., 2020).

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RCMs have often consisted of atmospheric and land components that do not include all possible Earth system processes and therefore neglect important processes such as air-sea coupling (in standard RCMs sea surface temperatures, SSTs, are prescribed from global model simulations or reanalyses) or the chemistry of aerosol–cloud interaction (aerosols prescribed with a climatology), which may influence regional climate projections. Therefore, some RCMs have been extended by coupling to additional components like interactive oceans, sometimes with sea ice (Kjellström et al., 2005; Somot et al., 2008; Van Pham et al., 2014; Sein et al., 2015; Ruti et al., 2016; Zou and Zhou, 2016a; Zou et al., 2017; Samanta et al., 2018), rivers (Sevault et al., 2014; Lee et al., 2015; Di Sante et al., 2019), glaciers (Kotlarski et al., 2010), and aerosols (Zakey et al., 2006; Zubler et al., 2011; Nabat et al., 2015). The coupling of these components allows for the investigation of additional climate processes such as regional sea level change (Adloff et al., 2018), ocean–land interactions (Lima et al., 2019; Soares et al., 2019a), or the impact of high-frequency ocean–atmosphere coupling on the climatology of Mediterranean cyclones (Flaounas et al., 2018).

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Including additional components, feedbacks and drivers can substantially modify the simulated future climate. For example, Kjellström et al. (2005) and Somot et al. (2008) have shown that a regional ESM can significantly modify the SST response to climate change of its driving global model with implications for the climate change signal over both the sea and land. In particular, coupled ocean–atmosphere RCMs may increase the credibility of projections in regions of strong air-sea coupling such as the East Asia–western North Pacific domain (Zou and Zhou, 2016b, 2017). Recent studies demonstrate the importance of including regional patterns of evolving aerosols in RCMs for simulating regional climate change (Boé et al., 2020a; Gutiérrez et al., 2020). RCMs not including the plant physiological response to increasing CO2 concentrations have been shown to substantially underestimate projected increases in extreme temperatures across Europe compared to global models that explicitly model this effect (Schwingshackl et al., 2019).

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The assessment of RCM performance needs to focus not only on mean climatology (Atlas), but also trends (Section 10.3.3.8) and extremes (Chapter 11), and the RCM’s ability at correctly reproducing relevant processes, forcings and feedbacks including aerosols, plant responses to increasing CO2, and so on, (Schwingshackl et al., 2019; Boé et al., 2020a; Sections 11.2. and 10.3.3.3 to 10.3.3.8) to be fit for future projections (Section 10.3.3.9).

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Little evidence was presented in the AR5 (IPCC, 2014) other than increased minimum and maximum temperature trends in the western Himalaya (Hartmann et al., 2013). The SROCC assessed that HKH (named High Mountain Asia) surface-air temperature has warmed more rapidly than the global mean over recent decades (high confidence). Annual mean HKH surface air temperature increased significantly (about 0.1°C per decade) over 1901–2014 (Ren et al., 2017), although Cross-Chapter Box 10.4, Figure 1d shows an observational range of 0.20°C–0.25°C per decade over 1961–2014. There is a rising trend of extreme warm events and fewer extreme cold events over 1961–2015 (Krishnan et al., 2019b; Wester et al., 2019). However, summer cooling over the Karakoram (western HKH) was reported for 1960–2010 (Forsythe et al., 2017). A key relevant process is elevation-dependent warming (EDW; reviewed inPepin et al., 2015), leading to warming of 2°C–2.5°C at 5000 m over 1961–2006, but only 0.5°C at sea level (Xu et al., 2016). However, EDW behaviour appears to depend on region, time period and elevation (D. Guo et al., 2019; b. Li et al., 2020) and understanding is limited by the sparse observational network (You et al., 2020). Observational and model analyses have attributed EDW to GHG and black carbon emissions, accelerating warming by snow-albedo feedback (Ming et al., 2012; Gautam et al., 2013; Xu et al., 2016; Yan et al., 2016; Lau and Kim, 2018; Y. Zhang et al., 2018), or the more pronounced cooling effect of scattering aerosols at low elevations and stratospheric ozone depletion (Guo and Wang, 2012; Zeng et al., 2015). There is high confidence that the eastern and central HKH has exhibited rising temperatures (Cross-Chapter Box 10.4, Figure 1), with warming dependent on season and elevation. There is high confidence that much of the warming can be attributed to GHGs, but the effect of albedo has only medium confidence. There is high confidence in more frequent extreme warm events and fewer extreme cold events over the eastern Himalayas in the last five decades.

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Boers, R., T. Brandsma, and A.P. Siebesma, 2017: Impact of aerosols and clouds on decadal trends in all-sky solar radiation over the Netherlands (1966–2015). Atmospheric Chemistry and Physics, 17(13), 8081–8100, doi: 10.5194/acp-17-8081-2017.

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Drugé, T., P. Nabat, M. Mallet, and S. Somot, 2019: Model simulation of ammonium and nitrate aerosols distribution in the Euro-Mediterranean region and their radiative and climatic effects over 1979–2016. Atmospheric Chemistry and Physics, 19(6), 3707–3731, doi: 10.5194/acp-19-3707-2019.

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Giannini, A. and A. Kaplan, 2019: The role of aerosols and greenhouse gases in Sahel drought and recovery. Climatic Change, 152(3–4), 449–466, doi: 10.1007/s10584-018-2341-9.

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Gustafsson, Ö. and V. Ramanathan, 2016: Convergence on climate warming by black carbon aerosols. Proceedings of the National Academy of Sciences, 113(16), 4243–4245, doi: 10.1073/pnas.1603570113.

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Gutiérrez, C. et al., 2018: Impact of aerosols on the spatiotemporal variability of photovoltaic energy production in the Euro-Mediterranean area. Solar Energy, 174, 1142–1152, doi: 10.1016/j.solener.2018.09.085.

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Gutiérrez, C. et al., 2020: Future evolution of surface solar radiation and photovoltaic potential in Europe: investigating the role of aerosols. Environmental Research Letters, 15(3), 034035, doi: 10.1088/1748-9326/ab6666.

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Han, W. et al., 2020: The mechanisms and seasonal differences of the impact of aerosols on daytime surface urban heat island effect. Atmospheric Chemistry and Physics, 20(11), 6479–6493, doi: 10.5194/acp-20-6479-2020.

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Haywood, J.M., A. Jones, N. Bellouin, and D. Stephenson, 2013: Asymmetric forcing from stratospheric aerosols impacts Sahelian rainfall. Nature Climate Change, 3(7), 660–665, doi: 10.1038/nclimate1857.

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Kasoar, M., D. Shawki, and A. Voulgarakis, 2018: Similar spatial patterns of global climate response to aerosols from different regions. npj Climate and Atmospheric Science, 1(1), 12, doi: 10.1038/s41612-018-0022-z.

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Lau, W.K.M. and K.-M. Kim, 2018: Impact of Snow Darkening by Deposition of Light-Absorbing Aerosols on Snow Cover in the Himalayas–Tibetan Plateau and Influence on the Asian Summer Monsoon: A Possible Mechanism for the Blanford Hypothesis. Atmosphere, 9(11), 438, doi: 10.3390/atmos9110438.

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Nabat, P. et al., 2020: Modulation of radiative aerosols effects by atmospheric circulation over the Euro-Mediterranean region. Atmospheric Chemistry and Physics, 20(14), 8315–8349, doi: 10.5194/acp-20-8315-2020.

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Philipona, R., K. Behrens, and C. Ruckstuhl, 2009: How declining aerosols and rising greenhouse gases forced rapid warming in Europe since the 1980s. Geophysical Research Letters, 36(2), L02806, doi: 10.1029/2008gl036350.

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Shawki, D., A. Voulgarakis, A. Chakraborty, M. Kasoar, and J. Srinivasan, 2018: The South Asian Monsoon Response to Remote Aerosols: Global and Regional Mechanisms. Journal of Geophysical Research: Atmospheres, 123(20), 11585–11601, doi: 10.1029/2018jd028623.

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Wang, Z. et al., 2021: Incorrect Asian aerosols affecting the attribution and projection of regional climate change in CMIP6 models. npj Climate and Atmospheric Science, 4(1), 2, doi: 10.1038/s41612-020-00159-2.

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Wu, G.X. et al., 2016: Advances in studying interactions between aerosols and monsoon in China. Science China Earth Sciences, 59(1), 1–16, doi: 10.1007/s11430-015-5198-z.

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Xu, Y., V. Ramanathan, and W.M. Washington, 2016: Observed high-altitude warming and snow cover retreat over Tibet and the Himalayas enhanced by black carbon aerosols. Atmospheric Chemistry and Physics, 16(3), 1303–1315, doi: 10.5194/acp-16-1303-2016.

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Zhuo, Z., C. Gao, and Y. Pan, 2014: Proxy evidence for China’s monsoon precipitation response to volcanic aerosols over the past seven centuries. Journal of Geophysical Research: Atmospheres, 119(11), 6638–6652, doi: 10.1002/2013jd021061.

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The use of scenarios remains a key element to inform mitigation decisions (Cross-Chapter Box 1.4), to assess which emissions pathways are consistent with a certain GWL (Cross-Chapter Box 1.4, Figure 1), to estimate when certain GWLs are reached (Section 4.3.4), and to assess for which variables it is meaningful to use GWLs as a dimension of integration. The use of scenarios is also essential for variables whose climate response strongly depends on the contribution of radiative forcing (e.g., aerosols) or land-use and land management changes, are time and warming rate dependent (e.g., sea level rise), or differ between transient and quasi-equilibrium states. Furthermore, the use of concentration or emission-driven scenario simulations is required if regional climate assessments need to account for the uncertainty in GSAT changes or climate-carbon feedbacks.

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In summary, long-term changes in various aspects of long- and short-duration extreme temperatures, including intensity, frequency, and duration have been detected in observations and attributed to human influence at global and continental scales. It is extremely likely that human influence is the main contributor to the observed increase in the intensity and frequency of hot extremes and the observed decrease in the intensity and frequency of cold extremes on the global scale. It is very likely that this applies on continental scales as well. Some specific recent hot extreme events would have been extremely unlikely to occur without human influence on the climate system. Changes in aerosol concentrations have affected trends in hot extremes in some regions, with the presence of aerosols leading to attenuated warming, in particular from 1950 to 1980. Crop intensification, irrigation and no-till farming have attenuated increases in summer hot extremes in some regions, such as Central North America (medium confidence).

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Decreases in atmospheric aerosols results in warming and thus an increase in extreme precipitation (Samset et al., 2018; Sillmann et al., 2019). Changes in atmospheric aerosols also result in dynamic changes such as in tropical cyclones (Takahashi et al., 2017; Strong et al., 2018). Uncertainty in the projections of future aerosol emissions results in additional uncertainty in the heavy precipitation projections of the 21st century (Lin et al., 2016).

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In summary, precipitation extremes are controlled by both thermodynamic and dynamic processes. Warming-induced thermodynamic change results in an increase in extreme precipitation, at a rate that closely follows the C-C relationship at the global scale (high confidence). The effects of warming-induced changes in dynamic drivers on extreme precipitation are more complicated, difficult to quantify, and are an uncertain aspect of projections. Precipitation extremes are also affected by forcings other than changes in greenhouse gases, including changes in aerosols, land-use and land-cover change, and urbanization (medium confidence).

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Evan, A.T., J.P. Kossin, C.E. Chung, and V. Ramanathan, 2011: Arabian Sea tropical cyclones intensified by emissions of black carbon and other aerosols. Nature, 479(7371), 94–97, doi: 10.1038/nature10552.

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Lin, L., Z. Wang, Y. Xu, and Q. Fu, 2016: Sensitivity of precipitation extremes to radiative forcing of greenhouse gases and aerosols. Geophysical Research Letters, 43(18), 9860–9868, doi: 10.1002/2016gl070869.

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Persad, G.G. and K. Caldeira, 2018: Divergent global-scale temperature effects from identical aerosols emitted in different regions. Nature Communications, 9(1), 3289, doi: 10.1038/s41467-018-05838-6.

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Schmid, P.E. and D. Niyogi, 2017: Modeling Urban Precipitation Modification by Spatially Heterogeneous Aerosols. Journal of Applied Meteorology and Climatology, 56(8), 2141–2153, doi: 10.1175/jamc-d-16-0320.1.

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Sillmann, J. et al., 2019: Extreme wet and dry conditions affected differently by greenhouse gases and aerosols. npj Climate and Atmospheric Science, 2(1), 24, doi: 10.1038/s41612-019-0079-3.

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Zhao, C. et al., 2018: Enlarging Rainfall Area of Tropical Cyclones by Atmospheric Aerosols. Geophysical Research Letters, 45(16), 8604–8611, doi: 10.1029/2018gl079427.

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It is well understood that global precipitation and evaporation changes are determined by Earth’s energy balance (Section 8.2.1). At regional scales smaller than about 4000 km, water cycle changes become dominated by the transport of moisture (Dagan et al., 2019a; Jakob et al., 2019; Dagan and Stier, 2020), which depend on both thermodynamic and dynamical processes (Section 8.2.2). The constraints of energy budgets at global scales and moisture budgets at regional scales cause key water cycle characteristics such as precipitation intensity, duration and intermittence to alter as the climate warms (Pendergrass and Hartmann, 2014b; Döll et al., 2018). Future water availability is also determined by changes in evaporation, which is driven by a general increase in the atmospheric evaporative demand (Scheff and Frierson, 2014) and modulated by vegetation controls on evaporative losses (Milly and Dunne, 2016; Lemordant et al. , 2018; Vicente-Serrano et al. , 2020). At regional scales, water cycle changes result from the interplay between multiple potential drivers (CO2, aerosols, land use change and human water use; Section 8.2.3). This section assesses advances in physical understanding of global to regional drivers of water cycle changes.

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The overall global mean rate of precipitation change per 1 °C of GSAT increase, apparent hydrological sensitivity (ηa), is reduced compared to hydrological sensitivity by the direct influence of radiative forcing agents on the atmospheric energy balance. Rapid atmospheric adjustments that alter precipitation are primarily caused by GHGs and absorbing aerosols, withhigh agreement and medium evidence across idealized simulations (Fläschner et al.,2016; Samset et al., 2016). A range of rapid precipitation adjustments to CO2 between models are also attributed to vegetation responses leading to a re-partitioning of surface latent and sensible heat fluxes (DeAngelis et al.,2016). Values obtained from six CMIP5 models simulating the Last Glacial Maximum (LGM; 21,000–19,000 years ago) and pre-industrial period (ηa=1.63.0%°C–1) are larger than for each correspondingabrupt 4×CO2experiment (ηa=1.3–2.6% °C–1) due to differences in the mix of forcings, vegetation and land surface changes and a higher thermodynamic % °C–1evaporation scaling in the colder state (Figure 8.4, Section 8.4.1.1; G. Li et al., 2013). Updated estimates across comparable experiments from 22 CMIP5/CMIP6 models (Rehfeld et al., 2020) display a consistent range (ηa=1.7 ± 0.6%°C–1). Confirmingηain observations (Figure 8.4) is difficult due to measurement uncertainty, varying rapid adjustments to radiative forcing and unforced variability (Dai and Bloecker, 2019; Allan et al., 2020).

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In summary, there is high confidence that global mean evaporation and precipitation increase with global warming, but the estimated rate is model-dependent (very likely range of 13 % °C–1). The global increase in precipitation is determined by a robust response to global surface temperature only (very likely 2–3% °C–1) that is partly offset by fast atmospheric adjustments to the vertical profile of atmospheric heating by GHGs and aerosols. Global precipitation increases due to GHGs are offset by the well-understood overall surface radiative cooling effect by aerosols (high confidence). Over land, the average warming-related increase in precipitation is expected to be smaller than over the ocean due to increasing land ocean thermal contrast and surface feedbacks, but the overall precipitation increase over land is generally reinforced by fast atmospheric responses to GHGs that strengthens convergence of winds (medium confidence). Global mean precipitation and evaporation increase at a lower rate than atmospheric moisture per 1°C of global warming (high confidence), leading to longer water vapour lifetime in the atmosphere and driving changes in precipitation intensity, duration and frequency and an overall intensification but not acceleration of the global water cycle.

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Responses of the large-scale atmospheric circulation to a warming climate are not as well understood as thermodynamic drivers (Shepherd, 2014). The AR5 identified robust features including a weakening and broadening of tropical circulation with poleward movement of tropical dry zones and mid-latitude jets (Collins et al., 2013). These can dominate regional water cycle changes, affecting the availability of freshwater and the occurrence of climate extremes. Atmospheric circulation changes generally dominate the spatial pattern of rapid precipitation adjustments (Section 8.2.1) to different forcing agents in the tropics (Bony et al., 2013; He and Soden, 2015; T.B. Richardson et al., 2016, 2018a; Tian et al., 2017; X. Li et al., 2018). Radiative forcing with heterogeneous spatial patterns such as ozone and aerosols (including cloud interactions; Section 6.4.1 and Box 8.1) drive substantial responses in regional atmospheric circulation through uneven heating and cooling effects(L. Liu et al., 2018; Dagan et al., 2019b; Wilcox et al., 2019). Changes in atmospheric circulation are also driven by slower, evolving patterns of warming and associated changes in temperature and moisture gradients (Bony et al., 2013; Samset et al., 2016, 2018a; Ceppi et al., 2018; Ma et al., 2018). There is strong evidence that large regional water cycle changes arise from the atmospheric circulation response to radiative forcings and associated SST pattern evolution butlow agreement in the sign and magnitude (Chadwick et al., 2016b). The role of prolonged weather regimes in determining wet and dry extremes is also better understood since AR5 (Kingston and McMecking, 2015; Schubert et al., 2016; D. Richardson et al., 2018; Barlow et al., 2019). Advances in knowledge of expected large-scale dynamical responses of the water cycle are further assessed in this section (see also Figure 8.21).

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Aerosols affect precipitation in two major pathways, by altering the shortwave and longwave radiation and influencing cloud microphysical properties.

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The aerosol effect on invigoration and rainfall from deep convective clouds peaks at moderate levels (aerosol optical depth of 0.2 to 0.3), but reverses into suppression with more aerosols (H. Liu et al., 2019). More generally, the microphysical aerosol-related processes often compensate or buffer each other (Stevens and Feingold, 2009). For example, suppressed rain by slowing drop coalescence enhances mixed-phase precipitation. Therefore, despite the potentially large aerosol influence on the precipitation forming processes, the net outcome of aerosol microphysical effects on precipitation amount has generallylow confidence, especially when evaluated with respect to the background of high natural variability in precipitation (Tao et al., 2012).

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Ice nucleating particle (INP) initiate ice precipitation from persistent supercooled water clouds that have cloud droplets too small for efficient warm rain, or expedite mixed-phase precipitation in short-lived supercooled rain clouds (Creamean et al., 2013). Most INPs are desert and soil dust particles, rather than air pollution aerosols (DeMott et al., 2010). Biogenic particles from terrestrial and marine origin are more rare, but important at temperatures above about15°C (Murray et al., 2012; DeMott et al., 2016). Dust particles from long-range transport across the Pacific were found to enhance snow-forming processes over the Sierra Nevada in California (Creamean et al., 2013; Fan et al., 2014). The impact of INPs was demonstrated by glaciogenic cloud seeding experiments, which enhanced orographic supercooled clouds with medium confidence of success (French et al., 2018; Rauber et al., 2019; Friedrich et al., 2020). There are still major gaps in understanding the effects of INPs mainly on deep convective clouds (Kanji et al., 2017; Stanford et al., 2017; Korolev et al., 2020).

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Rainfall increases have been observed over northern Australia since the 1950s, with most of the increases occurring in the north-west (Dey et al., 2019a, b; Dai, 2021) and decreases observed in the north-east (J. Li et al., 2012) since the 1970s. There is also a trend towards more intense convective rainfall from thunderstorms over northern Australia (Dowdy, 2020). There is no consensus on the cause of the observed Australian monsoon rainfall trends, with some studies suggesting changes are due to altered circulation driving increased moisture transport or increased frequency of the wettest synoptic regimes (Catto et al., 2012; Clark et al., 2018). Other studies find that model simulations that include anthopogenic aerosols (Rotstayn et al., 2012; Dey et al., 2019a) are better able to capture observed Australian monsoon rainfall trends than simulations with natural or GHG forcing only (Knutson and Zeng, 2018).

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The AR5 (Chapter 7 recognized that the simulation of clouds and precipitation remains challenging for state-of-the-art GCMs. Model development and evaluation have continued since AR5, with a particular emphasis on the representation of new model components, like interactive vegetation, aerosols and biogeochemical cycles. For example, the comparison of simulated tropical precipitation across three successive generations of CMIP models (including CMIP6) indicates overall little improvement for the summer monsoons, the double-ITCZ bias, the diurnal cycle and the frequency of precipitation (Fiedler et al., 2020). Some of these issues are related to inherent model limitations in three specific areas: atmospheric convection, cloudaerosol interactions and land surface processes (ocean and cryosphere-related processes are addressed in Chapter 9). These limitations do not weaken the overall progress made in the large-scale simulation of present-day climate (FAQ 3.3 and Section 3.3.2.3), even though the improvement of CMIP6 compared with CMIP5 models is limited (Figure 3.12) and is generally less systematic or obvious at the regional scale (e.g., Gusain et al. , 2020; Monerie et al. , 2020; Oudar et al. , 2020a). Instead, they call for a careful interpretation of hydrological projections with the full range of plausible outcomes, rather than only considering the most likely scenarios (Sutton, 2018, 2019).

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In AR5 Chapter 7, there was low confidence in the representation of cloud–aerosol interactions in climate models. Despite progresses in this field since AR5, cloud–aerosol interactions remain a major obstacle to understanding climate and severe weather (Varble, 2018). High aerosol concentrations have been observed to suppress rain in water clouds (Campos Braga et al., 2017; Fan et al., 2020). However, such aerosol effects are muted in GCMs, which tend to produce precipitation from shallow clouds too frequently at the expense of rain intensity (Suzuki et al., 2015; Jing et al., 2017). This arises from incomplete knowledge of how clouds adjust to aerosol primary effects such as cloud condensation nuclei (CCN). The adjustment occurs mainly as a dynamic response to the impacts of CCN on cloud droplet size and number concentrations on precipitation-forming processes (Rosenfeld et al., 2008; Goren and Rosenfeld, 2014; Koren et al., 2014; Camponogara et al., 2018). Uncertainties are large for deep clouds, as their processes are much more complex and include also the impacts of aerosols on ice-precipitation processes. Aerosols can substantially invigorate (Rosenfeld et al., 2008; Koren et al., 2014; Fan et al., 2018) and electrify (Thornton et al., 2017; Q. Wang et al., 2018) deep tropical convective clouds. High-resolution atmospheric simulations suggest that high aerosol concentrations can increase environmental humidity by producing clouds that mix more condensed water into the surrounding air, which in turn favours large-scale ascent and strong convective events (Abbott and Cronin, 2021). Further assessment of uncertainties in aerosolcloud interactions for shallow water clouds is provided in Section 7.3.3.2.

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Dust aerosols influence the climate system and hydrologic cycle through both direct impacts on radiation (absorbing and scattering longwave and shortwave) and via indirect effects on cloud and precipitation processes (Box 8.1; Choobari et al., 2014; Kok et al., 2018; Schepanski, 2018). The capacity of dust aerosols to suppress precipitation by reducing humidity and energy availability, and increasing stability in the atmosphere (Cook et al., 2013; Huang et al., 2014) can drive positive feedbacks (see also Section 6.3.6). Thus there is strong potential for dust to contribute to abrupt changes in the water cycle, especially in semi-arid regions where wind erosion is highly sensitive to vegetation cover and drought variability (Yu et al., 2015). One such event occurred over the Central USA during the 1930s: the Dust Bowl drought, an iconic event characterized by widespread land degradation and historically unprecedented levels of dust storm activity (Hansen and Libecap, 2004; Lee and Gill, 2015). While initialized by warm sea surface temperatures in the North Atlantic, modeling work indicates that land cover changes and resulting dust emissions contributed to the severity and spatial extent of the drought by further suppressing precipitation (Cook et al., 2009; Hu et al., 2018; Cowan et al., 2020). There is also increasing evidence that dust aerosol feedbacks are necessary to explain the magnitude of rainfall increase during the mid-Holocene Green Sahara (Pausata et al., 2016; Tierney et al., 2017).

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Solar radiation modification (SRM) techniques seek to reduce the impacts of climate change by modifying the Earth’s radiation budget, either by reflecting incoming solar radiation or increasing the amount of heat lost to space. Note that, following SR1.5, the definition of SRM in this Report refers to changes in both solar and longwave radiation (Section 4.6.3.3 and Glossary). A variety of methods have been proposed, including injection of aerosols or their precursors into the stratosphere, cloud brightening, and cirrus cloud thinning (Table 4.8). Since SRM alters the planetary energy balance, changes in the hydrological cycle are theoretically expected (Section 8.2). These changes can be abrupt if the initial magnitude of SRM is large, rather than increased gradually. Since AR5, a diversity of SRM techniques have been tested using climate model simulations, with an increasing focus on consequences for regional water availability. Techniques targeting shortwave radiation (sulfate injection, surface albedo modification, cloud brightening) are likely to reduce global mean precipitation relative to future CO2 -emissions scenarios (Bala et al. , 2008; A. Jones et al. , 2013; Tilmes et al. , 2013; Ferraro et al. , 2014; Crook et al. , 2015). In contrast, cirrus cloud thinning, a longwave radiation technique, results in increased global precipitation as it causes enhanced radiative cooling in the troposphere (medium confidence) (Crook et al., 2015; Kristjánsson et al., 2015; Jackson et al., 2016).

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Creamean, J.M. et al., 2013: Dust and biological aerosols from the Sahara and Asia influence precipitation in the Western U.S. Science, 340(6127), 1572–1578, doi: 10.1126/science.1227279.

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Fan, C. et al., 2020: Strong Precipitation Suppression by Aerosols in Marine Low Clouds. Geophysical Research Letters, 47(7), e2019GL086207, doi: 10.1029/2019gl086207.

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García-Martínez, I.M., M.A. Bollasina, and S. Undorf, 2020: Strong large-scale climate response to North American sulphate aerosols in CESM. Environmental Research Letters, 15(11), 114051, doi: 10.1088/1748-9326/abbe45.

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Giannini, A. and A. Kaplan, 2019: The role of aerosols and greenhouse gases in Sahel drought and recovery. Climatic Change, 152(3–4), 449–466, doi: 10.1007/s10584-018-2341-9.

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Guo, J. et al., 2017: Declining frequency of summertime local-scale precipitation over eastern China from 1970 to 2010 and its potential link to aerosols. Geophysical Research Letters, 44(11), 5700–5708, doi: 10.1002/2017gl073533.

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Haywood, J.M., A. Jones, N. Bellouin, and D. Stephenson, 2013: Asymmetric forcing from stratospheric aerosols impacts Sahelian rainfall. Nature Climate Change, 3(7), 660–665, doi: 10.1038/nclimate1857.

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Huang, J., T. Wang, W. Wang, Z. Li, and H. Yan, 2014: Climate effects of dust aerosols over East Asian arid and semiarid regions. Journal of Geophysical Research: Atmospheres, 119(19), 11398–11416, doi: 10.1002/2014jd021796.

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Kasoar, M., D. Shawki, and A. Voulgarakis, 2018: Similar spatial patterns of global climate response to aerosols from different regions. npj Climate and Atmospheric Science, 1(1), 12, doi: 10.1038/s41612-018-0022-z.

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Lau, W.K.-M. and K.-M. Kim, 2017: Competing influences of greenhouse warming and aerosols on Asian summer monsoon circulation and rainfall. Asia-Pacific Journal of Atmospheric Sciences, 53(2), 181–194, doi: 10.1007/s13143-017-0033-4.

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Ralph, F.M. et al., 2016: CalWater Field Studies Designed to Quantify the Roles of Atmospheric Rivers and Aerosols in Modulating U.S. West Coast Precipitation in a Changing Climate. Bulletin of the American Meteorological Society, 97(7), 1209–1228, doi: 10.1175/bams-d-14-00043.1.

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Rosenfeld, D. et al., 2008: Flood or Drought: How Do Aerosols Affect Precipitation?Science, 321(5894), 1309–1313, doi: 10.1126/science.1160606.

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Rotstayn, L.D., M.A. Collier, and J.- Luo, 2015: Effects of declining aerosols on projections of zonally averaged tropical precipitation. Environmental Research Letters, 10(4), 044018, doi: 10.1088/1748-9326/10/4/044018.

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Rotstayn, L.D., M.A. Collier, A. Chrastansky, S.J. Jeffrey, and J.-J. Luo, 2013: Projected effects of declining aerosols in RCP4.5: unmasking global warming?Atmospheric Chemistry and Physics, 13(21), 10883–10905, doi: 10.5194/acp-13-10883-2013.

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Schmid, P.E. and D. Niyogi, 2017: Modeling urban precipitation modification by spatially heterogeneous aerosols. Journal of Applied Meteorology and Climatology, 56(8), 2141–2153, doi: 10.1175/jamc-d-16-0320.1.

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Shine, K.P., R.P. Allan, W.J. Collins, and J.S. Fuglestvedt, 2015: Metrics for linking emissions of gases and aerosols to global precipitation changes. Earth System Dynamics, 6(2), 525–540, doi: 10.5194/esd-6-525-2015.

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Tang, T. et al., 2018: Dynamical response of Mediterranean precipitation to greenhouse gases and aerosols. Atmospheric Chemistry and Physics, 18(11), 8439–8452, doi: 10.5194/acp-18-8439-2018.

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Tao, W.-K., J.-P. Chen, Z. Li, C. Wang, and C. Zhang, 2012: Impact of aerosols on convective clouds and precipitation. Reviews of Geophysics, 50(2), RG2001, doi: 10.1029/2011rg000369.

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Tian, D., W. Dong, D. Gong, Y. Guo, and S. Yang, 2017: Fast responses of climate system to carbon dioxide, aerosols and sulfate aerosols without the mediation of SST in the CMIP5. International Journal of Climatology, 37(3), 1156–1166, doi: 10.1002/joc.4763.

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Wang, Q., Z. Li, J. Guo, C. Zhao, and M. Cribb, 2018: The climate impact of aerosols on the lightning flash rate: is it detectable from long-term measurements?Atmospheric Chemistry and Physics, 18(17), 12797–12816, doi: 10.5194/acp-18-12797-2018.

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Zhao, C. et al., 2018: Enlarging Rainfall Area of Tropical Cyclones by Atmospheric Aerosols. Geophysical Research Letters, 45(16), 8604–8611, doi: 10.1029/2018gl079427.

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Zhao, S. and K. Suzuki, 2019: Differing impacts of black carbon and sulfate aerosols on global precipitation and the ITCZ location via atmosphere and ocean energy perturbations. Journal of Climate, 32(17), 5567–5582, doi: 10.1175/jcli-d-18-0616.1.

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Zhao, X., R.J. Allen, T. Wood, and A.C. Maycock, 2020: Tropical Belt Width Proportionately More Sensitive to Aerosols Than Greenhouse Gases. Geophysical Research Letters, 47(7), e2019GL086425, doi: 10.1029/2019gl086425.

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Aerosols (tiny airborne particles) interact with climate in numerous ways, some direct (e.g., reflecting solar radiation back into space) and others indirect (e.g., cloud droplet nucleation); specific effects may cause either positive or negative radiative forcing. Major volcanic eruptions inject SO2 (a negative driver) into the stratosphere, creating aerosols that can cool the planet for years at a time by reflecting some incoming solar radiation. The history and climatic effects of volcanic activity have been traced through historical records, geological traces, and observations of major eruptions by aircraft, satellites and other instruments (Dörries, 2006). The negative RF of major volcanic eruptions was considered in the First Assessment Report (FAR; IPCC, 1990a). In subsequent assessments, the negative RF of smaller eruptions has also been considered (e.g., Cross-Chapter Box 4.1 in Chapter 4 of this Report; Section 2.4.3 in IPCC, 1996). Dust and other natural aerosols have been studied since the 1880s (e.g., Aitken, 1889; Ångström, 1929, 1964; Twomey, 1959), particularly in relation to their role in cloud nucleation, an aerosol indirect effect whose RF may be either positive or negative depending on such factors as cloud altitude, depth and albedo (Stevens and Feingold, 2009; Boucher et al., 2013).

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Climate models provide the ability to simulate these complex circulatory processes, and to improve the physical theory of climate by testing different mathematical formulations of those processes. Since controlled experiments at planetary scale are impossible, climate simulations provide one important way to explore the differential effects and interactions of variables such as solar irradiance, aerosols and GHGs. To assess their quality, models or components of models may be compared with observations. For this reason, they can be used to attribute observed climatic effects to different natural and human drivers (Hegerl et al., 2011). As early as Arrhenius (1896), simple mathematical models were used to calculate the effects of doubling atmospheric carbon dioxide over pre-industrial concentrations (approximately 550 ppm vs approximately 275 ppm respectively). In the early 20th century Bjerknes formulated the Navier–Stokes equations of fluid dynamics for motion of the atmosphere (Bjerknes, 1906; Bjerknes et al., 1910), and Richardson (1922) developed a system for numerical weather prediction based on these equations. When electronic computers became available in the late 1940s, the methods of Bjerknes and Richardson were successfully applied to weather forecasting (Charney et al., 1950; Nebeker, 1995; Harper, 2008).

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In the 1960s similar approaches to modelling the weather were used to model the climate, but with much longer runs than daily forecasting (Smagorinsky et al., 1965; Manabe and Wetherald, 1967). Simpler statistical and one- and two-dimensional modelling approaches continued in tandem with the more complex general circulation models (GCMs; Manabe and Wetherald, 1967; Budyko, 1969; Sellers, 1969). The first coupled atmosphere–ocean model (AOGCM) with realistic topography appeared in 1975 (Bryan et al., 1975; Manabe et al., 1975). Rapid increases in computer power enabled higher resolutions, longer model simulations, and the inclusion of additional physical processes in GCMs, such as aerosols, atmospheric chemistry, sea ice, and snow.

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Observations of the composition of the atmosphere have been further improved through expansions of existing surface observation networks (Bodeker et al., 2016; De Mazière et al., 2018) and through in situ measurements such as aircraft campaigns (Sections 2.2, 5.2 and Section 6.2). Examples of expanded networks include the Aerosols, Clouds and Trace Gases Research Infrastructure (ACTRIS; Pandolfi et al., 2018), which focuses on short-lived climate forcers, and the Integrated Carbon Observation System (ICOS), which allows scientists to study and monitor the global carbon cycle and GHG emissions (Colomb et al., 2018). Examples of recent aircraft observations include the Atmospheric Tomography Mission (ATom), which has flown repeatedly along the north–south axis of both the Pacific and Atlantic oceans, and the continuation of the In-service Aircraft for a Global Observing System (IAGOS) effort, which measures atmospheric composition from commercial aircraft (Petzold et al., 2015).

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Models of atmospheric composition and emissions sources and sinks allow the forecast and reanalysis of constituents such as O3, carbon monoxide (CO), nitrogen oxides (NOx) and aerosols. The Copernicus Atmosphere Monitoring Service (CAMS) reanalysis shows improvement against earlier atmospheric composition reanalyses, giving greater confidence for its use to study trends and evaluate models (Section 7.3; e.g., Inness et al., 2019).

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Atmospheric models include representations of physical processes such as clouds, turbulence, convection and gravity waves that are not fully represented by grid-scale dynamics. The CMIP6 models have undergone updates in some of their parameterization schemes compared to their CMIP5 counterparts, with the aim of better representing the physics and bringing the climatology of the models closer to newly available observational datasets. Most notable developments are to schemes involving radiative transfer, cloud microphysics, and aerosols, in particular a more explicit representation of the aerosol indirect effects through aerosol-induced modification of cloud properties. Broadly, aerosol–cloud microphysics has been a key topic for the aerosol and chemistry modelling communities since AR5, leading to improved understanding of the climate influence of short-lived climate forcers, but they remain the single largest source of spread in ESM calculations of climate sensitivity (Meehl et al., 2020), with numerous parameterization schemes in use (Section 6.4; Gettelman and Sherwood, 2016; Zhao et al., 2018; Gettelman et al., 2019). The treatment of droplet size and mixed-phase clouds (liquid and ice) was found to lead to changes in the climate sensitivity (Glossary) of some models between AR5 and AR6 (Section 7.4; Bodas-Salcedo et al., 2019; Gettelman et al., 2019; Zelinka et al., 2020).

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A scenario is a description of how the future may develop, based on a coherent and internally consistent set of assumptions about key drivers including demography, economic processes, technological innovation, governance, lifestyles, and relationships among these driving forces (Section 1.6.1.1; IPCC, 2000; Rounsevell and Metzger, 2010; O’Neill et al., 2014). Scenarios can also be defined by geophysical driving forces only, such as emissions or abundances of GHGs, aerosols, and aerosol precursors or land-use patterns. Scenarios are not predictions; instead, they provide a ‘what-if’ investigation of the implications of various developments and actions (Moss et al., 2010). WGI investigates potential future climate change principally by assessing climate model simulations using emissions scenarios originating from the WGIII community (Section 1.6.1.2). The scenarios used in this WGI Report cover various hypothetical ‘baseline scenarios’ or ‘reference futures’ that could unfold in the absence of any – or any additional – climate policies (Glossary). These ‘reference scenarios’ originate from a comprehensive analysis of a wide array of socio-economic drivers, such as population growth, technological development, and economic development, and their broad spectrum of associated energy, land use and emissions implications (Riahi et al., 2017). With direct policy relevance to the Paris Agreement’s 1.5°C and ‘well below’ 2°C goals, this Report also assesses climate futures where the effects of additional climate change mitigation action are explored, i.e., so-called mitigation scenarios (for a broader discussion of scenarios and futures analysis, see Cross-Chapter Box 1, Table 1 in SRCCL, IPCC, 2019a).

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The SSP scenarios can be used for either emissions- or concentration-driven model experiments (Cross-Chapter Box 1.4). ESMs can be run with emissions and concentrations data for GHGs and aerosols and land-use or landcover maps and calculate levels of radiative forcing internally. The radiative forcing labels of the RCP and SSP scenarios, such as ‘2.6’ in RCP2.6 or SSP1-2.6, are thus approximate labels for the year 2100 only. The actual global mean effective radiative forcing varies across ESMs due to different radiative transfer schemes, uncertainties in aerosol–cloud interactions, and different feedback mechanisms, among other reasons. Nonetheless, using approximate radiative forcing labels is advantageous because it establishes a clear categorization of scenarios, with multiple climate forcings and different combinations in those scenarios summarized in a single number. The classifications according to cumulative carbon emissions (Section 1.6.3) and global warming level (Section 1.6.2 and Cross-Chapter Box 7.1 on emulators) complement those forcing labels.

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By 2000, the IPCC Special Report on Emissions Scenarios (SRES) produced the SRES scenarios (IPCC, 2000), albeit without assuming any climate policy-induced mitigation. The four broad groups of SRES scenarios (scenario ‘families’) – A1, A2, B1 and B2 – were the first scenarios to emphasize socio-economic scenario storylines, and also first to emphasize other GHGs, land-use change and aerosols. Represented by three scenarios for the high-growth A1 scenario family, those 6 SRES scenarios (A1FI, A1B, A1T, A2, B1, and B2) can still sometimes be found in today’s climate impact literature. The void of missing climate change mitigation scenarios was filled by a range of community exercises, including the so-called ‘post-SRES scenarios’ (Swart et al., 2002).

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The RCP scenarios (van Vuuren et al., 2011) then broke new ground by providing low-emissions pathways that implied strong climate change mitigation, including an example with negative CO2 emissions on a large scale, namely RCP2.6. As shown in Figure 1.28, the upper end of the scenario range has not substantially shifted. Building on the SRES multi-gas scenarios, the RCPs include time series of emissions and concentrations of the full suite of GHGs, aerosols and chemically active gases, as well as land use and land cover (Moss et al., 2010). The word ‘representative’ signifies that each RCP is only one of many possible scenarios that would lead to the specific radiative forcing characteristics. The term ‘pathway’ emphasizes that not only the long-term concentration levels are of interest, but also the trajectory taken over time to reach that outcome (Moss et al., 2010). RCPs usually refer to the concentration pathway extending to 2100, for which IAMs produced corresponding emissions scenarios. Four RCPs produced from IAMs were selected from the published literature and are used in AR5 as well as in this report, spanning approximately the range from below 2°C warming to high (above 4°C) warming best-estimates by the end of the 21st century: RCP2.6, RCP4.5 and RCP6.0 and RCP8.5 (Cross-Chapter Box 1.4, Table 1). Extended Concentration Pathways (ECPs) describe extensions of the RCPs from 2100 to 2300 that were calculated using simple rules generated by stakeholder consultations; these do not represent fully consistent scenarios (Meinshausen et al., 2011b).

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In contrast to stylized assumptions about the future evolution of emissions (e.g., a linear phase-out from year A to year B), these SSP scenarios are the result of a detailed scenario generation process (Sections 1.6.1.1 and 1.6.1.2). While IAMs produce internally consistent future-emissions time series for CO2, CH4, N2O, and aerosols for the SSP scenarios (Riahi et al., 2017; Rogelj et al., 2018a), these emissions scenarios are subject to several processing steps for harmonization (Gidden et al., 2018) and in-filling (Lamboll et al., 2020), before also being complemented by several datasets so that ESMs can run these SSPs (Durack et al., 2018; Tebaldi et al., 2021). Although five scenarios are the primary focus of WGI, a total of nine SSP scenarios have been prepared with all the necessary detail to drive the ESMs as part of the CMIP6 (Cross-Chapter Box 1.4, Figure 1 and Table 2).

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Boucher, O. et al., 2013: Clouds and Aerosols. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change[Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 571–658, doi: 10.1017/cbo9781107415324.016.

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Carslaw, K.S. et al., 2017: Aerosols in the Pre-industrial Atmosphere. Current Climate Change Reports, 3(1), 1–15, doi: 10.1007/s40641-017-0061-2.

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Collins, W.J. et al., 2017: AerChemMIP: quantifying the effects of chemistry and aerosols in CMIP6. Geoscientific Model Development, 10(2), 585–607, doi: 10.5194/gmd-10-585-2017.

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Hidy, G.M., 2019: Atmospheric Aerosols: Some Highlights and Highlighters, 1950 to 2018. Aerosol Science and Engineering, 3(1), 1–20, doi: 10.1007/s41810-019-00039-0.

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Lamarque, J.-F. et al., 2011: Global and regional evolution of short-lived radiatively-active gases and aerosols in the Representative Concentration Pathways. Climatic Change, 109(1–2), 191–212, doi: 10.1007/s10584-011-0155-0.

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Rasool, S.I. and S.H. Schneider, 1971: Atmospheric Carbon Dioxide and Aerosols: Effects of Large Increases on Global Climate. Science, 173(3992), 138–141, doi: 10.1126/science.173.3992.138.

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Shine, K.P., R.P. Allan, W.J. Collins, and J.S. Fuglestvedt, 2015: Metrics for linking emissions of gases and aerosols to global precipitation changes. Earth System Dynamics, 6(2), 525–540, doi: 10.5194/esd-6-525-2015.

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Toon, O.B. and J.B. Pollack, 1976: A Global Average Model of Atmospheric Aerosols for Radiative Transfer Calculations. Journal of Applied Meteorology and Climatology, 15(3), 225–246, doi: 10.1175/1520-0450(1976)015<02 25:agamoa>2.0.co;2.

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SLCFs can affect climate by interacting with radiation or by perturbing other components of the climate system (e.g., the cryosphere and carbon cycle through deposition, or the water cycle through modifications of cloud properties via cloud condensation nuclei or ice nuclei). SLCFs can have either net warming or net cooling effects on climate. In addition to altering the Earth’s radiative balance, many SLCFs are also air pollutants with adverse effects on human health and ecosystems. SLCFs are of interest for climate policies (e.g., methane, HFCs), and are regulated as air pollutants (e.g., aerosols, ozone) or because of their deleterious influence on stratospheric ozone (e.g., HCFCs). The list of SLCFs assessed in this chapter and their effects are provided in Table 6.1.

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Although ozone, aerosols and their precursors have been considered in previous IPCC assessment reports, AR5 considered SLCFs as a specific category of climate-relevant compounds but referred to them as near-term climate forcers (NTCFs; Myhre et al. , 2013). In AR5, the linkages between air quality and climate change were also considered in a more detailed and quantitative way than in previous reports (Kirtman et al., 2013; Myhre et al., 2013).

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The AR5 WGI assessed radiative forcings for short-lived gases, aerosols, aerosol precursors and aerosol–cloud interactions as well as the evolution of confidence levels in the forcing mechanisms from SAR to AR5. Whereas the forcing mechanisms for ozone and aerosol–radiation interactions were estimated to be characterized with high confidence, the ones induced by aerosols through other processes remained of very low to low confidence. The AR5 also reported that forcing agents such as aerosols and ozone are highly heterogeneous spatially and temporally, and these patterns affect global and regional temperature responses as well as other aspects of climate response such as the hydrologic cycle (Myhre et al. , 2013).

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Following SR1.5, the IPCC Special Report on Climate Change and Land (SRCCL; IPCC, 2019a) took into consideration the emissions on land of three major SLCFs: mineral dust, carbonaceous aerosols (BC and OA) and biogenic volatile compounds (BVOCs) (Jia et al., 2019). The SRCCL concluded that: (i) there is no agreement about the direction of future changes in mineral dust emissions; (ii) fossil fuel and biomass burning, and secondary organic aerosols (SOA) from natural BVOC emissions are the main global sources of carbonaceous aerosols whose emissions are expected to increase in the near future due to possible increases in open biomass burning and increase in SOA from oxidation of BVOCs (medium confidence); and (iii) BVOCs are emitted in large amounts by forests and they are rapidly oxidized in the atmosphere to form less volatile compounds that can condense and form SOA, and in a warming planet, BVOC emissions are expected to increase but magnitude is unknown and will depend on future land-use change, in addition to climate (limited evidence, medium agreement).

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Specific aspects of SLCFs can also be found in other chapters of this report: the evolution of ozone, HFCs and aerosols, as well as the long-term evolution of methane, dust and volcanic aerosols are discussed in Chapter 2; near-term climate projections and SLCFs are discussed in Chapter 4; the global budget of methane is addressed in Chapter 5; aerosol–cloud and aerosol–precipitation interactions are treated in Chapters 7 and 8, respectively; the global radiative forcing of SLCFs is assessed in Chapter 7; some aspects of downscaling methodology in climate modelling concerning SLCFs are discussed in Chapter 10. The WGII report assesses how climate change affects air pollution and its impacts on human health and the WGIII report assesses the role of SLCFs in abatement strategies and their cost-effectiveness, the implications of mitigation efforts on air pollution as well as the articulation between air pollution policies and GHG mitigation.

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Emissions of carbonaceous aerosols (BC, OC) have been steadily increasing and their emissions have almost doubled since 1950 (medium confidence) (Hoesly et al., 2018). Before 1950, North America and Europe contributed about half of the global total but successful introduction of diesel particulate filters on road vehicles (Fiebig et al., 2014; Robinson et al., 2015; Klimont et al., 2017a) and declining reliance on solid fuels for heating brought in large reductions (high confidence) (Figure 6.19). Currently, global carbonaceous aerosol emissions originate primarily from Asia and Africa (Bond et al., 2013; Hoesly et al., 2018; Elguindi et al., 2020; McDuffie et al., 2020), representing about 80% of the global total (high confidence) (Figure 6.3). Consideration, in CMIP6, of emissions from kerosene lamps and gas flaring, revised estimates for open burning of waste, regional coal consumption, and new estimates for Russia (Stohl et al., 2013; Huang et al., 2015; Huang and Fu, 2016; Kholod et al., 2016; Conrad and Johnson, 2017; Evans et al., 2017; Klimont et al., 2017a) resulted in over 15% higher global emissions of OC and BC than in the CMIP5 estimates for the first decade of the 21st century (Figure 6.18). However, the continued increase of BC emissions over Eastern Asia after 2005, estimated in CMIP6 (Figure 6.19), has been questioned recently as a steady decline of BC concentrations was measured in the air masses flowing out from the east coast of China (Kanaya et al., 2020), which has been also estimated in recent regional bottom-up and top-down inventories (Zheng et al. , 2018a; Elguindi et al. , 2020; McDuffie et al. , 2020). Since AR5, confidence in emissions estimates and trends in North America and Europe has increased, but high uncertainties remain for Asia and Africa, despite their major contribution to global emissions. The size distribution of emitted species, of importance for climate and health impacts, remains uncertain and the CEDS inventory does not provide such information. Overall, a factor two uncertainty in global estimates of BC and OC emissions remains, with post-2005 emissions overestimated in Asia (high confidence) and Africa (medium confidence).

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A wide range of BVOCs are emitted from vegetation with the dominant compounds being isoprene and monoterpenes but also including sesquiterpenes, alkenes, alcohols, aldehydes and ketones. The photooxidation of BVOC emissions plays a fundamental role in atmospheric composition by controlling the regional and global budgets of ozone and organic aerosols, and impacting the lifetime of methane and other reactive components (Arneth et al., 2010b; Heald and Spracklen, 2015). Substantial uncertainty exists across different modelling frameworks for estimates of global total BVOC emissions and individual compound emissions (Messina et al., 2016). Global isoprene emissions estimates differ by a factor of two from 300–600 TgC yr–1 and global monoterpene emissions estimates by a factor of five from 30–150 TgC yr–1 (Messina et al., 2016). A main driver of the uncertainty ranges is the choice of basal emissions rates assigned to different plant functional types in the model; however, the smaller uncertainty range for isoprene than for monoterpenes is not fully understood (Arneth et al., 2008). The evaluation of global BVOC emissions is challenging because of poor measurement data coverage in many regions and the lack of year-round measurements (Unger et al., 2013). Several observational approaches have been developed in the past few years to improve understanding of BVOC emissions, including indirect methods such as the measurement of the OH loss rate in forested environments (Yang et al., 2016) and application of the variability in satellite formaldehyde concentrations (Palmer et al., 2006; Barkley et al., 2013; Stavrakou et al., 2014). Direct space-borne isoprene retrievals using infrared radiance (IR) measurements have very recently become available (Fu et al., 2019; Wells et al., 2020). Collectively these approaches have identified weaknesses in the ability of the parametrizations in global models to reproduce BVOC emissions hotspots (Wells et al., 2020). However, none of the current observational approaches have yet been able to reduce the uncertainty ranges in global emissions estimates.

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Oceans are a significant source of marine aerosols that influence climate directly by scattering and absorbing solar radiation or indirectly through the formation of cloud condensation nuclei (CCN) and ice nucleating particles (INPs). Marine aerosols consist of primary sea-spray particles and secondary aerosols produced by the oxidation of emitted precursors, such as dimethylsulphide (DMS) and numerous other BVOCs. Sea-spray particles, composed of sea salt and primary organic aerosols (POA), are produced by wind-induced wave breaking as well as the direct mechanical disruption of waves. The understanding of sea-spray emissions has increased substantially over the last five years, however, the knowledge of formation pathways and factors influencing their emissions continue to have large uncertainties (Forestieri et al., 2018; Saliba et al., 2019). The emission rate of sea-spray particles is predominantly controlled by wind speed. Since AR5, the influence of other factors, including sea surface temperature, wave history and salinity is increasingly evident (Callaghan et al. , 2014; Grythe et al. , 2014; Ovadnevaite et al. , 2014; Salter et al. , 2014; Barthel et al. , 2019). Marine POA, often the dominant submicron component of sea spray, are emitted as a result of oceanic biological activity, however the biological processes by which these particles are produced remain poorly characterized contributing to large uncertainties in global marine POA emissions estimates (Tsigaridis et al. , 2014; Cravigan et al. , 2020; Hodzic et al. , 2020) . Furthermore, the particle size and chemical composition of sea-spray particles, and how these evolve in response to changing climate factors and dynamic oceanic biology, continue to have large uncertainties.

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DMS, the largest natural source of sulphur in the atmosphere, is produced by marine phytoplankton and is transferred from ocean water to the atmosphere due to wind-induced mixing of surface water. DMS oxidizes to produce sulphate aerosols and contributes to the formation of CCN. Since AR5, the range in global DMS flux estimates reduced from 10–40 TgS yr–1 to 9–34 TgS yr–1 with a very likely range of 18–24 TgS yr–1based on sea-surface measurements and satellite observations (Lana et al., 2011). DMS production, and consequently emissions, have been shown to respond to multiple stressors, including climate warming, eutrophication, and ocean acidification. However, large uncertainties in process-based understanding of the mechanisms controlling DMS emissions, from physiological to ecological, limit our knowledge of past variations and our capacity to project future changes.

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This section assesses trends in the atmospheric distribution of aerosols and improvements in relevant physical and chemical processes. The observed large-scale temporal evolution of aerosols is assessed in Section 2.2.6. Since AR5, long-term measurements of aerosol mass concentrations from regional global surface networks have continued to expand and provide information on the distribution and trends in aerosols (Figure 6.7). There is large spatial variability in aerosol mass concentration, expressed as PM2.5, dominant aerosol type and aerosol composition, consistent with the findings in AR5.

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Remote-sensing instruments provide a larger-scale view of aerosol distributions and trends than ground-based monitoring networks by retrieving the aerosol optical depth (AOD), which is indirectly related to aerosol mass concentrations. AOD is the column-integrated measure of extinction of the solar intensity due to aerosols at a given wavelength, and is therefore relevant to the estimation of the radiative forcing of aerosol–radiation interactions (Section 7.3.3.1). Models participating in Phase III of the AeroCom intercomparison project were found to underestimate present-day AOD by about 20% (Gliß et al., 2021), although different remote-sensing estimates obtain different estimates of global mean AOD. Gliß et al. (2021) also highlight the considerable diversity in the simulated contribution of various aerosol types to total AOD. However, models simulate regional trends in AODs that agree well, when expressed as percentage change, with ground- (Mortier et al. , 2020; Gliß et al. , 2021) and satellite-based (Cherian and Quaas, 2020; Gliß et al., 2021) observations. AOD trends simulated by CMIP6 models are more consistent with satellite-derived trends than CMIP5 models for several sub-regions, thanks to improved emissions estimates (Cherian and Quaas, 2020).

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Process understanding of sulphate production pathways from SO2 emissions has seen some progress since AR5. More specifically, many global climate models now have a more complete description of chemical reactions such that oxidant levels (including ozone) are better described, include a pH-dependence of SO2 oxidation (e.g., Kirkevåg et al., 2018; Bauer et al., 2020), and implement explicit descriptions of ammonium and nitrate aerosol components, which may influence the partitioning of sulphate (Bian et al. , 2017; Lund et al. , 2018a). The pH influences the heterogeneous chemistry as well as the physical properties of the aerosols, and this topic has been a subject of growing interest since AR5 (Cheng et al., 2016; Freedman et al., 2019; Nenes et al., 2020). Increases in cloudwater pH have been shown to significantly increase the radiative forcing due to sulphate aerosols (Turnock et al., 2019).

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Based on long-term surface-based in situ observations, AR5 reported a strong decline in sulphate aerosols in Europe and the USA over 1990–2009, with the largest decreases occurring before 2000 in Europe and post-2000 in the USA. Since AR5, atmospheric measurements in conjunction with model results have provided insights into the spatial and temporal distribution of sulphate and sulphur deposition (Vet et al., 2014; Tan et al., 2018; Aas et al., 2019). The in situ observations in North America and Europe reveal substantial reduction since the measurements started around 1980, though the trends have not been linear through this period (Table 6.5). Several regional studies agree with these trend estimates for Europe (Banzhaf et al. , 2015; Theobald et al. , 2019) and North America (Sickles II and Shadwick, 2015; Paulot et al. , 2016). Further, the concentrations of primary emitted SO2 (Section 6.3.3.5) show greater decreases than secondary sulphate aerosols over these regions due to a combination of higher oxidation rate (hence more SO2 converted to SO42–) and increased dry deposition rate of SO2 (Fowler et al. , 2009; Banzhaf et al. , 2015). In situ observations over other parts of the world are scattered (Figure 6.7), and the lack of observations makes it too uncertain to quantify regional representative trends (Hammer et al., 2018). However, limited in situ observations in Eastern Asia indicate an increase in atmospheric sulphate up to around 2005 and then a decline (Aas et al., 2019), which is confirmed by satellite observations of SO2 (Section 6.3.3.5). In India, on the other hand, satellite observations indicate a rapid increase in the SO2 levels (Krotkov et al., 2016), and long-term measurements of sulphate in precipitation in India further provide evidence of an increasing trend from 1980–2010 (Bhaskar and Rao, 2017; Aas et al., 2019). Further improvements in global trend assessments are expected with new integrated reanalysis products from the Earth-system data assimilation projects (Randles et al., 2017; Inness et al., 2019).

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Ammonium sulphate and ammonium nitrate aerosols are formed when NH3 reacts with nitric acid (HNO3) and sulphuric acid (H2SO4), produced in the atmosphere by the oxidation of NOx and SO2 respectively. Ammonium nitrate is formed only after H2SO4 is fully neutralized. NH4+ and NO3aerosols produced via these gas-to-particle reactions are a major fraction of fine-mode particles (with diameter <1µm) affecting air quality and climate. Coarse-mode nitrate, formed by the heterogeneous reaction of nitric acid with dust and sea salt, dominates the overall global nitrate burden, but has little radiative effect (Hauglustaine et al., 2014; Bian et al., 2017). Trends in ammonium (NH4+) and nitrate (NO3) were not assessed in AR5.

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Models indicate that the burden of fine-mode NO3has increased by a factor of two to five from 1850–2000 (Xu and Penner, 2012; Hauglustaine et al., 2014; Lund et al., 2018a), an increase that has accelerated between 2001 and 2015 (Lund et al., 2018a; Paulot et al., 2018b). The sensitivity of NO3 to changes in NH3, SO42–, and HNO3 is determined primarily by aerosol pH, temperature, and aerosol liquid water (Guo et al., 2016, 2018; Weber et al., 2016; Nenes et al., 2020). In regions where aerosol pH is high, changes in NO3follow changes in NOx emissions, consistent with the observed increase of ammonium nitrate in northern China from 2000–2015 (Wen et al., 2018) and its decrease in the US Central Valley (Pusede et al., 2016). In contrast, the decrease in SO2 emissions in the south-east USA has caused little change in NO3 from 1998–2014 as nitric acid largely remains in the gas phase due to highly acidic aerosols (Weber et al., 2016; Guo et al., 2018).

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In summary, there is high confidence that the NH4+ and NO3burdens have increased from the pre-industrial period to the present day, although the magnitude of the increase is uncertain especially for NO3. The sensitivity of NH4+ and NO3 to changes in NH3, H2SO4 and HNO3 is well understood theoretically. However, it remains challenging to represent in models, in part because of uncertainties in the simulation of aerosol pH, and only a minority of ESMs consider nitrate aerosols in CMIP6.

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Carbonaceous aerosols are black carbon (BC)3, which is soot made almost purely of carbon, and organic aerosols4 (OA), which also contain hydrogen and oxygen and can be of both primary (POA) or secondary (SOA) origin. BC and a fraction of OA called brown carbon (BrC) absorb solar radiation. The various components of carbonaceous aerosols have different optical properties, so the knowledge of their partition, mixing, coating and ageing is essential to assess their climate effect (Section 7.3.3.1.2).

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Carbonaceous aerosols receive attention in the scientific and policy arena due to their radiative forcing, and their sizeable contribution to PM in an air-quality context (Rogelj et al. , 2014b; Harmsen et al. , 2015; Shindell et al. , 2016; Haines et al. , 2017; Myhre et al. , 2017). BC exerts a positive forcing, but the forcing from carbonaceous aerosol as a whole is negative (Bond et al., 2013; Thornhill et al., 2021b). On average, carbonaceous aerosols account for 50–70% of PM with a diameter lower than 1 µm in polluted and pristine areas (Zhang et al. , 2007; Carslaw et al. , 2010; Andreae et al. , 2015; Monteiro dos Santos et al. , 2016; Chen et al. , 2017).

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In summary, the lack of global-scale observations of carbonaceous aerosols, the complexity of processes influencing them, and the large spread in their simulated global budget and burdens means that there is only low confidence in the quantification of the present-day atmospheric distribution of individual components of carbonaceous aerosols. Global trends in carbonaceous aerosols cannot be characterized due to limited observations, but sites representative of background conditions have reported multi-year declines in BC over several regions of the Northern Hemisphere.

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The highly heterogeneous distribution of SLCF abundances (Section 6.3) translates to strong heterogeneity in the spatial pattern and temporal evolution of forcing and climate responses due to SLCFs. This section assesses the spatial patterns of the current forcing due to aerosols and their historical evolution by region.

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Time evolution of 20-year means of regional net aerosol ERF shows that the regions are divided into two groups depending on whether the mean ERF attains its negative peak value in the 1970s–1980s (e.g., Europe, North America) or in the late 1990s–2000s (e.g., Asia, South America; Figure 6.11). Qualitatively, this shift in the distribution of ERF trends is consistent with the regional long-term trends in aerosol precursor emissions (Section 6.2; Figures 6.18 and 6.19) and their abundances (Section 6.3). However, at finer regional scales, there are regions where sulphate aerosols are still following an upward trend (e.g., Southern Asia; Section 6.3.5) implying that the trends in ERF may not have shifted for these regions. The continental-scale ERF trends are also in line with the satellite-observed AOD trends assessed in Section 2.2.6. Global mean ERF reaches maximum negative values in the mid-1970s and its magnitude gradually decreases thereafter. This weakening of the negative forcing since 1990 agrees with findings that attribute this to a reduction in global mean SO2 emissions combined with an increase in global BC (Myhre et al., 2017). Uncertainties in model-simulated aerosol ERF distribution and trends can result from inter-model variations in the representation of aerosol–cloud interactions and aerosol microphysical processes as also demonstrated by Bauer et al. (2020).

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Ozone-depleting substances, such as N2O and halocarbons, cause a reduction in stratospheric ozone, which affects ozone and OH production in the troposphere through ultraviolet radiation changes (and thus affect methane). They also have indirect effects on aerosols and clouds (Karset et al., 2018), since changes in oxidants induce changes in the oxidation of aerosol precursors.

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The net ERF from N2O emissions is estimated to be 0.24 [0.13 to 0.34] W m–2, which is very close to the abundance-based estimate of 0.21 W m–2Section 7.3.2.3). The indirect contributions from N2O are relatively minor with negative (methane-lifetime) and positive (ozone-and-cloud) effects nearly compensating each other. Emissions of halogenated compounds, including CFCs and HCFCs, were assessed as very likely causing a net-positive ERF in the AR5. However, recent studies (Morgenstern et al., 2020; O’Connor et al., 2021; Thornhill et al., 2021b) find strong adjustments in Southern Hemisphere aerosols and clouds, such that the very likely range in the emission-based ERF for CFC + HCFCs + HFCs now also include negative values.

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For methane emissions, in addition to their direct effect, there are indirect positive ERFs from methane enhancing its own lifetime, causing ozone production, enhancing stratospheric water vapour, and influencing aerosols and the lifetimes of HCFCs and HFCs (Myhre et al., 2013; O’Connor et al., 2021). The ERF from methane emissions is considerably higher than the ERF estimate resulting from its abundance change. The central estimate with the very likely range is 1.19 [0.81 to 1.58] W m–2 for the emissions-based estimatecompared with 0.54 W m–2 for the abundance-based estimate (Section 7.3.5). The abundance-based ERF estimate for methane results from contributions of its own emissions and the effects of several other compounds, some decreasing methane lifetime, notably NOx, which importantly reduce the methane abundance-based ERF. Emissions of CO and NMVOCs both indirectly contribute to a positive ERF through enhancing ozone production in the troposphere and increasing the methane lifetime. For CO and NMVOCs of fossil origin there is also a 0.07 W m–2 contribution to CO2 from their oxidation. The very likely total ERF of CO and NMVOCs emissions is estimated to be 0.44 [0.22 to 0.67] W m–2.

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The best estimate for the ERF due to emissions of BC is reduced from the AR5, and is now estimated to be 0.11 [–0.20 to 0.42] W m–2 with an uncertainty also including negative values. As discussed in Section 7.3.3.1.2, a significant portion of the positive BC forcing from aerosol–radiation interactions is offset by negative atmospheric adjustments due to cloud changes, as well as lapse rate and atmospheric water vapour changes, resulting in a smaller positive net ERF for BC compared with AR5. The large range in the forcing estimate stems from variation in the magnitude and sign of atmospheric adjustments across models and is related to the differences in the model treatment of different processes affecting BC (e.g., ageing, mixing) and its interactions with clouds and cryosphere (Section 7.3.3; Thornhill et al., 2021b). The emissions-based ERF for organic carbon aerosols is –0.21 [–0.44 to +0.02] W m–2, a weaker estimate compared with AR5 attributed to stronger absorption by OC (Section 7.3.3.1.2).

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This section briefly discusses the climate response to SLCFs, in particular to changes in aerosols, and gathers complementary information and assessments from Chapters 3, 7, 8 and 10.

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Warming or cooling atmospheric aerosols, such as BC and sulphate, can affect temperature and precipitation in distinct ways by modifying the shortwave and longwave radiation, the lapse rate of the troposphere, and influencing cloud microphysical properties (Section 10.1.4, Box 8.1). An important distinction between scattering and absorbing aerosols is the opposing nature of their influences on circulation, clouds and precipitation, besides surface temperature as evident from the contrasting regional climate responses to regional aerosol emissions (e.g., Lewinschal et al., 2019; Sand et al., 2020; also see Chapters 8 and 10).

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In AR5, there was low confidence in the overall understanding of climate response to spatially varying patterns of forcing, though there was medium to high confidence in some regional climate responses, such as the damped warming of the NH and shifting of the ITCZ from aerosols, and positive feedbacks enhancing the local response from high-latitude snow and ice albedo changes. Since AR5, the relationship between inhomogeneous forcing and climate response is better understood, providing further evidence of the climate influence of SLCFs (aerosols and ozone in particular) on global to regional scales (Collins et al., 2013; Shindell et al., 2015; Aamaas et al., 2017; Kasoar et al., 2018; Persad and Caldeira, 2018; Wilcox et al., 2019) which differ from the relatively homogeneous spatial influence from LLGHGs.

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Large geographical variations in aerosol ERFs (Section 6.4.1) affect global and regional temperature responses (Myhre et al., 2013; Shindell et al., 2015). Multi-model CMIP6 ensemble-mean results (Figure 6.13) show cooling over almost all areas of the globe in response to increases of aerosol and their precursor emissions from 1850 to the recent past (1995–2014). While the ERF has hotspots, the temperature response is more evenly distributed in line with the results of CMIP5 models including the temperature response to ozone changes (Shindell et al., 2015). The ensemble-mean global mean surface temperature decreases by 0.66°C ± 0.51°C while decreasing by 0.97°C ± 0.54°C for the Northern Hemisphere and 0.34°C ± 0.2°C for the Southern Hemisphere. The zonal-mean temperature response is negative at all latitudes (high confidence) and becomes more negative with increasing latitude, with a maximum ensemble-mean decrease of around 2.7°C at northern polar latitudes. The zonal-mean response is not directly proportional to the zonal-mean forcing, especially in the Arctic where the temperature response is cooling while the local ERF is positive (Figure 6.10). This is consistent with prior studies showing that the Arctic, in particular, is highly sensitive to forcing at NH mid-latitudes (e.g., Shindell and Faluvegi, 2009; Sand et al., 2013a) and with results from CMIP5 models (more on the Arctic below; Shindell et al., 2015). Thus, there is high confidence that the temperature response to aerosols is more asymmetric than the response to WMGHGs and negative at all latitudes.

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The Arctic region is warming considerably faster than the rest of the globe (Atlas 11.2.2) and, generally, studies indicate that this amplification of the temperature response toward the Arctic has an important contribution from local and remote aerosol forcing (Stjern et al., 2017; Westervelt et al., 2018). Several studies indicate that changes in long-range transport of sulphate and BC from northern mid-latitudes can potentially explain a significant fraction of Arctic warming since the 1980s (e.g., Navarro et al. , 2016; Breider et al. , 2017; Ren et al. , 2020). Modelling studies show that changes in mid-latitude aerosols have influenced Arctic climate by changing the radiative balance through aerosol–radiation and aerosol–cloud interactions, and enhancing poleward heat transport (Navarro et al., 2016; Ren et al., 2020). Idealized aerosol-perturbation studies have shed further light on the sensitivity of Arctic temperature response to individual aerosol species. Studies show relatively large responses in the Arctic to BC perturbations and reveal the importance of remote BC forcing by rapid adjustments (Sand et al. , 2013b; Stjern et al. , 2017; L. Liu et al. , 2018; Yang et al. , 2019b). Perturbations in SO2 emissions over major emitting regions in the Northern Hemisphere have been shown to produce the largest Arctic temperature responses (Kasoar et al., 2018; Lewinschal et al., 2019).

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The effects of changes in aerosols on local and remote changes in temperature, circulation and precipitation are sensitive to a number of model uncertainties affecting aerosol sources, transformation and resulting radiative efficacy. Therefore, regional climate effects in global model studies must be interpreted with caution. When investigating the climate response to regional aerosol emissions, such uncertainties are likely to be confounded even further by the variability between models in regional climate and circulation patterns, leading to greater inter-model spread at regional scales than at a global scale (Baker et al., 2015; Kasoar et al., 2016).

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In summary, over the historical period, changes in aerosols and their ERF have primarily contributed to cooling, partly masking the human-induced warming (high confidence). Radiative forcings induced by aerosol changes lead to both local and remote changes in temperature (high confidence). The temperature response preserves hemispheric asymmetry of the ERF but is more latitudinally uniform with strong amplification of the temperature response towards the Arctic (medium confidence).

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In summary, reactive nitrogen, ozone and aerosols affect terrestrial vegetation and the carbon cycle through deposition and effects on large-scale radiation (high confidence) but the magnitude of these effects on the land carbon sink, ecosystem productivity and indirect CO2 forcing remain uncertain due to the difficulty in disentangling the complex interactions between the effects. As such, we assess the effects to be of second order in comparison to the direct CO2 forcing (high confidence) but, at least for ozone, it could add a substantial (positive) forcing compared with its direct forcing (low confidence).

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Climate–sea-spray feedback: Sea-spray emissions from ocean surfaces influence climate directly or indirectly through the formation of CCN as discused in Section 6.2.1.2. They are sensitive to SST and sea ice extent, as well as to wind speed, and are therefore expected to feedback on climate (Struthers et al., 2013). However, there are large uncertainties in the strength of climate feedback from sea-spray aerosols because of the diversity in the model representation of emissions (many represent sea-salt emissions only) and their functional dependence on environmental factors noted above, in situ atmospheric chemical and physical processes affecting the sea-spray lifetime, and aerosol–cloud interactions (Struthers et al., 2013; Soares et al., 2016; Nazarenko et al., 2017). Additional work is needed to identify how sea-spray and POA emissions respond to shifts in ocean biology and chemistry in response to warming, ocean acidification and changes in circulation patterns (Cochran et al. , 2017), and affect CCN and INP formation (DeMott et al., 2016). AerChemMIP models, representing only the sea-salt emissions, agree that the sea-salt-climate feedback is negative, however there is a large range in the feedback parameter indicating large uncertainties (Table 6.8).

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Climate–BVOC feedback: BVOCs, such as isoprene and terpenes, are produced by land vegetation and marine plankton (Sections 6.2.2.3 and 6.2.2.5). Once in the atmosphere, BVOCs and their oxidation products lead to the formation of secondary organic aerosols (SOA) exerting a negative forcing, and increased ozone concentrations and methane lifetime exerting a positive forcing. BVOC emissions are suggested to lead to a climate feedback in part because of their strong temperature dependence observed under present-day conditions (Kulmala et al., 2004; Arneth et al., 2010a). Their response to future changes in climate and CO2 levels remains uncertain (Section 6.2.2.3). Estimates of the climate-BVOC feedback parameter are typically based on global models which vary in their level of complexity of emissions parametrization, BVOC speciation, the mechanism of SOA formation and the interaction with ozone chemistry (Thornhill et al., 2021a). Since AR5, observational studies (Paasonen et al., 2013) and models (Scott et al., 2018) estimate the feedback due to biogenic SOA (via changes in BVOC emissions) to be in the range of about –0.06 to –0.01 W m–2°C–1. The assessed central estimate of the climate-BVOC feedback parameter based on the AerChemMIP ensemble suggests that climate-induced increases in SOA from BVOCs will lead to a strong cooling effect that will outweigh the warming from increased ozone and methane lifetime, however the uncertainty is large (Thornhill et al., 2021a).

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Climate–fire feedback: Wildfires are a major source of SLCF emissions (Section 6.2.2.6). Climate change has the potential to enhance fire activity (Sections 12.4 and 5.4.3.2) thereby enhancing SLCF emissions leading to feedbacks. Climate-driven increases in fire could potentially lead to offsetting feedback from increased ozone and decreased methane lifetime (due to increases in OH) leaving the feedback from aerosols to dominate with an uncertain net effect (e.g., Landry et al., 2015). The AR5 assessment of climate-fire feedbacks included a value of α due to fire aerosols to be in the range of –0.03 to + 0.06 W m−2°C−1based on Arneth et al. (2010a) . A recent study estimates climate feedback due to fire aerosols to be greater than that due to BVOCs, with a value of α equal to –0.15 (–0.24 to –0.05) W m−2°C−1 (Scott et al., 2018). Clearly, the assessment of fire-related non-CO2 biogeochemical feedbacks is very uncertain because of limitations in the process understanding of the interactions between climate, vegetation and fire dynamics, and atmospheric chemistry and their representation in the current generation ESMs. Some AerChemMIP ESMs include the representation of fire dynamics but do not activate their interaction with atmospheric chemistry. Given the large uncertainty and lack of information from AerChemMIP ESMs, we do not include a quantitative assessment of climate-fire feedback for AR6.

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Stratospheric aerosol injections (SAI) have the potential to achieve a high negative global ERF, with maximum ERFs ranging from –5 to –2 W m–2 (Niemeier and Timmreck, 2015; Weisenstein et al., 2015; Niemeier and Schmidt, 2017; Kleinschmitt et al., 2018). The magnitude of the maximum achievable ERF depends on the chosen aerosol type and mixture, internal structure and size, or precursor gas (e.g., SO2), as well as the injection strategy (latitude, altitude, magnitude and season of injections), plume dispersal, model representation of aerosol microphysics, and ambient aerosol concentrations (Rasch et al. , 2008; Robock et al. , 2008; Pierce et al. , 2010; Weisenstein et al. , 2015; Laakso et al. , 2017; MacMartin et al. , 2017; Dai et al. , 2018; Kleinschmitt et al. , 2018; Vattioni et al. , 2019; Visioni et al. , 2019). For sulphur, the radiative forcing efficiency is of around –0.1 to –0.4 W m–2/(TgS yr–1) (Niemeier and Timmreck, 2015; Weisenstein et al., 2015; Niemeier and Schmidt, 2017). Different manufactured aerosols, such as ZrO2, TiO2 and Al2O3, have different ERF efficiencies compared to sulphate (Ferraro et al. , 2011; Weisenstein et al. , 2015; Dykema et al. , 2016; Jones et al. , 2016). The aerosol size distribution influences the optical properties of an aerosol layer, and hence the ERF efficiency, which also depends on the dispersion, transport, and residence time of the aerosols.

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For marine cloud brightening(MCB), seeded aerosols may affect both cloud microphysical and macrophysical properties (Section 7.3.3.2). By principle, MCB relies on ERFaci through the so-called Twomey effect (Twomey, 1977), but ERFari may be of equal magnitude as shown in studies that consider spraying of sea salt outside tropical marine cloud areas (Jones and Haywood, 2012; Partanen et al., 2012; Alterskjaer and Kristjánsson, 2013; Ahlm et al., 2017). The maximum negative ERF estimated from modelling is within the range of –5.4 to –0.8 W m–2 (Latham et al. , 2008; Rasch et al. , 2009; Jones et al. , 2011; Partanen et al. , 2012; Alterskjaer and Kristjánsson, 2013). For dry sea salt, the ERF efficiency is estimated to be within the range of –3 to –10 W m–2/(Pg yr–1), when emitted over tropical oceans in ESMs in the Geoengineering Intercomparison Project (GeoMIP; Ahlm et al., 2017). Cloud-resolving models reveal the complex behaviour and response of stratocumulus clouds to seeding, in that the ERF efficiency depends on meteorological conditions, and the ambient aerosol composition, where lower background particle concentrations may increase the ERFaci efficiency (Wang et al., 2011). Seeding could suppress precipitation formation and drizzle, and hence increase the lifetime of clouds, preserving their cooling effect (Ferek et al., 2000). In contrast, cloud lifetime could be decreased by making the smaller droplets more susceptible to evaporation. Modelling studies have shown that a positive ERFaci (warming) could also result from seeding clouds with too large aerosols (Pringle et al., 2012; Alterskjaer and Kristjánsson, 2013). These individual and combined processes are not well understood, and may have a limited representation in models, or counteracting errors (Mülmenstädt and Feingold, 2018), lending low to medium confidence to the ERF estimates.

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Air pollutants can be impacted by climate change through physical changes affecting meterorological conditions, chemical changes affecting their lifetimes, and biological changes affecting their natural emissions (Kirtman et al., 2013). Changes in meteorology affect air quality directly through modifications of atmospheric transport patterns (e.g., occurrence and length of atmospheric blocking episodes, ventilation of the polluted boundary layer), extent of mixing layer and stratosphere–troposphere exchange (STE) for surface ozone (von Schneidemesser et al., 2015), and through modifications of the rate of reactions that generate secondary species in the atmosphere. Changing precipitation patterns in a future climate also influence the wet removal efficiency, in particular for atmospheric aerosols (Hou et al., 2018). Processes at play in non-CO2 biogeochemical feedbacks (Section 6.4.5) are also involved in the perturbation of atmospheric pollutants (Section 6.2.2).

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Higher temperatures increase the reaction rate of gaseous SO2 to particulate sulphate conversion but also favour evaporation of particulate ammonium nitrate (Megaritis et al., 2013). Also, higher temperatures are expected to affect BVOC emissions (e.g., Pacifico et al., 2012) that would influence SOA concentrations, although this effect has been questioned by more recent evidence (B. Wang et al., 2018; Z. Zhao et al., 2019). More generally, climate change will also affect dust concentration levels in the atmosphere (Section 6.2.2.4) and the occurrence of forest fires, both very large sources of aerosols to the global troposphere (Section 6.2.2.6).

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At the global scale, depending on its magnitude, the warming leads either to a small increase in global mean PM concentration levels (about 0.21 µg m–3 in 2100 for RCP8.5), mainly controlled by sulphate and organic aerosols or a small decrease (–0.06 µg m–3 for RCP2.6, Westervelt et al. (2016) and Xu and Lamarque (2018)). On the other hand, Xu and Lamarque (2018) and Allen et al. (2016, 2019) found an increase of aerosol burden and PM surface concentration throughout the 21st Century, attributed to a decrease in wet-removal flux despite the overall projected increase in global precipitation, on the ground of an expected shift of future precipitation towards more frequent heavy events. Based only on three models, the CMIP6 ensembleshows that for most land areas, there is low agreement between models on the sign of the effect of climate change on annual mean PM2.5 (Supplementary Material Figure 6.SM.2).

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According to the SRCCL assessment (Jia et al., 2019), agriculture, forestry and other land use (AFOLU) are a significant net source of GHG emissions (high confidence), with more than half of these emissions attributed to non-CO2 gHGs from agriculture. With respect to SLCFs, agricultural activities are major global sources of methane and NH3 (Section 6.2.1). The agriculture sector exerts strong near-term warming due to large methane emissions that is slightly offset by a small cooling from secondary inorganic aerosols formed notably from the NH3 emissions (Heald and Geddes, 2016; Lund et al., 2020). For present-day emissions, agriculture is the second largest contributor to warming on short time scales but with a small persisting effect on surface temperature (+0.0012°C ± 0.00028°C) after a pulse of current emissions (Figure 6.16, see detailed description in Section 6.6.2.3.4; Lund et al., 2020). Aerosols produced from agricultural emissions, released after nitrogen fertilizer application and from animal husbandry, influence surface air quality and make an important contribution to surface PM2.5 in many densely populated areas ( Figure 6.17; Lelieveld et al., 2015b; Bauer et al., 2016).

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The residential and commercial sector is associated with SLCF emissions of carbonaceous aerosols, CO and NMVOCs, SO2 and NOx, and can be split by fuel type (biofuel or fossil fuel) where residential fossil fuel is also associated with CO2 and methane emissions (Section 6.2.1).

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The net effect of residential CO and NMVOC emissions is warming and that of SO2 and NOx is cooling of the atmosphere. However, the sign of the net global radiative effects of carbonaceous aerosols from the residential sector and solid-fuel cookstove emissions (warming or cooling) is not well constrained based on evidence from recent global atmospheric modelling studies. Estimates of direct aerosolradiation and aerosolcloud effects fromtheglobal residential sector range from –20 to +60 mW m–2 (Kodros et al., 2015) and –66 to +21 mW m–2 (Butt et al., 2016) and from –20 to +10 mW m–2 (Kodros et al., 2015) and –52 to –16 mW m–2 (Butt et al., 2016), respectively. Uncertainties are due to assumptions about the aerosol emissions masses, size distribution, aerosol optical properties and mixing states (Section 6.3.5.3). Allowing BC to act as an INP in a global model leads to a much larger global forcing estimate from –275 to +154 mW m–2 with a large uncertainty range due to uncertainty in the plausible range of maximum freezing efficiency of BC (Huang et al. , 2018). The residential biofuel sector is a major concern for indoor air quality (Bonjour et al. , 2013). In addition, several atmospheric modelling studies find that this sector is also important for outdoor air quality and even a dominant source of population-weighted outdoor PM2.5 in India and China (Lelieveld et al. 2015b, Silva et al. , 2016; Spracklen et al. , 2018; Reddington et al. , 2019).

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Our assessment is built upon Lee et al. (2021). Their study consists of an updated, comprehensive assessment of aviation climate forcing in terms of RF and ERF based on a large number of studies and the most recent air-traffic and fuel-use datasets available (for 2018), new calculations and the normalization of values from published modelling studies, and combining the resulting best estimates via a Monte-Carlo analysis. Lee et al. (2021) report a net aviation ERF for year-2018 emissions of +0.101 [0.055–0.145] W m–2 with major contributions from contrail cirrus (0.057 W m–2), CO2 (0.034 W m–2) and NOx (0.017 W m–2). Contrails and aviation-induced cirrus yield the largest individual positive ERF followed by CO2 and NOx emissions (Lee et al. 2021). The confidence level in ERF due to contrails and aviation-induced cirrus is assessed to be low in Chapter 7 (Section 7.3.4.2) due to potential missing processes. The formation and emission of sulphate aerosols yield a negative (cooling) term. SLCF forcing terms contribute about eight times more than CO2 to the uncertainty in the aviation net ERF in 2018 (Lee et al., 2021). The largest uncertainty in assessing aviation climate effects is on the interactions of BC and sulphate aerosols on cirrus and mixed-phase clouds, for which no best estimates of the ERFs were provided (Lee et al., 2021).

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The vehicle tailpipe emissions profiles of diesel and gasoline are distinctly different. Diesel air pollutant emissions are dominated by BC and NOx whereas gasoline air pollutant emissions are dominated by CO and NMVOCs, especially when distribution and upstream losses are considered. Thus, the net radiative effect of the on-road vehicle fleets depends upon the share of different fuels used, in particular gasoline and diesel (Lund et al., 2014; Huang et al., 2020). The net SLCF for year-2010 emissions from the global diesel vehicle fleet have been estimated to be +28 mW m–2 (Lund et al., 2014). Huang et al. (2020) estimated net global radiative effects of SLCFs (including aerosols, ozone, and methane) from the gasoline and diesel vehicle fleets in the year 2015 to be +13.6 and +9.4 mW m–2, respectively, with similar fractional contributions of SLCFs to the total global climate impact including CO2 on the 20‐year time scale (14–15%).

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In terms of source regions, the largest contributions to net short-term warming are caused by emissions in Eastern Asia, Latin America and North America, followed by Africa, Eastern Europe, West-Central Asia and South East Asia (medium confidence). However, the relative contributions from individual species vary. In Eastern Asia, North America, Europe and Southern Asia, the effect of current emissions of cooling and warming SLCFs approximately balance in the near term and these regions cause comparable net warming effects on 10- and 100-year time horizons (Figure 6.16). In Latin America, Africa, and South East Asia and Developing Pacific, methane and BC emissions are currently high while emissions of CO2 and cooling aerosols are low compared to other regions, resulting in a net warming effect after 10 years that is substantially higher than that of CO2 alone.

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Air-quality policies emerged several decades ago focusing on emissions mitigation, first driven by local- then by regional-scale air-quality and ecosystem-damage concerns, that is, health impacts, acidification and eutrophication. They have made it possible to reduce or limit pollution exposure in many megacities or highly populated regions, for example, in Los Angeles, Mexico City and Houston in North America (Parrish et al., 2011), Santiago in Chile (Gallardo et al., 2018), São Paulo in Brazil (Andrade et al., 2017), Europe (Reis et al., 2012; Crippa et al., 2016; Serrano et al., 2019), and over Eastern Asia during the last decade (Silver et al., 2018; Zheng et al., 2018b). However, very few studies have quantified the impact of these policies on climate. The AR5 concluded that air-quality control will have consequences on climate including strong regional variability, however, no estimates of impacts of specific air-quality policy were available. Since AR5, few studies have provided estimates of climate-relevant indicators affected by significant air pollutant burden changes due to air-quality policy in selected regions. Turnock et al. (2016) estimated that the strong decrease in NOx, SO2 and PM2.5 emissions in Europe, induced by air-quality policies resulting in implementation of abatement measures since the 1970s, have caused a surface warming of +0.45°C ± 0.11°C and increase of precipitation +13 ± 0.8 mm yr–1 over Europe, compared to the scenario without such policies. While the temperature increase is likely overestimated since the impact of the increase in ammonium nitrate was not considered in this study, the simulated European all-sky TOA radiative effect of the European air pollutant mitigation over the period 1970–2009 is 2.5 times the change in global mean CO2 radiative forcing over the same period (Myhre et al., 2013). Other studies found that the recent measures to reduce pollution over China have induced a decrease of aerosols and increase of ozone over east China (K. Li et al., 2019, 2020), resulting in an overall warming effect mainly due to the dominant effect of sulphate reductions in the period 2012–2017 (Dang and Liao, 2019).

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International shipping emissions regulation: from January 2020, a new global standard, proposed by the International Maritime Organisation, limits the sulphur content in marine fuels to 0.5% against the previous 3.5% (IMO, 2016). This legislation is considered in the SSP5 and SSP2-4.5 and with a delay of few years in SSP3-lowSLCF, SSP1-1.9, and SSP1-2.6, and in other SSP-emissions scenarios achieved by the mid-21st century. This global measure aims to reduce the formation of sulphate (and consequently PM2.5) and largely reduce the health exposure to PM2.5, especially over India, east China and coastal areas of Africa, and the Middle East (Sofiev et al., 2018). Sofiev et al. (2018) used a high spatial-and-temporal resolution chemistry climate model and estimated a net total ERF of +71 mW m–2 Associated with this measure and due to lower direct aerosol cooling (+3.9 mW m–2) and lower cloud albedo (+67 mW m–2). This value, which correponds to an 80% decrease of the cooling effect of shipping induced by about 8 Tg of SO2 of avoided emissions, is consistent with older estimates which considered similar reduction of emitted sulphur. However, there is considerable uncertainty in the indirect forcing since small changes in aerosols, acting as CCNs in a clean environment, can have disproportionally large effects on the radiative balance. Since sulphate is by far the largest component of the radiative forcing (Fuglestvedt et al., 2008) and of surface temperature effect (Figure 6.16) due to ship emissions over a short time scale, limiting the co-emitted SLCFs can not offset the warming by sulphur reductions. The reduction of sulphur emissions from shipping is assessed to lead to a slight warming mainly due to aerosol–cloud interactions (medium evidence, medium agreement).

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Focusing on air quality, specifically addressing aerosols, by introducing the best available technology reducing PM2.5, SO2 and NOx in most Asian countries within the 2030–2050 time frame (a strategy that has indeed shown reduction in PM2.5 exposure in China) comes, in many regions, short of national regulatory PM2.5 concentration standards (often set at 35 µg m–3 for annual mean; UNEP, 2019). Similarly, global studies (Rafaj et al., 2018; Amann et al., 2020) show that strengthening current air-quality policies, that address primarily aerosols and their precursors, will not enable the achievement of WHO air quality guidelines (annual average concentration of PM2.54 below 10 µg m–3) in many regions.

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While there is robust evidence that air-quality policies resulting in reductions of aerosols and ozone can be beneficial for human health but can lead to ‘disbenefits’ for near-term climate change, the existence of such trade-offs in response to climate change mitigation policies is less certain (Shindell and Smith, 2019). Recent studies show that very ambitious but plausible gradual phasing out of fossil fuels in 1.5°C-compatible pathways with little or no overshoot, lead to a near-term future warming of less than 0.1°C, when considering associated emissions reduction of both warming and cooling species. This suggests that there may not be a strong conflict, at least at the global scale, between climate and air-quality benefits in the case of a worldwide transition to clean energy (Shindell and Smith, 2019; Smith et al., 2019). However, at the regional scale, the changes in spatially variable emissions and abundance changes might result in different responses, including implications for precipitation and monsoons (Chapter 8), especially over Southern Asia (e.g., Wilcox et al., 2020).

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Cross-Chapter Box 6.1, Figure 1 | Emissions reductions and their effect on aerosols and climate in response to COVID-19. Estimated reductions in emissions of CO2, SO2 and NOx are shown in panel (a) based on reconstructions using activity data (updated from Forster et al., 2020). Eight Earth system models (ESMs) performed multiple ensemble simulations of the response to COVID-19 emissions reductions forced with these assumed emissions reductions up until August 2020 followed by a constant continuation near the August value to the end of 2020. Emissions reductions were applied relative to the SSP2-4.5 scenario. Panel (b) shows ESM-simulated AOD at 550nm (only seven models reported this variable). Panel (c) shows ESM-simulated GSAT anomalies during 2020; curves denote the ensemble mean result for each model with shading used for ±1 standard deviation for each model. ESM data from these simulations (‘ssp245-covid’) is archived on the Earth System Grid CMIP6 database. Uncertainty is represented using the simple approach: no overlay indicates regions with high model agreement, where ≥80% of models agree on sign of change; diagonal lines indicate regions with low model agreement, where <80% of models agree on sign of change. For more information on the simple approach, please refer to the Cross-Chapter Box Atlas.1.

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Model simulations of the response to COVID-19 emissions reductions indicate a small warming of global surface air temperature (GSAT) due to a decrease in sulphate aerosols (Forster et al., 2020; Fyfe et al., 2021), balanced by cooling due to an ozone decrease (Forster et al., 2020; Weber et al., 2020), black carbon decrease (Weber et al., 2020) and CO2 decrease. It is noted that observational studies report little SO2 change, at least locally near the surface (Shi et al., 2021), and do not correlate with emissions inventory-based changes (Gkatzelis et al., 2021). One study suggests a small net warming while another using idealized simulations suggests a small cooling (Weber et al., 2020). Simulated GSAT and rainfall changes are unlikely to be detectable in observations (high confidence) (Samset et al., 2020; Fyfe et al., 2021). Multi-model ESM simulations based on a realistic COVID-19 containment forcing scenario (Forster et al., 2020) indicate a model mean reduction in regional AOD but no discernible response in GSAT (Figure 1, Cross-Chapter Box 6.1).

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Historical emissions have been updated until 2019 (see Supplementary Material 7.SM.1.3.1) and used for ERF for calculating GSAT in Figure 6.22. The year 2019 has been chosen as the base year to be consistent with the attributed temperature changes since 1750 (Figure 7.8). The warming attributed to SLCFs (methane, ozone and aerosols) over the last decade (Figure 7.8) constitutes about 30% of the peak SLCF-driven warming in the most stringent scenarios (SSP1), in good agreement with Shindell and Smith (2019), and supported by the recent observed decline in AOD (Section 2.2.6).

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From 2019 and until about 2040, SLCFs and HFCs will contribute to increase GSAT in the WGI core set of SSP scenarios, with a very likely range of 0.04°C–0.41°C relative to 2019. The warming is most pronounced in the strong mitigation scenarios (i.e., SSP1-1.9 and SSP1-2.6) due to rapid cuts in aerosols. In scenario SSP3-7.0, there is no reduction of aerosols until mid-century and it is the increases in methane and ozone that give a net warming in 2040. The warming is similar in magnitude to that in the SSP1-scenarios, in which the reduction in aerosols is the main driver. Contributions to warming from methane, ozone, aerosols and HFCs make SSP5-8.5 the scenario with the highest warming in 2040 and throughout the century.

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The simplified approach used to estimate the contributions to GSAT in Figure 6.22 has been supplemented with ESM simulations driven by the two versions of the SSP3-7.0-lowSLCF scenario (Section 6.7.1.1). Results from five CMIP6 ESMs with fully interactive atmospheric chemistry and aerosols for the high-methane scenario show (Allen et al., 2020, 2021) that reductions in emissions of air pollutants would lead to an additional increase in GSAT by 2055 relative to 2015 compared to the standard SSP3-7.0 scenario, with a best estimate of 0.23°C ± 0.05°C, and a corresponding increase in global mean precipitation of 1.3 ± 0.17% (note that uncertainties from the work of Allen et al. here and elsewhere are reported as twice standard deviation). Including methane mitigation (SSP3-7.0-lowSLCF-lowCH4) would lead to a small increase in global precipitation (0.7 ± 0.1%) by mid-century despite a decrease in GSAT (Section 6.7.3), which is related to the higher sensitivity of precipitation to sulphate aerosols than greenhouse gases (Section 8.2.1; Allen et al., 2021).

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Regionally inhomogeneous ERFs can lead to regionally dependent responses (Section 6.4.3). Mitigation of non-methane SLCFs over the period 2015–2055 (SSP3-7.0-lowSLCF-highCH4 versus SSP3-7.0) will lead to positive ERF over land regions (Allen et al. , 2020). There are large regional differences in the ERF from no significant trend over northern Africa to about 0.5 W m–2 decade–1 for Southern Asia. The differences are mainly driven by differences in the reductions of sulphate aerosols. There is no strong correspondence between regional warming and the ERF trends. As expected, the sensitivity (temperature change per unit ERF) increases towards higher latitudes due to climate feedbacks and teleconnections. Regionally, the warming rates are higher over continental regions, with the highest increase in temperatures for Central and northern Asia and the Arctic in 2055 relative to 2015. The models agree on an increasing global mean trend in precipitation due to SLCFs, however precipitation trends over land are more uncertain (Allen et al., 2020), in agreement with the relationship between aerosol and precipitation trends assessed in Chapter 8.

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The SSP3 storyline assumes ‘regional rivalry’ (Section 1.6.1.1) with weak air pollution legislation and no climate change mitigation, and is compared here against SSP3-7.0-lowSLCF-lowCH4 (strong air pollution control) and SSP3-3.4 (the most ambitious climate policy feasible under the SSP3 narrative). In the SSP3-3.4 scenario, all emissions follow the SSP3-7.0 scenario until about 2030 and then deep and rapid cuts in fossil fuel use are imposed (Fujimori et al., 2017). In the case of climate change mitigation, such as in the SSP3-3.4 scenario, the decrease of SLCF emissions is a co-benefit from the targeted decrease of CO2 (when SLCFs are co-emitted), but also directly targeted as in the case of methane. For SLCFs, this means that emissions of aerosols and methane increase until 2030 and are reduced quickly thereafter (Fujimori et al., 2017). The effect on GSAT (relative to 2019) is shown in Figures 6.22 and 6.24. The net GSAT response to the SLCFs is dominated by the aerosols, with an initial cooling until 2030, then a fast rebound for 15 years followed by a very moderate warming reaching 0.21°C in 2100. The ozone reduction causes a slight cooling (up to 0.06°C), in contrast to the warming in the SSP3-7.0-lowSLCF-highCH4 scenario in which the methane emissions increase until 2100.

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Ice-core analyses can now inform trends for more SLCFs over the last millennium (such as light NMVOCs or CO) and more proxies are available to inform about past emissions. However, pre-industrial levels of SLCFs are still relatively poorly constrained. In addition, recent trends in abundances of the various types of aerosols and of NMVOCs suffer from the scarcity of observation networks in various parts of the world, in particular in the Southern Hemisphere. Such network development is necessary to record and understand the evolution of atmospheric composition.

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Short-lived climate forcers (SLCFs) are compounds such as methane and sulphate aerosols that warm or cool the Earth’s climate over shorter time scales – from days to years – than greenhouse gases like carbon dioxide, whose climatic effect lasts for decades, centuries or more. Because SLCFs do not remain in the atmosphere for very long, their effects on the climate are different from one region to another and can change rapidly in response to changes in SLCF emissions. As some SLCFs also negatively affect air quality, measures to improve air quality have resulted in sharp reductions in emissions and concentrations of those SLCFs in many regions over the few last decades.

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Lin, J.T. et al., 2015: Influence of aerosols and surface reflectance on satellite NO2 retrieval: Seasonal and spatial characteristics and implications for NOx emission constraints. Atmospheric Chemistry and Physics, 15(19), 11217–11241, doi: 10.5194/acp-15-11217-2015.

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Ohata, S., N. Moteki, T. Mori, M. Koike, and Y. Kondo, 2016: A key process controlling the wet removal of aerosols: New observational evidence. Scientific Reports, 6(1), 1–9, doi: 10.1038/srep34113.

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Oliveira, P.H.F. et al., 2007: The effects of biomass burning aerosols and clouds on the CO2 flux in Amazonia. Tellus B: Chemical and Physical Meteorology, 59(3), 338–349, doi: 10.1111/j.1600-0889.2007.00270.x.

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Paulot, F. et al., 2016: Sensitivity of nitrate aerosols to ammonia emissions and to nitrate chemistry: Implications for present and future nitrate optical depth. Atmospheric Chemistry and Physics, 16(3), 1459–1477, doi: 10.5194/acp-16-1459-2016.

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Ren, L. et al., 2020: Source attribution of Arctic black carbon and sulfate aerosols and associated Arctic surface warming during 1980–2018. Atmospheric Chemistry and Physics, 20(14), 9067–9085, doi: 10.5194/acp-20-9067-2020.

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Scott, C.E. et al., 2018: Substantial large-scale feedbacks between natural aerosols and climate. Nature Geoscience, 11(1), 44–48, doi: 10.1038/s41561-017-0020-5.

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Thornhill, G.D. et al., 2021b: Effective radiative forcing from emissions of reactive gases and aerosols – a multi-model comparison. Atmospheric Chemistry and Physics, 21(2), 853–874, doi: 10.5194/acp-21-853-2021.

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Given that TCR and ECS are metrics of GSAT response to a theoretical doubling of atmospheric CO2 (Box 7.1), they do not directly correspond to the warming that would occur under realistic forcing scenarios that include time-varying CO2 concentrations and non-CO2 forcing agents (such as aerosols and land-use changes). It has been argued that TCR, as a metric of transient warming, is more policy-relevant than ECS (Frame et al., 2006; Schwartz, 2018). However, as detailed in Chapter 4, both established and recent results (Forster et al., 2013; Gregory et al., 2015; Marotzke and Forster, 2015; Grose et al., 2018; Marotzke, 2019) indicate that TCR and ECS help explain variation across climate models both over the historical period and across a range of concentration-driven future scenarios. In emission-driven scenarios the carbon cycle response is also important (Smith et al., 2019). The proportion of variation explained by ECS and TCR varies with scenario and the time period considered, but both past and future surface warming depend on these metrics (Section 7.5.7).

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For comparison with offline radiative transfer calculations the SARFs can be approximated by removing the adjustment terms (apart from stratospheric temperature) from the ERFs using radiative kernels to quantify the adjustment for each meteorological variable. Kernel analysis by Chung and Soden (2015) suggested a large spread in CO2 SARF across climate models, but their analysis was based on regressing variables in a coupled-ocean experiment rather than using a fSST approach which leads to a large spread due to natural variability (Forster et al., 2016). Adjustments computed from radiative kernels are shown for seven different climate drivers (using a fSST approach) in Figure 7.4. Table 7.2 shows the estimates of SARF, ΔFfsst and ERF (corrected for land surface temperature change) for 2×CO2 from the nine climate models analysed in Smith et al. (2018b). The SARF shows a smaller spread over previous studies (Pincus et al., 2016; Soden et al., 2018) and most estimates are within 10% of the multi-model mean and the assessment of 2×CO2 SARF in (Section 7.3.2 (3.75 W m–2). It is not possible from these studies to determine how much of this reduction in spread is due to convergence in the model radiation schemes or the meteorological conditions of the model base states; nevertheless the level of agreement in this and earlier intercomparisons gives medium confidence in the ability of ESMs to represent radiative forcing from CO2. The 4×CO2 CMIP6 fSST experiments (Smith et al., 2020b) in Table 7.2 include ESMs with varying levels of complexity in aerosols and reactive gas chemistry. The CMIP6 experimental setup allows for further climate effects of CO2 (including on aerosols and ozone) depending on model complexity. The chemical effects are adjustments to CO2 but are not separable from the SARF in the diagnosis in Table 7.2. In these particular models, this leads to higher SARF than when only CO2 varies, however there are insufficient studies to make a formal assessment of composition adjustments to CO2.

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In AR5, the assessment of the black carbon (BC) contribution to IRFari was markedly strengthened in confidence by the review by Bond et al. (2013), where a key finding was a perceived model underestimate of atmospheric absorption when compared to Aeronet observations (Boucher et al., 2013). This assessment has since been revised considering: new knowledge on the effect of the temporal resolution of emissions inventories (Wang et al., 2016); the representativeness of Aeronet sites (Wang et al., 2018); issues with comparing absorption retrieval to models (E. Andrews et al., 2017); and the ageing (Peng et al., 2016), lifetime (Lund et al., 2018b) and average optical parameters (Zanatta et al., 2016) of BC. Consistent with these updates, Lund et al. (2018a) estimated the net IRFari in 2014 (relative to 1750) to be –0.17 W m–2, using CEDS emissions (Hoesly et al., 2018) as input to a chemical transport model. They attributed the weaker estimate relative to AR5 (–0.35 ± 0.5 W m–2; Myhre et al., 2013a) to stronger absorption by organic aerosol, updated parametrization of BC absorption, and slightly reduced sulphate cooling. Broadly consistent with Lund et al. (2018a) , another single-model study by Petersik et al. (2018) estimated an IRFari of –0.19 W m–2. Another single-model study by Lurton et al. (2020) reported a more negative estimate at –0.38 W m–2, but is given less weight here because the model lacked interactive aerosols and instead used prescribed climatological aerosol concentrations.

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Since AR5 considerable progress has been made in the understanding of adjustments in response to a wide range of climate forcings, as discussed in (Section 7.3.1. The adjustments in ERFari are principally caused by cloud changes, but also by lapse rate and atmospheric water vapour changes, all mainly associated with absorbing aerosols like BC. Stjern et al. (2017) found that for BC, about 30% of the (positive) IRFari is offset by adjustments of clouds (specifically, an increase in low-clouds and decrease in high-clouds) and lapse rate, by analysing simulations by five Precipitation Driver Response Model Intercomparison Project (PDRMIP) models. Smith et al. (2018b) considered more models participating in PDRMIP and suggested that about half the IRFari was offset by adjustments for BC, a finding generally supported by single-model studies (Takemura and Suzuki, 2019; Zhao and Suzuki, 2019). Thornhill et al. (2021b) also reported a negative adjustment for BC based on AerChemMIP (Collins et al., 2017) but found it to be somewhat smaller in magnitude than those reported in Smith et al. (2018b) and Stjern et al. (2017). In contrast, Allen et al. (2019) found a positive adjustment for BC and suggested that most models simulate negative adjustment for BC because of a misrepresentation of aerosol atmospheric heating profiles.

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Zelinka et al. (2014) used the approximate partial radiation perturbation technique to quantify the ERFari in 2000 relative to 1860 in nine CMIP5 models; they estimated the ERFari (accounting for a small contribution from longwave radiation) to be –0.27 ± 0.35 W m–2. However, it should be noted that in Zelinka et al. (2014) adjustments of clouds caused by absorbing aerosols through changes in the thermal structure of the atmosphere (termed the semidirect effect of aerosols in AR5) are not included in ERFari but in ERFaci. The corresponding estimate emerging from the Radiative Forcing Model Intercomparison Project (RFMIP, Pincus et al., 2016) is –0.25 ± 0.40 W m–2(Smith et al., 2020b), which is generally supported by single-model studies published since AR5 (Zhang et al., 2016; Fiedler et al., 2017; Nazarenko et al., 2017; Zhou et al., 2017c, 2018b; Grandey et al., 2018). A 5% inflation is applied to the CMIP5 and CMIP6 fixed-SST derived estimates of ERFari from Zelinka et al. (2014) and Smith et al. (2020b) to account for land surface cooling (Table 7.6). Based on the above, ERFari from model-based evidence is assessed to be –0.25 ± 0.25 W m–2.

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Further, studies assessed in AR5 mostly investigated linear relationships between cloud droplet concentration and aerosol (Boucher et al., 2013). Since in most cases the relationships are not linear, this leads to a bias (Gryspeerdt et al., 2016). Several studies did not relate cloud droplet concentration, but cloud droplet effective radius, to the aerosol (Brenguier et al., 2000). This is problematic because in order to infer IRFaci, stratification by cloud LWP is required (McComiskey and Feingold, 2012). Where LWP positively co-varies with aerosol retrievals (which is often the case), IRFaci inferred from such relationships is biased towards low values. Also, it is increasingly evident that different cloud regimes show different sensitivities to aerosols (Stevens and Feingold, 2009). Averaging statistics over regimes thus biases the inferred IRFaci (Gryspeerdt et al., 2014b). The AR5 concluded that IRFaci estimates tied to satellite studies generally show weak IRFaci (Boucher et al., 2013), but when correcting for the biases discussed above, this is no longer the case.

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Multiple studies have found a positive relationship between cloud fraction and/or cloud LWP and aerosols (e.g., Nakajimaet al., 2001; Kaufman and Koren, 2006; Quaas et al., 2009). Since AR5, however, it has been documented that factors independent of causal aerosol–cloud interactions heavily influence such statistical relationships. These include the swelling of aerosols in the high relative humidity in the vicinity of clouds (Grandey et al., 2013) and the contamination of aerosol retrievals next to clouds by cloud remnants and cloud-side scattering (Várnai and Marshak, 2015; Christensen et al., 2017). Stratifying relationships by possible influencing factors such as relative humidity (Koren et al., 2010) does not yield satisfying results since observations of the relevant quantities are not available at the resolution and quality required. Another approach to tackle this problem was to assess the relationship of cloud fraction with droplet concentration (Gryspeerdt et al., 2016; Michibata et al., 2016; Sato et al., 2018). The relationship between satellite-retrieved cloud fraction and Nd was found to be positive (Christensen et al., 2016a, 2017; Gryspeerdt et al., 2016), implying an overall adjustment that leads to a more negative ERFaci. However, since retrieved Nd is biased low for broken clouds this result has been called into question (Grosvenor et al., 2018). Zhu et al. (2018) proposed to circumvent this problem by considering Nd of only continuous thick cloud covers, on the basis of which Rosenfeld et al. (2019) still obtained a positive relationship between cloud fraction and Nd relationship.

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Only a handful of studies have estimated the LWP and Cf adjustments that are needed for satellite-based estimates of ERFaci. Chen et al. (2014) and Christensen et al. (2017) used the relationship between cloud fraction and AI to infer the cloud fraction adjustment. Gryspeerdt et al. (2017) used a similar approach but tried to account for non-causal coorelations between aerosols and cloud fraction by using Ndas a mediating factor. These three studies together suggest a global Cf adjustment that augments ERFaci relative to IRFaci by –0.5 ± 0.4 W m–2(medium confidence). For global estimates of the LWP adjustment, evidence is even scarcer. Gryspeerdt et al. (2019) derived an estimate of the LWP adjustment using a method similar to Gryspeerdt et al. (2016). They estimated that the LWP adjustment offsets 0–60% of the (negative) IRFaci (0.0 to +0.3 W m–2). Supporting an offsetting LWP adjustment, Toll et al. (2019) estimated a moderate LWP adjustment of 29% (+0.15 W m–2). The adjustment due to LWP is assessed to be small, with a central estimate and very likely range of 0.2 ± 0.2 W m–2, but with low confidence due to the limited number of studies available.

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As in AR5, the representation of aerosol–cloud interactions in ESMs remains a challenge, due to the limited representation of important sub-gridscale processes, from the emissions of aerosols and their precursors to precipitation formation. ESMs that simulate ERFaci typically include aerosol–cloud interactions in liquid stratiform clouds only, while very few include aerosol interactions with mixed-phase, convective and ice clouds. Adding to the spread in model-derived estimates of ERFaci is the fact that model configurations and assumptions vary across studies, for example when it comes to the treatment of oxidants, which influence aerosol formation, and their changes through time (Karset et al., 2018).

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As most modelling and observational estimates of aerosol ERF have end points in 2014 or earlier, there is limited evidence available for the assessment of how aerosol ERF has changed from 2014 to 2019. However, based on a general reduction in global mean AOD over this period (Section 2.2.6 and Figure 2.9), combined with a reduction in emissions of aerosols and their precursors in updated emissions inventories (Hoesly et al., 2018), the aerosol ERF is assessed to have decreased in magnitude from about 2014 to 2019 (medium confidence). Consistent with Figure 2.10, the change in aerosol ERF from about 2014 to 2019 is assessed to be +0.2 W m–2, but with low confidence due tolimited evidence. Aerosols are therefore assessed to have contributed an ERF of –1.1 [–1.7 to –0.4] W m–2 over 1750–2019 (medium confidence).

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Land-use forcing is defined as those changes in land-surface properties directly caused by human activity rather than by climate processes (see also Section 2.2.7). Land-use change affects the surface albedo. For example, deforestation typically replaces darker forested areas with brighter cropland, and thus imposes a negative radiative forcing on climate, while afforestation and reforestation can have the opposite effect. Precise changes depend on the nature of the forest, crops and underlying soil. Land-use change also affects the amount of water transpired by vegetation (Devaraju et al., 2015). Irrigation of land directly affects evaporation (Sherwood et al., 2018), causing a global increase of 32,500 m3s−1due to human activity. Changes in evaporation and transpiration affect the latent heat budget, but do not directly affect the top-of-atmosphere (TOA) radiative fluxes. The lifetime of water vapour is so short that the effect of changes in evaporation on the greenhouse contribution of water vapour are negligible (Sherwood et al., 2018). However, evaporation can affect the ERF through adjustments, particularly through changes in low-cloud amounts. Land management affects the emissions or removal of GHGs from the atmosphere (such as CO2, CH4, N2O). These emissions changes have the greatest effect on climate (Ward et al., 2014), however they are already included in GHG inventories. Land-use change also affects the emissions of dust and biogenic volatile organic compounds (BVOCs), which form aerosols and affect the atmospheric concentrations of ozone and methane (Section 6.2.2). The effects of land use on surface temperature and hydrology were recently assessed in SRCCL (Jia et al., 2019).

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The indirect contributions of land-use change through biogenic emissions is very uncertain. Decreases in BVOCs reduce ozone and methane (Unger, 2014), but also reduce the formation of organic aerosols and their effects on clouds (Scott et al., 2017). Adjustments through changes in aerosols and chemistry are model dependent (Zhu et al., 2019b; Zhu and Penner, 2020), and it is not yet possible to make an assessment based on a limited number of studies.

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Non-explosive volcanic eruptions generally yield negligible global ERFs due to the short atmospheric lifetimes (a few weeks) of volcanic aerosols in the troposphere. However, as discussed in (Section 7.3.3.2, the massive fissure eruption in Holuhraun, Iceland persisted for months in 2014 and 2015 and did in fact result in a marked and persistent reduction in cloud droplet radii and a corresponding increase in cloud albedo regionally (Malavelle et al., 2017). This shows that non-explosive fissure eruptions can lead to strong regional and even global ERFs, but because the Holuhraun eruption occurred in Northern Hemisphere winter, solar insolation was weak and the observed albedo changes therefore did not result in an appreciable global ERF (Gettelman et al., 2015).

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The ERF for volcanic stratospheric aerosols is assessed to be –20 ± 5 W m–2 per unit SAOD (medium confidence) based on the CMIP5 multi-model mean from the Larson and Portmann (2016) SAOD forcing efficiency calculations combined with the single-model results of Gregory et al. (2016), Schmidt et al. (2018) and Marshall et al. (2020). This is applied to the SAOD time series from (Chapter 2 Section 2.2.2) to generate a time series of ERF and temperature response shown in (Chapter 2 (Figure 2.2 and Figure 7.8, respectively). The period from 500 BCE to 1749 CE, spanning back to the start of the record of Toohey and Sigl (2017), is defined as the pre-industrial baseline and the volcanic ERF is calculated using an SAOD anomaly from this long-term mean. As in AR5, a pre-industrial to present-day ERF assessment is not provided due to the episodic nature of volcanic eruptions.

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For aerosols there has been a convergence of model and observational estimates of aerosol forcing, and the partitioning of the total aerosol ERF has changed. Compared to AR5 a greater fraction of the ERF is assessed to come from ERFaci compared to the ERFari. It is now assessed as virtually certain that the total aerosol ERF (ERFari+aci) is negative.

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As discussed in (Section 7.3.3, aerosols have in total contributed an ERF of –1.1 [–1.7 to –0.4] W m–2 over 1750–2019 (medium confidence). Aerosol–cloud interactions contribute approximately 75–80% of this ERF with the remainder due to aerosol–radiation interactions (Table 7.8).

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Figure 7.8 presents the GSAT time series using ERF time series for individual forcing agents rather than their aggregation. It shows that for most of the historical period the long time scale total GSAT trend estimate from the emulator closely follows the CO2 contribution. The GSAT estimate from non-CO2 greenhouse gas forcing (from other WMGHGs and ozone) has been approximately cancelled out in the global average by a cooling GSAT trend from aerosols. However, since 1980 the aerosol cooling trend has stabilized and may have started to reverse, so that over the last few decades the long-term warming has been occurring at a faster rate than would be expected due to CO2 alone (high confidence) (see also Sections 2.2.6 and 2.2.8). Throughout the record, but especially prior to 1930, periods of volcanic cooling dominate decadal variability. These estimates of the forced response are compared with model simulations and attributable warming estimates in (Chapter 3 Section 3.3.1).

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The magnitude of global surface temperature change primarily depends on the strength of the radiative forcings and feedbacks, the latter defined as the changes of the net energy budget at the top-of-atmosphere (TOA) in response to a change in the GSAT (Box 7.1, Equation 7.1). Feedbacks in the Earth system are numerous, and it can be helpful to categorize them into three groups: (i) physical feedbacks; (ii) biogeophysical and biogeochemical feedbacks; and (iii) long-term feedbacks associated with ice sheets. The physical feedbacks (e.g., those associated with changes in lapse rate, water vapour, surface albedo, or clouds; (Sections 7.4.2.1–7.4.2.4) and biogeophysical/biogeochemical feedbacks (e.g., those associated with changes in methane, aerosols, ozone, or vegetation; Section 7.4.2.5) act both on time scales that are used to estimate the equilibrium climate sensitivity (ECS) in models (typically 150 years, see Box 7.1) and on longer time scales required to reach equilibrium. Long-term feedbacks associated with ice sheets (Section 7.4.2.6) are relevant primarily after several centuries or more. The feedbacks associated with biogeophysical/biogeochemical processes and ice sheets, often collectively referred to as Earth system feedbacks, had not been included in conventional estimates of the climate feedback (e.g., Hansen et al., 1984), but the former can now be quantified and included in the assessment of the total (net) climate feedback. Feedback analysis represents a formal framework for the quantification of the coupled interactions occurring within a complex Earth system in which everything influences everything else (e.g., Roe, 2009). As used here (as presented in Section 7.4.1), the primary objective of feedback analysis is to identify and understand the key processes that determine the magnitude of the surface temperature response to an external forcing. For each feedback, the basic underlying mechanisms and their assessments are presented in Section 7.4.2.

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Following the conventional definition, the physical climate feedbacks are here decomposed into terms associated with a vertically uniform temperature change (Planck response, P), changes in the water-vapour plus temperature lapse-rate (WV+LR), surface albedo (A) and clouds (C). The water-vapour plus temperature lapse rate feedback is further decomposed using two different approaches, one based on changes in specific humidity, the other on changes in relative humidity. Biogeochemical feedbacks arise due to changes in aerosols and atmospheric chemical composition in response to changes in surface temperature, and Gregory et al. (2009) and Raes et al. (2010) show that they can be analysed using the same framework as for the physical climate feedbacks (Sections 5.4 and 6.4.5). Similarly, feedbacks associated with biogeophysical and ice-sheet changes can also be incorporated.

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Clouds can be formed almost anywhere in the atmosphere when moist air parcels rise and cool, enabling the water vapour to condense. Clouds consist of liquid water droplets and/or ice crystals, and these droplets and crystals can grow into larger particles of rain, snow or drizzle. These microphysical processes interact with aerosols, radiation and atmospheric circulation, resulting in a highly complex set of processes governing cloud formation and life cycles that operate across a wide range of spatial and temporal scales.

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The magnitude of polar amplification also depends on the type of radiative forcing applied (Section 4.5.1.1; Stjern et al., 2019), with (Chapter 6 (Section 6.4.3) discussing changes in sulphate aerosol emissions and the deposition of black carbon aerosols on ice and snow as potential drivers of amplified Arctic warming. The timing of the emergence of SH polar amplification remains uncertain due to insufficient knowledge of the time scales associated with Southern Ocean warming and the response to surface wind and freshwater forcing (Bintanja et al., 2013; Kostov et al., 2017, 2018; Pauling et al., 2017; Purich et al., 2018). ESM simulations indicate that freshwater input from melting ice shelves could reduce Southern Ocean warming by up to several tenths of a °C over the 21st century by increasing stratification of the surface ocean around Antarctica (low confidence due to medium agreement butlimited evidence) (Sections 7.4.2.6 and 9.2.1, and Box 9.3; Bronselaer et al., 2018; Golledge et al., 2019; Lago and England, 2019). However, even a large reduction in the Atlantic Meridional Overturning Circulation (AMOC) and associated northward heat transport due, for instance, to greatly increased freshwater runoff from Greenland would be insufficient to eliminate Arctic amplification (medium confidence based onmedium agreement and medium evidence) (Liu et al., 2017; Y. Liu et al., 2018; Wen et al., 2018).

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Since AR5, there has been progress in the simulation of polar amplification by paleoclimate models of the Early Eocene. Initial work indicated that changes to model parameters associated with aerosols and/or clouds could increase simulated polar amplification and improve agreement between models and paleoclimate data (Kiehl and Shields, 2013; Sagoo et al., 2013), but such parameter changes were not physically based. In support of these initial findings, a more recent (CMIP5) climate model, that includes a process-based representation of cloud microphysics, exhibits polar amplification in better agreement with proxies when compared to the models assessed in AR5 (Zhu et al., 2019a). Since then, some other CMIP3 and CMIP5 models in the DeepMIP multi-model ensemble (Lunt et al., 2021) have obtained polar amplification for the EECO that is consistent with proxy indications of both polar amplification and CO2. Although there is a lack of tropical proxy SAT estimates, both proxies and DeepMIP models show greater terrestrial warming in the high latitudes than the mid-latitudes in both hemispheres (Figure 7.13a,d). SST proxies also exhibit polar amplification in both hemispheres, but the magnitude of this polar amplification is too low in the models, in particular in the south-west Pacific (Figure 7.13g,j).

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The LGM (Cross-Chapter Box 2.1) has been used to provide estimates of ECS (see Table 7.11 for estimates since AR5; Sherwood et al., 2020; Tierney et al., 2020b). The major forcings and feedback processes that led to the cold climate at that time (e.g., CO2 , non-CO2 greenhouse gases, and ice sheets) are relatively well-known (Section 5.1), orbital forcing relative to pre-industrial was negligible, and there are relatively high spatial resolution and well-dated paleoclimate temperature data available for this time period (Section 2.3.1). Uncertainties in deriving global surface temperature from the LGM proxy data arise partly from uncertainties in the calibration from the paleoclimate data to local annual mean surface temperature, and partly from uncertainties in the conversion of the local temperatures to an annual mean global surface temperature. Overall, the global mean LGM cooling relative to pre-industrial is assessed to be very likely from 5 to 7 °c (Section 2.3.1). The LGM climate is often assumed to be in full equilibrium with the forcing, such that ΔN in Equation 7.1, Box 7.1, is zero. A calculation of sensitivity using solely CO2 forcing, and assuming that the LGM ice sheets were in equilibrium with that forcing, would give an Earth System Sensitivity (ESS) rather than an ECS (see Box 7.1). In order to calculate an ECS, which is defined here to include all feedback processes except ice sheets, the approach of Rohling et al. (2012) can be used. This approach introduces an additional forcing term in Equation 7.1, Box 7.1, that quantifies the resulting forcing associated with the ice-sheet feedback (primarily an estimate of the radiative forcing associated with the change in surface albedo). However, differences between studies as to which processes are considered as forcings (for example, some studies also include vegetation and/or aerosols, such as dust, as forcings), means that published estimates are not always directly comparable. Additional uncertainty arises from the magnitude of the ice-sheet forcing itself (Stap et al., 2019; Zhu and Poulsen, 2021), which is often estimated using ESMs. Furthermore, the ECS at the LGM may differ from that of today due to state-dependence (Section 7.4.3). Here, only studies that report values of ECS that have accounted for the long-term feedbacks associated with ice sheets, and therefore most closely estimate ECS as defined in this chapter, are assessed here (Table 7.11).

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The effect of a compound on climate is not limited to its direct radiative forcing. Compounds can perturb the carbon cycle affecting atmospheric CO2 concentrations. Chemical reactions from emitted compounds can produce or destroy other GHGs or aerosols.

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Emissions of non-CO2 species can affect the carbon cycle in other ways: emissions of ozone precursors can reduce the carbon uptake by plants (W.J. Collins et al., 2013); emissions of reactive nitrogen species can fertilize plants and hence increase the carbon uptake (Zaehle et al., 2015); and emissions of aerosols or their precursors can affect the utilisation of light by plants (Cohan et al., 2002; Mercado et al., 2009; Mahowald et al., 2017; see Section 6.4.4 for further discussion). There is robust evidence that these processes occur and are important, butinsufficient evidence to determine the magnitude of their contributions to emissions metrics. Ideally, emissions metrics should include all indirect effects to be consistent, but limits to our knowledge restrict how much can be included in practice.

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Ebmeier, S.K., A.M. Sayer, R.G. Grainger, T.A. Mather, and E. Carboni, 2014: Systematic satellite observations of the impact of aerosols from passive volcanic degassing on local cloud properties. Atmospheric Chemistry and Physics, 14(19), 10601–10618, doi: 10.5194/acp-14-10601-2014.

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Krishnamohan, K.S., G. Bala, L. Cao, L. Duan, and K. Caldeira, 2019: Climate System Response to Stratospheric Sulfate Aerosols: Sensitivity to Altitude of Aerosol Layer. Earth System Dynamics, 10(4), 885–900, doi: 10.5194/esd-10-885-2019.

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The OHC patterns projected by CMIP6 models (Figures 9.6 and 9.7) are similar to the CMIP5 projections assessed in SROCC (Bindoff et al., 2019): faster warming in all water mass subduction regions (e.g., subtropical cells and mode waters); deeper penetration in the centre of subtropical gyres; slower northern North Atlantic warming due to slowing AMOC; and slower subpolar Southern Ocean warming due upwelled pre-industrial water masses. Decreased aerosol forcing will allow Northern Hemisphere ocean warming to be faster and less dominated by Southern Hemisphere change (Shi et al., 2018; Irving et al., 2019). Since SROCC, distinguishing between added and redistributed heat has aided in understanding projections (Bronselaer and Zanna, 2020; Dias et al., 2020; Couldrey et al., 2021). The near-term decades will feature patterns strongly influenced by heat redistribution and internal variability (Rathore et al., 2020). Strengthening Southern Hemisphere westerlies are projected, except for stringent mitigation scenarios (Bracegirdle et al., 2020), and will cause a northward and downward OHT. There is low agreement in future Southern Ocean warming across model results due to uncertainties in the magnitude of westerly wind changes (Figure 9.4; Liu et al., 2018; He et al., 2019; Dias et al., 2020; Lyu et al., 2020b) and the degree of eddy compensation of overturning across different parametrizations and resolutions (Section 9.2.3.2; Beal and Elipot, 2016; Mak et al., 2017; Roberts et al., 2020). By 2100, however, the OHC change will be dominated by the added heat response, particularly for strong warming scenarios (Garuba and Klinger, 2018; Bronselaer and Zanna, 2020) with added heat following unperturbed water mass pathways in the North Atlantic and Southern Ocean (Figure 9.8; Dias et al., 2020; Couldrey et al., 2021). There is high confidence that projected weakening of the AMOC (Section 9.2.3.1) will cause a decrease in northward OHT in the Northern Hemisphere mid-latitudes (Figure 9.8 and Sections 9.2.3.1 and 4.3.2.3; Weijer et al., 2020) associated with a dipole pattern of Atlantic OHC redistributed from northern to low latitudes that may override added heating in the northern North Atlantic (Figures 9.6, 9.7 and 9.8). Variations in the degree of AMOC redistributed heat (Menary and Wood, 2018) causes large intermodel spread in SST (Figure 9.3) and OHC change (Figure 9.6; Kostov et al., 2014; Bronselaer and Zanna, 2020; Todd et al., 2020; Couldrey et al., 2021). In the 700–2000 m depth range, CMIP5 and CMIP6 models project the largest warming to be in the North Atlantic Deep Water and Antarctic Intermediate Water (Figure 9.7) while below 2000 m, the North Atlantic cools in many models, and Antarctic Bottom Waters warm (Sallée et al., 2013b; Heuzé et al., 2015). In summary, on decadal time scales, redistribution will dominate regional patterns of OHC change without affecting the globally integrated OHC; however, by 2100, particularly under strong warming scenarios, there is high confidence that regional patterns of OHC change will be dominated by added heat entering the sea surface, primarily in water mass formation regions in the subtropics; and reduced aerosols will increase the relative rate of Northern Hemisphere heat uptake (medium confidence).

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Radiation at surface: Radiation has undergone decadal variations in past observations, which are mostly responding to the so-called dimming and brightening phenomenon driven by the increase and decrease of aerosols. Over the last two decades or so, brightening continues in Europe and North America and dimming stabilizes over South and East Asia and increases in some other areas (Section 7.2.2.3). Future regional shortwave radiation projections depend mostly on cloud trends, aerosol and water vapour trends, and stratospheric ozone when considering UV radiation. Over Africa in 2050 and beyond, there is medium confidence that radiation will increase in North and South Africa and decrease over the Sahara, North Eastern Africa and Western Africa (Wild et al., 2015, 2017; Soares et al., 2019; C. Tang et al., 2019; Sawadogo et al., 2020, 2021). Over Asia, the CMIP5 multi-model mean response shows that solar radiation will decrease in South Asia and increase in East Asia (medium confidence) by the mid-century RCP8.5 (Wild et al., 2015, 2017; Ruosteenoja et al., 2019b). Projected solar resources show an increasing trend throughout the 21st century in East Asia under RCP2.6 and RCP8.5 scenarios in CMIP5 simulations (medium confidence) (Wild et al., 2015; F. Zhang et al., 2018; Shiogama et al., 2020). More sunshine is projected over Australia in winter and spring by the end of the century (medium confidence) with the increases in Southern Australia exceeding 10% (CSIRO and BOM, 2015; Wild et al., 2015). In Central and South America, there is medium confidence of increasing solar radiation over the Amazon basin and the northern part of South America (medium confidence) (Wild et al., 2015, 2017; de Jong et al., 2019). There is low confidence for an increase in surface radiation in central Europe, owing in particular to disagreement in cloud cover across global and regional models (Jerez et al., 2015; Bartók et al., 2017; Craig et al., 2018), as well as water vapour. The treatment of aerosol appears to be key in explaining these differences (Boé, 2016; Undorf et al., 2018; Boé et al., 2020; Gutiérrez et al., 2020). Regional and global studies, however, indicate that there is medium confidence in increasing radiation over southern Europe and decreasing radiation over Northern Europe. Increasing radiation trends are also found over southern and eastern USA, and decreasing trends over North-Western North America (Wild et al., 2015; Losada Carreño et al., 2020), despite large differences between responses from regional climate models (RCMs) and general circulation models (GCMs) over southern and eastern USA (low confidence), where, as for Central Europe, the role of aerosols appears important (Chen, 2021). Over polar regions there is medium confidence of a decrease in radiation due to increasing moisture in the atmosphere and clouds (Wild et al., 2015).

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Mean wind speed: Mean surface wind speeds have decreased in Europe as in many other areas of the Northern Hemisphere over the past four decades (medium confidence) (AR5 WGI), with a reversal to an increasing trend in the last decade (low confidence) that is, however, not fully consistent across studies (Tian et al., 2019; Zeng et al., 2019; Z. Zhang et al., 2019; Deng et al., 2021; see Section 2.3.1.4.4). Re-analyses also show declining winds in Europe (Deng et al., 2021) with large interdecadal variability (Laurila et al., 2021). The declining trend has induced a corresponding decline in wind power potential indices across Europe (low confidence) (Tian et al., 2019). However, there is low agreement and limited evidence that climate model historical trends are consistent with observed trends (Tian et al., 2019; Deng et al., 2021). Several factors have been attributed to these trends, including forest growth, urbanization, local changes in wind measurement exposure and aerosols (Bichet et al., 2012), as well as natural variability (Zeng et al., 2019).

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Agier, L. et al., 2013: Seasonality of meningitis in Africa and climate forcing: aerosols stand out. Journal of The Royal Society Interface, 10(79), 20120814, doi: 10.1098/rsif.2012.0814.

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Brahney, J., A.P. Ballantyne, C. Sievers, and J.C. Neff, 2013: Increasing Ca2+deposition in the western US: The role of mineral aerosols. Aeolian Research, 10, 77–87, doi: 10.1016/j.aeolia.2013.04.003.

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Gutiérrez, C. et al., 2020: Future evolution of surface solar radiation and photovoltaic potential in Europe: investigating the role of aerosols. Environmental Research Letters, 15(3), 34035, doi: 10.1088/1748-9326/ab6666.

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Ji, Z., G. Wang, M. Yu, and J.S. Pal, 2018: Potential climate effect of mineral aerosols over West Africa: Part II – contribution of dust and land cover to future climate change. Climate Dynamics, 50(7–8), 2335–2353, doi: 10.1007/s00382-015-2792-x.

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Undorf, S., M.A. Bollasina, B.B.B. Booth, and G.C. Hegerl, 2018: Contrasting the Effects of the 1850–1975 Increase in Sulphate Aerosols from North America and Europe on the Atlantic in the CESM. Geophysical Research Letters, 45(21), 11930–11940, doi: 10.1029/2018gl079970.

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Global surface temperature was around 1.1°C above 1850–1900 in 2011–2020 (1.09 [0.95 to 1.20]°C) 64, with larger increases over land (1.59 [1.34 to 1.83]°C) than over the ocean (0.88 [0.68 to 1.01]°C) 65. Observed warming is human- caused, with warming from greenhouse gases (GHG), dominated by CO2 and methane (CH4), partly masked by aerosol cooling (Figure 2.1). Global surface temperature in the first two decades of the 21st century (2001–2020) was 0.99 [0.84 to 1.10]°C higher than 1850–1900. Global surface temperature has increased faster since 1970 than in any other 50-year period over at least the last 2000 years (high confidence). The likely range of total human-caused global surface temperature increase from 1850–1900 to 2010–201966 is 0.8°C to 1.3°C, with a best estimate of 1.07°C. It is likely that well-mixed GHGs 67 contributed a warming of 1.0°C to 2.0°C, and other human drivers (principally aerosols) contributed a cooling of 0.0°C to 0.8°C, natural (solar and volcanic) drivers changed global surface temperature by ±0.1°C and internal variability changed it by ±0.2°C. {WGI SPM A.1, WGI SPM A.1.2, WGI SPM A.1.3, WGI SPM A.2.2, WGI Figure SPM.2; SRCCL TS.2}

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Xie, H., J. Zhao, K. Wang and H. Peng, 2021: Long-term variations in solar radiation, diffuse radiation, and diffuse radiation fraction caused by aerosols in China during 1961–2016. PLOS ONE, 16 (5), e0250376, doi:10.1371/journal.pone.0250376.

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The water cycle is affected by both climatic and non-climatic factors (Douville et al., 2021). Radiative forcing by changes in greenhouse gas (GHG) concentrations, aerosols and surface albedo drives global and regional changes in evaporation and precipitation (Douville et al., 2021). A warmer atmosphere holds more moisture, increasing global and regional mean precipitation, and more extreme precipitation (Allan et al., 2014; Giorgi et al., 2019; Allan et al., 2020). Regional precipitation responses vary according to changes in atmospheric circulation. Geographical variation in aerosols drives changes in atmospheric circulation, affecting precipitation patterns such as the Asian monsoon (Ganguly et al., 2012; Singh et al., 2019). (Section 4.2.1)

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In summary, radiative forcing by GHG and aerosols drives changes in ET and precipitation at global and regional scales, and the associated warming shifts the balance between frozen and liquid water (high confidence). Rising CO2 concentrations also affect the water cycle via plant physiological responses affecting transpiration, including via reduced stomatal opening and increased leaf area (high confidence regarding the individual processes; medium confidence regarding their net impact). Land cover changes and urbanisation affect both the climate and land hydrology by altering the exchanges of energy and moisture between the atmosphere and surface (high confidence) and changing the permeability of the land surface. Direct human interventions in river systems and groundwater systems are non-climatic drivers with substantial impacts on the water cycle (high confidence) and have the potential to change as part of societal responses to climate change (Figure 4.2).

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There are three main sources of uncertainty in climate projections: emission scenarios, regional climate responses and internal climate variability (CSIRO and BOM, 2015). Emission scenario uncertainty is captured in Representative Concentration Pathways (RCPs) for greenhouse gases and aerosols. RCP2.6 represents low emissions, RCP4.5 medium emissions and RCP8.5 high emissions. Regional climate response uncertainty and internal climate variability uncertainty are captured in climate model simulations driven by the RCPs.

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Effiong, U. and R.L. Neitzel, 2016: Assessing the direct occupational and public health impacts of solar radiation management with stratospheric aerosols. Environ Health, 15 (1), 1–9.

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Krishna-Pillai, S.-P.K., et al., 2019: Climate system response to stratospheric sulfate aerosols: Sensitivity to altitude of aerosol layer. Earth Syst. Dyn. , 10 (4), 885–900.

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Wang, H., et al., 2020: Aerosols in the E3SM version 1: new developments and their impacts on radiative forcing. J. Adv. Model. Earth Syst. , 12 (1), e2019MS001851.

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Agier, L. et al., 2013: Seasonality of meningitis in Africa and climate forcing: aerosols stand out. Journal of the Royal Society Interface, 10 (79), 20120814, doi: https://doi.org/10.1098/rsif.2012.0814.

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Booth, B. B. B. et al., 2012: Aerosols implicated as a prime driver of twentieth-century North Atlantic climate variability. Nature, 484 (7393), 228–232, doi:10.1038/nature10946.

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García-Pando, C. P. et al., 2014: Soil dust aerosols and wind as predictors of seasonal meningitis incidence in Niger. Environmental health perspectives, 122 (7), 679–686, doi: https://doi.org/10.1289/ehp.1306640.

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Giannini, A. and A. Kaplan, 2019: The role of aerosols and greenhouse gases in Sahel drought and recovery. Climatic Change, 152 (3), 449–466, doi:10.1007/s10584-018-2341-9.

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There is low evidence and limited agreement on projections of solar power potential due to differences in the integration of aerosols and the estimated cloud cover between climate models (Bartok et al., 2017; Boé et al., 2020; Gutiérrez et al., 2020). Studies on climate risks for bioenergy are also limited.

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Gutiérrez, C., et al., 2020: Future evolution of surface solar radiation and photovoltaic potential in Europe: investigating the role of aerosols. Environ. Res. Lett. , 15, 34035, doi:10.1088/1748-9326/ab6666.

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The most appropriate metric to aggregate GHG emissions depends on the objective (Cross-Chapter Box 2). One such objective can be to understand the contribution of emissions in any given year to warming, while another can be to understand the contribution of cumulative emissions over an extended time period to warming. In Figure 2.4 the modelled warming from emissions of each gas or group of gases is also shown – calculated using the reduced-complexity climate model Finite Amplitude Impulse Response (FaIR) model v1.6, which has been calibrated to match several aspects of the overall WGI assessment (Forster et al. 2021a; specifically Cross-Chapter Box 7 in Chapter 10 therein). Additionally, its temperature response to emissions with shorter atmospheric lifetimes such as aerosols, methane or ozone has been adjusted to broadly match those presented in Szopa et al. (2021a). There are some differences in actual warming compared to the GWP100 weighted emissions of each gas (Figure 2.4), in particular a greater contribution from CH4 emissions to historical warming. This is consistent with warming from CH4 being short-lived and hence having a more pronounced effect in the near-term during a period of rising emissions. Nonetheless, Figure 2.4 highlights that emissions weighted by GWP100 do not provide a fundamentally different information about the contribution of individual gases than modelled actual warming over the historical period, when emissions of most GHGs have been rising continuously, with CO2 being the dominant and CH4 being the second most important contributor to GHG-induced warming. Other metrics such as GWP* (or GWP star) (Cain et al. 2019) offer an even closer resemblance between cumulative CO2-eq emissions and temperature change. Such a metric may be more appropriate when the key objective is to track temperature change when emissions are falling, as in mitigation scenarios.

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There are other emissions with shorter atmospheric lifetimes that contribute to climate changes. Some of them (aerosols, sulphur emissions or organic carbon) reduce forcing, while others – such as black carbon, carbon monoxide or non-methane volatile organic compounds (NMVOC) – contribute to warming (Figure 2.4) as assessed in WGI (Forster et al. 2021c; Szopa et al. 2021a). Many of these other SLCFs are co-emitted during combustion processes in power plants, cars, trucks, airplanes, but also during wildfires and household activities such as traditional cooking with open biomass burning. As these co-emissions have implications for net warming, they are also considered in long-term emission reduction scenarios as covered in the literature (Harmsen et al. 2020; Rauner et al. 2020b; Smith et al. 2020; Vandyck et al. 2020) as well as Chapter 3 of this report. These air pollutants are also detrimental to human health (e.g., Lelieveld et al. 2015, 2018; Vohra et al. 2021). For example, Lelieveld et al. (2015) estimate a total of 3.3 (1.6–4.8) million premature deaths in 2010 from outdoor air pollution. Reducing air pollutants in the context of climate policies therefore leads to substantial co-benefits of mitigation efforts (Von Stechow et al. 2015; Rao et al. 2017; Lelieveld et al. 2019; Rauner et al. 2020a). Here we briefly outline the major trends in emissions of SLCFs.

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Dang, R. and H. Liao, 2019: Radiative Forcing and Health Impact of Aerosols and Ozone in China as the Consequence of Clean Air Actions over 2012–2017. Geophys. Res. Lett. , 46(21) , 12511–12519, doi:10.1029/2019GL084605.

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Rapid reductions in non-CO2GHGs, particularly methane, would lower the level of peak warming (high confidence). Residual non-CO2 emissions at the time of reaching net zero CO2 range between 5 and 11 GtCO2-eq yr –1 in pathways limiting warming to 2°C (>67%) or lower. Methane (CH4) is reduced by around 19% (4–46%) in 2030 and 45% (29–64%) in 2050, relative to 2019. Methane emission reductions in pathways limiting warming to 1.5°C (>50%) with no or limited overshoot are substantially higher by 2030, 34% (21–57%), but only moderately so by 2050, 51% (35–70%). Methane emissions reductions are thus attainable at relatively lower GHG prices but are at the same time limited in scope in most 1.5°C–2°C pathways. Deeper methane emissions reductions by 2050 could further constrain the peak warming. N2O emissions are reduced too, but similar to CH4, emission reductions saturate for more stringent climate goals. In the mitigation pathways, the emissions of cooling aerosols are reduced due to reduced use of fossil fuels. The overall impact on non-CO2-related warming combines these factors. {3.3}

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For the scenarios in the C1 category (limit warming to 1.5°C (>50% with no or limited overshoot, the net zero year for CO2 emissions is typically around 2035–2070. For scenarios in C3 (limiting warming to 2°C (>67%)), CO2 emissions reach net zero around after 2050. Similarly, also the years for net zero GHG emissions can be calculated (see Fig 3.14b. The GHG net zero emissions year is typically around 10–40 years later than the carbon neutrality. Residual non-CO2 emissions at the time of reaching net zero CO2 range between 5–11 GtCO2-eq in pathways that limit warming to 2°C (>67%) or lower. In pathways limiting warming to 2°C (>67%), methane is reduced by around 19% (3–46%) in 2030 and 46% (29–64%) in 2050, and in pathways limiting warming to 1.5°C (>50%) with no or limited overshoot by around 34% (21–57%) in 2030 and a similar 51% (35–70%) in 2050. Emissions-reduction potentials assumed in the pathways become largely exhausted when limiting warming to 2°C (>50%). N2O emissions are reduced too, but similar to CH4, emission reductions saturate for stringent climate goals. In the mitigation pathways, the emissions of cooling aerosols are reduced due to reduced use of fossil fuels. The overall impact on non-CO2-related warming combines these factors.

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Systematic reviews of the literature on co-benefits and trade-offs from mitigation actions have shown that only a small portion of articles provide economic quantifications (Deng et al. 2017; Karlsson et al. 2020). Most economic quantifications use monetary valuation approaches. Improved air quality, and associated health effects, are the co-benefit category dominating the literature (Markandya et al. 2018; Vandyck et al. 2018; Scovronick et al. 2019; Howard et al. 2020; Karlsson et al. 2020 b; Rauner et al. 2020a,b), but some studies cover other categories, including health effects from diet change (Springmann et al. 2016b) and biodiversity impacts (Rauner et al. 2020a). Regarding health effects from air quality improvement and from diet change, co-benefits are shown to be of the same order of magnitude as mitigation costs (Thompson et al. 2014; Springmann et al. 2016a,b; Markandya et al. 2018; Scovronick et al. 2019b; Howard et al. 2020; Rauner et al. 2020a,b; Liu et al. 2021; Yang et al. 2021). Co-benefits from improved air quality are concentrated sooner in time than economic benefits from avoided climate change impacts (Karlsson et al. 2020), such that when accounting both for positive health impacts from reduced air pollution and for the negative climate effect of reduced cooling aerosols, optimal GHG mitigation pathways exhibit immediate and continual net economic benefits (Scovronick et al. 2019a). However, AR6 WGI Chapter 6 (Section 6.7.3) shows a delay in air pollution reduction benefits when they come from climate change mitigation policies compared with air pollution reduction policies.

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The SDGs provide a lens on diverse national and local development objectives. Humankind currently faces multiple sustainability challenges that together present global society with the challenge of assessing, deliberating, and attempting to bring about a viable, positive future development pathway. Ecological sustainability challenges include reducing GHG emissions, protecting the ozone layer, controlling pollutants such as aerosols and persistent organics, managing nitrogen and phosphorous cycles, etc. (Steffen et al. 2015), which are necessary to address the rising risks to biodiversity and ecosystem services on which humanity depends (IPBES 2019a). Socio-economic sustainability challenges include conflict, persistent poverty and deprivation, various forms of pervasive and systemic discrimination and deprivation, and socially corrosive inequality. The global adoption of the SDGs and their underlying indicators (UN 2017, 2018 and 2019) reflect a negotiated prioritisation of these common challenges.

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The Paris Agreement primarily deals with national commitments relating to domestic emissions and removals, hence emissions from international aviation and shipping are not covered. Aviation and shipping accounted for approximately 2.7% of greenhouse gas emissions in 2019 (before COVID-19); see Section 10.5.2 for discussion. In addition to CO2 emissions, aircraft-produced contrail cirrus clouds, and emissions of black carbon and short-lived aerosols (e.g., sulphates) from shipping are especially harmful for the Arctic (Section 10.8 and Box 10.6).

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Myhre, G., C.E.L. Myhre, B.H. Samset, and T. Storelvmo, 2013: Aerosols and their Relation to Global Climate and Climate Sensitivity. Nat. Educ. Knowl. , 4(7) https://www.nature.com/scitable/knowledge/library/aerosols-and-their-relation-to-global-climate-102215345/ (Accessed June 18, 2021).

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Climate change is not expected to substantially impact global solar insolation and will not compromise the ability of solar energy to support low-carbon transitions ( high confidence). Models show dimming and brightening in certain regions, driven by cloud, aerosol and water vapour trends (Chapter 12 of IPCC AR6 WGI). The increase in surface temperature, which affects all regions, decreases solar power output by reducing the PV panel efficiency. In some models and climate scenarios, the increases in solar insolation are counterbalanced by reducing efficiency due to rising surface air temperatures, which increase significantly in all models and scenarios (Jerez et al. 2015; Bartók et al. 2017; Emodi et al. 2019). Increases in aerosols would reduce the solar resource available and add to maintenance costs (Chapter 12 of IPCC AR6 WGI).

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Wildland fires account for approximately 70% of the global biomass burned annually (van der Werf et al. 2017) and constitute a large global source of atmospheric trace gases and aerosols (Gunsch et al. 2018) (IPCC WGI AR6). Although fires are part of the natural system, the frequency of fires has increased in many areas, exacerbated by decreases in precipitation, including in many regions with humid and temperate forests that rarely experience large-scale fires naturally. Natural and human-ignited fires affect all major biomes, from peatlands through shrublands to tropical and boreal forests, altering ecosystem structure and functioning (Argañaraz et al. 2015; Nunes et al. 2016; Remy et al. 2017; Mancini et al. 2018; Aragão et al. 2018; Engel et al. 2019; Rodríguez Vásquez et al. 2021). However, the degree of incidence and regional trends are quite different and a study over 14 years indicated, on average, the largest fires in Australia, boreal North America and Northern Hemisphere Africa (Andela et al. 2019). More than half of the terrestrial surface of the Earth has fire regimes outside the range of natural variability, with changes in fire frequency and intensity posing major challenges for land restoration and recovery (Barger et al. 2018). In some ecosystems, fire prevention might lead to accumulation of large fuel loads that enable wildfires (Moreira et al. 2020a).

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Kalliokoski, T. et al., 2020: Mitigation Impact of Different Harvest Scenarios of Finnish Forests That Account for Albedo, Aerosols, and Trade-Offs of Carbon Sequestration and Avoided Emissions. Front. For. Glob. Change, 3, doi:10.3389/ffgc.2020.562044.

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i. direct emissions which are defined as all on-site fossil fuel or biomass-based combustion activities (i.e., use of biomass for cooking, or gas for heating and hot water) and F-gas emissions (i.e., use of heating and cooling systems, aerosols, fire extinguishers, soundproof);

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Effiong, U. and R.L. Neitzel, 2016: Assessing the direct occupational and public health impacts of solar radiation management with stratospheric aerosols. Environ. Heal. , 15(1) , 1–9, doi:10.1186/s12940-016-0089-0.

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Krishnamohan, K.P.S. P., G. Bala, L. Cao, L. Duan, and K. Caldeira, 2019: Climate system response to stratospheric sulfate aerosols: Sensitivity to altitude of aerosol layer. Earth Syst. Dyn. , 10(4) , 885–900, doi:10.5194/esd-10-885-2019.

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Global surface temperature was around 1.1°C above 1850–1900 in 2011–2020 (1.09 [0.95 to 1.20]°C) 64, with larger increases over land (1.59 [1.34 to 1.83]°C) than over the ocean (0.88 [0.68 to 1.01]°C) 65. Observed warming is human- caused, with warming from greenhouse gases (GHG), dominated by CO2 and methane (CH4), partly masked by aerosol cooling (Figure 2.1). Global surface temperature in the first two decades of the 21st century (2001–2020) was 0.99 [0.84 to 1.10]°C higher than 1850–1900. Global surface temperature has increased faster since 1970 than in any other 50-year period over at least the last 2000 years (high confidence). The likely range of total human-caused global surface temperature increase from 1850–1900 to 2010–201966 is 0.8°C to 1.3°C, with a best estimate of 1.07°C. It is likely that well-mixed GHGs 67 contributed a warming of 1.0°C to 2.0°C, and other human drivers (principally aerosols) contributed a cooling of 0.0°C to 0.8°C, natural (solar and volcanic) drivers changed global surface temperature by ±0.1°C and internal variability changed it by ±0.2°C. {WGI SPM A.1, WGI SPM A.1.2, WGI SPM A.1.3, WGI SPM A.2.2, WGI Figure SPM.2; SRCCL TS.2}

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Rapid reductions in non-CO2GHGs, particularly methane, would lower the level of peak warming (high confidence). Residual non-CO2 emissions at the time of reaching net zero CO2 range between 5 and 11 GtCO2-eq yr –1 in pathways limiting warming to 2°C (>67%) or lower. Methane (CH4) is reduced by around 19% (4–46%) in 2030 and 45% (29–64%) in 2050, relative to 2019. Methane emission reductions in pathways limiting warming to 1.5°C (>50%) with no or limited overshoot are substantially higher by 2030, 34% (21–57%), but only moderately so by 2050, 51% (35–70%). Methane emissions reductions are thus attainable at relatively lower GHG prices but are at the same time limited in scope in most 1.5°C–2°C pathways. Deeper methane emissions reductions by 2050 could further constrain the peak warming. N2O emissions are reduced too, but similar to CH4, emission reductions saturate for more stringent climate goals. In the mitigation pathways, the emissions of cooling aerosols are reduced due to reduced use of fossil fuels. The overall impact on non-CO2-related warming combines these factors. {3.3}

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For the scenarios in the C1 category (limit warming to 1.5°C (>50% with no or limited overshoot, the net zero year for CO2 emissions is typically around 2035–2070. For scenarios in C3 (limiting warming to 2°C (>67%)), CO2 emissions reach net zero around after 2050. Similarly, also the years for net zero GHG emissions can be calculated (see Fig 3.14b. The GHG net zero emissions year is typically around 10–40 years later than the carbon neutrality. Residual non-CO2 emissions at the time of reaching net zero CO2 range between 5–11 GtCO2-eq in pathways that limit warming to 2°C (>67%) or lower. In pathways limiting warming to 2°C (>67%), methane is reduced by around 19% (3–46%) in 2030 and 46% (29–64%) in 2050, and in pathways limiting warming to 1.5°C (>50%) with no or limited overshoot by around 34% (21–57%) in 2030 and a similar 51% (35–70%) in 2050. Emissions-reduction potentials assumed in the pathways become largely exhausted when limiting warming to 2°C (>50%). N2O emissions are reduced too, but similar to CH4, emission reductions saturate for stringent climate goals. In the mitigation pathways, the emissions of cooling aerosols are reduced due to reduced use of fossil fuels. The overall impact on non-CO2-related warming combines these factors.

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Systematic reviews of the literature on co-benefits and trade-offs from mitigation actions have shown that only a small portion of articles provide economic quantifications (Deng et al. 2017; Karlsson et al. 2020). Most economic quantifications use monetary valuation approaches. Improved air quality, and associated health effects, are the co-benefit category dominating the literature (Markandya et al. 2018; Vandyck et al. 2018; Scovronick et al. 2019; Howard et al. 2020; Karlsson et al. 2020 b; Rauner et al. 2020a,b), but some studies cover other categories, including health effects from diet change (Springmann et al. 2016b) and biodiversity impacts (Rauner et al. 2020a). Regarding health effects from air quality improvement and from diet change, co-benefits are shown to be of the same order of magnitude as mitigation costs (Thompson et al. 2014; Springmann et al. 2016a,b; Markandya et al. 2018; Scovronick et al. 2019b; Howard et al. 2020; Rauner et al. 2020a,b; Liu et al. 2021; Yang et al. 2021). Co-benefits from improved air quality are concentrated sooner in time than economic benefits from avoided climate change impacts (Karlsson et al. 2020), such that when accounting both for positive health impacts from reduced air pollution and for the negative climate effect of reduced cooling aerosols, optimal GHG mitigation pathways exhibit immediate and continual net economic benefits (Scovronick et al. 2019a). However, AR6 WGI Chapter 6 (Section 6.7.3) shows a delay in air pollution reduction benefits when they come from climate change mitigation policies compared with air pollution reduction policies.

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Ocean warming reduces the vertical supply of nutrients to the upper ocean due to increasing stratification (Section 9.2.1.4) but may also act to alleviate seasonal light limitation. The projected effect is to decrease PP at low latitudes and increase PP at high latitudes (Kwiatkowski et al., 2020). Future changes to dust deposition due to desertification (Mahowald et al., 2017), alterations to the nitrogen cycle (Section 5.3.3.2; SROCC, Section 5.2.3.1.2), and reducing sea ice cover (Ardyna and Arrigo, 2020) all have the potential to alter PP regionally. Higher ocean temperatures tend to result in higher metabolic rates, although respiration may increase more rapidly than PP (Boscolo-Galazzo et al., 2018; Brewer, 2019; Cavan et al., 2019). Ocean warming and reduced PP are expected to result in lower zooplankton abundance, and the expansion of oxygen minimum zones (OMZs) may reduce the ability of zooplankton to remineralize POC, thus increasing the efficiency of the BCP and forming a negative climate feedback (Cavan et al., 2017). Increased microbial respiration due to warming may result in greater quantities of organic carbon transferred into the dissolved organic carbon pool (Jiao et al., 2014; Legendre et al., 2015; Roshan and DeVries, 2017) which, while increasing the residence time of carbon in the ocean, would ultimately reduce the sedimentary burial, and hence sequestration on geologic time scales (Olivarez Lyle and Lyle, 2006).

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The scope of this section is to assess the general and methods-specific effects of CDR on the global carbon cycle and other biogeochemical cycles. The focus is on Earth system feedbacks that either amplify or reduce carbon sequestration potentials of specific CDR methods, and determine their effectiveness in reducing atmospheric CO2 and mitigating climate change. Technical carbon sequestration potentials of CDR methods are assessed on a qualitative scale; a comprehensive quantitative assessment is left to the AR6 Working Group III Report (Chapters 7 and 12). Biogeochemical and biophysical side effects of CDR methods are assessed here, while the co-benefits and trade-offs for biodiversity, water and food production are briefly discussed for completeness, but a comprehensive assessment is left to WGII (Chapters 2 and 5) and WGIII (Chapters 7 and 12). The assessment in this chapter emphasizes literature published since the AR5 WGI report (Chapter 6) for the assessment of the global carbon cycle response to CDR, and literature published since SR1.5 (Chapter 4; IPCC, 2018), SRCCL (Chapter 6, IPCC, 2019a) and SROCC (Bindoff et al., 2019) for the assessment of potentials and side effects of specific CDR methods. Emerging literature on deliberate methane removal is also briefly discussed.

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The AR5 WGI (Chapter 6) discussed the CDR methods, their implications and unintended side effects on carbon cycle and climate, including their time scales and potentials. Since then, three IPCC special reports (SR) have been published. First, SR1.5 (Chapter 4 (IPCC, 2018) assessed the potentials and current understanding, including the side effects, of bioenergy with carbon capture and storage (BECCS), afforestation/reforestation, soil carbon sequestration, biochar, enhanced weathering, ocean alkalinization, direct air carbon capture with storage (DACCS) and ocean fertilization. Second, SRCCL (Chapter 6 (IPCC, 2019a) assessed the potentials, co-benefits and trade-offs of land-based mitigation options. It assessed with high confidence that land-based CDR options do not sequester carbon indefinitely, except for peatland restoration. Multiple co-benefits were identified in the deployment of CDR options, many of them with a potential to make positive contributions to sustainable development, enhancement of ecosystem functions and services and other societal goals. However, their potential was concluded to be context specific, and limits were identified in their contribution to global mitigation, such as competition for land. The third report, the Special Report on Ocean and the Cryosphere in a Changing Climate (SROCC) Chapter 5 (IPCC, 2019b), assessed the potential of marine options for climate change mitigation. It concluded that the feasibility of open ocean fertilization and alkalinization approaches were negligible, due to their inconclusive influence on ocean carbon storage on long time scales, due to the unintended side effects on marine ecosystems, and the associated governance challenges. The assessment of blue carbon ecosystems concluded that they could contribute only minimally to atmospheric CO2 reduction globally, but emphasized that the benefits of protecting and restoring coastal blue carbon extend beyond climate change mitigation (SROCC (Section 5.5.12).

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In response to increasing risks to permanence of carbon stocks of some types of afforestation practices and the competition for land, there has been an increasing recognition that secondary forest regrowth and restoration of degraded forests and non-forest ecosystems can play a large role in carbon sequestration (high confidence). The rational for this focus builds on their high carbon stocks and rates of sequestration (Griscom et al., 2017; Lewis et al., 2019; Maxwell et al., 2019; Pugh et al., 2019), high resilience to disturbances (Dymond et al., 2014; Messier et al., 2019), and additional benefits such as enhanced biodiversity (Strassburg et al., 2020).

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The global sequestration potential of forestation varies substantially depending on the scenario-assumptions of available land and of background climate (AR6 WGIII, Section 7.5). Afforestation of native grasslands, savannas, and open-canopy woodlands leads to the undesirable loss of unique natural ecosystems with rich biodiversity, carbon storage and other ecosystem services (Veldman et al., 2015; IPBES, 2018). Comprehensive approaches to assess the effectiveness of land-based carbon removal options need to be based on the whole carbon cycle, covering both carbon stocks and flows, and establishing the links between human activities and their impacts on the biosphere and atmosphere (Keith et al., 2021).

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A range of mechanisms could enhance CO2 sequestration of forest-based methods under future scenarios, including CO2 fertilization, soil carbon enrichment due to enhanced litter input, or the northward shift of the tree line in future climate projection (Bathiany et al., 2010; Sonntag et al., 2016; Boysen et al., 2017b; Harper et al., 2018). There is low confidence in the net direction of feedbacks of afforestation on global mean temperature. The feedbacks are highly region dependent. For instance, afforestation at high latitudes would decrease albedo and increase local warming, while at low latitudes, the cooling effect of enhanced evapotranspiration could exceed the warming effect due to albedo decrease (Pearson et al., 2013; W. Zhang et al., 2013; Jia et al., 2019, SRCCL, Section 2.6.1). Both afforestation and reforestation affect the hydrological cycle through increased volatile organic compound (VOC) emissions and cloud albedo (Teuling et al., 2017; Kalliokoski et al., 2020), enhanced precipitation (Ellison et al., 2017) and increased transpiration, with potential effects on runoff and, especially in dry regions, on water supply (Figure 5.36 and Cross-Chapter Box 5.1; Farley et al., 2005; Smith et al., 2016; Krause et al., 2017; Teuling et al., 2019). Forest-based methods can either raise or lower N2O emissions, depending on tree species, previous land use, soil type and climatic factors (low confidence) (Figure 5.36 and Supplementary Materials, Table 5.SM.4; Benanti et al., 2014; Chen et al., 2019; McDaniel et al., 2019). Afforestation will decrease biodiversity if native species are replaced by monocultures (high confidence), while there is medium confidence that biodiversity is improved when forests are introduced into land areas with degraded soils or intensive monocultures, or where native species are re-introduced into managed land (Figure 5.36, Supplementary Materials Table 5.SM.4; Hua et al., 2016; Williamson and Bodle, 2016; P. Smith et al., 2018; Holl and Brancalion, 2020).

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Soil carbon losses from human agriculture accounted for about 116 PgC in the last 12,000 years (Section 5.2.1.2; Sanderman et al., 2017). With best management practices, two-thirds of these losses may be recoverable, setting a theoretical maximum of 77 PgC that can be sequestered in soils. Methods to increase soil carbon content may be applied to the restoration of marginal or degraded land (Paustian et al., 2016; Smith, 2016), but may also be used in traditional agricultural lands. A simple practice is to increase the input of carbon to the soil by selecting appropriate varieties or species with greater root mass (Kell, 2011) or higher yields and net primary production (NPP) (Burney et al., 2010). In addition, improved agricultural practices also increase soil carbon content. These include using crop rotation cycles, increasing the amount of crop residues, using crop cover to prevent periods of bare soil (Poeplau and Don, 2015; Griscom et al., 2017), optimizing grazing (Henderson et al., 2015) and residue management (Wilhelm et al., 2004), using irrigation (Campos et al., 2020), employment of low-tillage or no-tillage (W. Sun et al., 2020), agroforestry, cropland nutrient recycling, and avoiding grassland conversion (Paustian et al., 2016; Fargione et al., 2018). With medium confidence, methods that seek soil carbon sequestration will diminish nitrous oxide (N2O) emissions and nutrient leaching, and improve soil fertility and biological activity (Figure 5.36; Tonitto et al., 2006; Fornara et al., 2011; Paustian et al., 2016; Smith et al., 2016; SRCCL, Section 2.6.1.3, IPCC, 2019a). However, if improved soil carbon sequestration practices involve higher fertilization rates, N2O emissions would increase (Gu et al., 2017). Some soil carbon sequestration methods, such as cover crops and crop diversity, can increase biodiversity (medium confidence) (Paustian et al., 2016; P. Smith et al., 2018).

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The concept of BECCS rests on the premise that bioenergy production is carbon neutral – that is, as much CO2 is sequestered when growing biomass as feedstock as is released by its combustion. If these emissions are also captured and stored, the net effect is removal of CO2 from the atmosphere (Fuss et al., 2018). Sequestration potentials from BECCS depend strongly on the feedstock, climate, and management practices (Beringer et al., 2011; Kato and Yamagata, 2014; Heck et al., 2016; Smith et al., 2016; Krause et al., 2017). If woody bioenergy plants replace marginal land, net carbon uptake increases, enriching soil carbon (Don et al., 2012; Heck et al., 2016; Boysen et al., 2017a, b). However, replacing carbon-rich ecosystems with herbaceous bioenergy plants could deplete soil-carbon stocks and reduce the additional sink capacity of standing forests (Don et al., 2012; Harper et al., 2018). Furthermore, wood-based BECCS may not be carbon negative in the first decades, initially emitting more CO2 than sequestering (Sterman et al., 2018). BECCS has several trade-offs to deal with, including possible threats to water supply and soil nutrient deficiencies (medium confidence) (SRCCL Chapters 2 and 6, and Cross-Chapter Box 5.1; Smith et al., 2016; Krause et al., 2017; de Coninck et al., 2018; Heck et al., 2018; Roy et al., 2018). Deployment of BECCS at the scales envisioned by many 1.5°C–2.0°C mitigation scenarios could threaten biodiversity and require large land areas, competing with afforestation, reforestation and food security (Anderson and Peters, 2016; P. Smith et al., 2018). Additional risks and side effects are related to geologic carbon storage (Fuss et al., 2018; see also Section 5.6.2.2.4).

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In conclusion, land-based CDR methods that rely on enhanced net biological uptake and storage of carbon, have a wide range of biogeochemical and biophysical side effects. These side effects can (directly or indirectly) strengthen or weaken the climate change mitigation effect of a given method, or affect water quality and quantity, food supply and biodiversity (Figure 5.36). With the exception of weakened ocean carbon sequestration, there is low confidence in the Earth system feedbacks of these methods. Most methods are associated with a range of biogeochemical and biophysical side effects and co-benefits and trade-offs, but these are often highly dependent on local context, management regime, prior land use, and scale (high confidence). Highest co-benefits are obtained with methods that seek to restore natural ecosystems and improve soil carbon sequestration (Figure 5.36) while highest trade-off possibilities (symmetry with the highest co-benefits) occur for reforestation or afforestation with monocultures and BECCS, again with strong dependence on scale and context (medium confidence).

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Both ocean biological and physical processes drive the CO2 exchange between the ocean and atmosphere. However, the ocean physical processes that remove CO2 from the atmosphere, such as large-scale circulation, cannot be feasibly altered, so ocean CDR methods focus on increasing the productivity of ocean ecosystems, and subsequent sequestration of carbon (GESAMP, 2019). There has been no change to the assessment of SROCC (SROCCSection 5.5.1): there is low confidence that nutrient addition to the open ocean, either through artificial ocean upwelling or iron fertilization, could contribute to climate change mitigation, due to its inconclusive effect on carbon sequestration and risks of adverse side effects on marine ecosystems (Figure 5.36, Table 5.9; Supplementary Materials Text 5.SM.3 and Table 5.SM.4; AR6 WGIII Chapter 12; Gattuso et al., 2018; Boyd and Vivian, 2019; Feng et al., 2020). In addition, ocean fertilization is currently prohibited by the LondonProtocol (Dixon et al., 2014; GESAMP, 2019).

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Restoration of vegetated coastal ecosystems (sometimes referred to as ‘blue carbon’ – see Glossary) refers to the potential for increasing carbon sequestration by plant growth and burial of organic carbon in the soil of coastal wetlands (including salt marshes and mangroves) and seagrass ecosystems. Wider usage of the term blue carbon occurs in the literature, for example, including seaweeds (macroalgae), shelf sea sediments and open ocean carbon exchanges. However, such systems are less amenable to management, with many uncertainties relating to the permanence of their carbon stores (Windham-Myers et al., 2018; Lovelock and Duarte, 2019; SROCC, Section 5.5.1.1).

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Biogeochemical factors affecting reliable quantification of the climatic benefits of coastal vegetation include the variable production of CH4 and N2O by such ecosystems (Adams et al., 2012; Keller, 2018; Rosentreter et al., 2018), uncertainties regarding the provenance of the carbon that they accumulate (Macreadie et al., 2019), and the release of CO2 by biogenic carbonate formation in seagrass ecosystems (Kennedy et al., 2018). While coastal habitat restoration potentially provides significant mitigation of national emissions for some countries (Taillardat et al., 2018; Serrano et al., 2019), the global sequestration potential of blue carbon approaches is <0.02 PgC yr–1 (medium confidence) (Figure 5.36; SROCC, Section 5.5.1.2; Griscom et al., 2017; Gattuso et al., 2018; GESAMP, 2019; NASEM, 2019).

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Anderson, R.F. et al., 2019: Deep-Sea Oxygen Depletion and Ocean Carbon Sequestration During the Last Ice Age. Global Biogeochemical Cycles, 33(3), 301–317, doi: 10.1029/2018gb006049.

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Bowen, G.J. and J.C. Zachos, 2010: Rapid carbon sequestration at the termination of the Palaeocene–Eocene Thermal Maximum. Nature Geoscience, 3(12), 866–869, doi: 10.1038/ngeo1014.

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Boyd, P.W., H. Claustre, M. Levy, D.A. Siegel, and T. Weber, 2019: Multi-faceted particle pumps drive carbon sequestration in the ocean. Nature, 568(7752), 327–335, doi: 10.1038/s41586-019-1098-2.

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Campos, R., G.F. Pires, and M.H. Costa, 2020: Soil Carbon Sequestration in Rainfed and Irrigated Production Systems in a New Brazilian Agricultural Frontier. Agriculture, 10(5), 156, doi: 10.3390/agriculture10050156.

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de Vries, W. et al., 2009: The impact of nitrogen deposition on carbon sequestration by European forests and heathlands. Forest Ecology and Management, 258(8), 1814–1823, doi: 10.1016/j.foreco.2009.02.034.

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Fornara, D.A. et al., 2011: Increases in soil organic carbon sequestration can reduce the global warming potential of long-term liming to permanent grassland. Global Change Biology, 17(5), 1925–1934, doi: 10.1111/j.1365-2486.2010.02328.x.

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Gu, J. et al., 2017: Trade-off between soil organic carbon sequestration and nitrous oxide emissions from winter wheat-summer maize rotations: Implications of a 25-year fertilization experiment in Northwestern China. Science of The Total Environment, 595, 371–379, doi: 10.1016/j.scitotenv.2017.03.280.

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Jackson, R.B. et al., 2005: Atmospheric science: Trading water for carbon with biological carbon sequestration. Science, 310(5756), 1944–1947, doi: 10.1126/science.1119282.

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Jiao, N. et al., 2014: Mechanisms of microbial carbon sequestration in the ocean – future research directions. Biogeosciences, 11(19), 5285–5306, doi: 10.5194/bg-11-5285-2014.

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Kell, D.B., 2011: Breeding crop plants with deep roots: their role in sustainable carbon, nutrient and water sequestration. Annals of Botany, 108(3), 407–418, doi: 10.1093/aob/mcr175.

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Lorenz, K. and R. Lal, 2014: Biochar application to soil for climate change mitigation by soil organic carbon sequestration. Journal of Plant Nutrition and Soil Science, 177(5), 651–670, doi: 10.1002/jpln.201400058.

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NASEM, 2019: Negative Emissions Technologies and Reliable Sequestration: A Research Agenda. National Academies of Sciences, Engineering, and Medicine (NASEM). The National Academies Press, Washington, DC, USA, 510 pp., doi: 10.17226/25259.

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Ortega, A. et al., 2019: Important contribution of macroalgae to oceanic carbon sequestration. Nature Geoscience, 12(9), 748–754, doi: 10.1038/s41561-019-0421-8.

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Poeplau, C. and A. Don, 2015: Carbon sequestration in agricultural soils via cultivation of cover crops – A meta-analysis. Agriculture, Ecosystems & Environment, 200, 33–41, doi: 10.1016/j.agee.2014.10.024.

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Robinson, J. et al., 2014: How deep is deep enough? Ocean iron fertilization and carbon sequestration in the Southern Ocean. Geophysical Research Letters, 41(7), 2489–2495, doi: 10.1002/2013gl058799.

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Smith, P., 2016: Soil carbon sequestration and biochar as negative emission technologies. Global Change Biology, 22(3), 1315–1324, doi: 10. 1111/gcb.13178.

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Sun, W. et al., 2020: Climate drives global soil carbon sequestration and crop yield changes under conservation agriculture. Global Change Biology, 26(6), 3325–3335, doi: 10. 1111/gcb.15001.

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Walker, A.P. et al., 2015: Predicting long-term carbon sequestration in response to CO2 enrichment: How and why do current ecosystem models differ?Global Biogeochemical Cycles, 29(4), 476–495, doi: 10.1002/2014gb004995.

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Anderson, R.F. et al., 2019: Deep-Sea Oxygen Depletion and Ocean Carbon Sequestration During the Last Ice Age. Global Biogeochemical Cycles, 33(3), 301–317, doi: 10.1029/2018gb006049.

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CDR options include afforestation, soil carbon sequestration, bioenergy with carbon capture and storage (BECCS), wet land restoration, ocean fertilization, ocean alkalinisation, enhanced terrestrial weathering and direct air capture and storage (see Section 5.6.2 and Table 5.9 for a more complete discussion). Chapter 8 (Section 8.4.3) assesses the implications of CDR for water cycle changes. The potential of different CDR options in terms of the amount of CO2 removed per year from the atmosphere, costs, co-benefits and side effects of the CDR approaches are assessed in SR1.5 (de Coninck et al., 2018), the AR6 WGIII Report (see Chapters 7 and 12), and in several review papers (Fuss et al., 2018; Lawrence et al., 2018; Nemet et al., 2018). In the literature, CDR options are also referred to as ‘negative CO2 emissions technologies’.

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NRC, 2015a: Climate Intervention: Carbon Dioxide Removal and Reliable Sequestration. National Research Council (NRC). The National Academies Press, Washington, DC, USA, 154 pp., doi: 10.17226/18805.

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The terrestrial water and carbon cycles are also strongly coupled (Cross-Chapter Box 5.1). As atmospheric carbon dioxide (CO2) concentration increases, the physical environment in which plants grow is altered, including the availability of soil moisture necessary for plants’ CO2 uptake and, potentially, the effectiveness of CO2 removal techniques to mitigate climate change (Section 5.6.2.1.2). Rising surface CO2 concentrations also modify stomatal (small pores at the leaf surface) regulation as well as the plants’ biomass, thus affecting ecosystem photosynthesis and transpiration rates and leading generally to a net increase in water use efficiency (Lemordant et al., 2018). These coupled changes have profound implications for the simulation of the carbon and water cycles (Gentine et al. , 2019; see also Section 5.4.1), which can be better assessed with the new generation Earth system models, although both the carbon concentration and carbon-climate feedbacks remain highly uncertain over land Section 5.4.5; Arora et al., 2020). The water constraints on the terrestrial carbon sinks are a matter of debate regarding the feasibility or efficiency of some land-based CO2 removal and sequestration techniques requested to comply with the Paris Agreement (Section5.6.2.2.1; Fuss et al. , 2018; Belyazid and Giuliana, 2019).

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Belyazid, S. and Z. Giuliana, 2019: Water limitation can negate the effect of higher temperatures on forest carbon sequestration. European Journal of Forest Research, 138(2), 287–297, doi: 10.1007/s10342-019-01168-4.

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Deposition of reactive nitrogen (Nr; i.e., NH3 and NOx) increases the plant productivity and carbon sequestration in N-limited forests and grasslands, and also in open and coastal waters and open ocean. Such inadvertent fertilization of the biosphere can lead to eutrophication and reduction in biodiversity in terrestrial and aquatic ecosystems. The AR5 assessed that it is likely that Nr deposition over land currently increases natural CO2 sinks, in particular in forests, but the magnitude of this effect varies between regions (Ciais et al., 2013). Increasing Nr deposition or the synergy between increasing Nr deposition and atmospheric CO2 concentration could have contributed to the increasing global-net land CO2 sink (Section 5.2.1.4.1).

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de Vries, W., M. Posch, D. Simpson, and G.J. Reinds, 2017: Modelling long-term impacts of changes in climate, nitrogen deposition and ozone exposure on carbon sequestration of European forest ecosystems. Science of The Total Environment, 605–606, 1097–1116, doi: 10.1016/j.scitotenv.2017.06.132.

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In addition to deep, rapid, and sustained emission reductions, CDR can fulfil three complementary roles: lowering net CO2 or net GHG emissions in the near term; counterbalancing ‘ hard-to-abate’ residual emissions (e.g., some emissions from agriculture , aviation, shipping, industrial processes) to help reach net zero CO2 or GHG emissions, and achieving net negative CO2 or GHG emissions if deployed at levels exceeding annual residual emissions (high confidence) . CDR methods vary in terms of their maturity, removal process, time scale of carbon storage, storage medium, mitigation potential, cost, co-benefits, impacts and risks, and governance requirements. (high confidence). Specifically, maturity ranges from lower maturity (e.g., ocean alkalinisation) to higher maturity (e.g., reforestation); removal and storage potential ranges from lower potential (<1 Gt CO2 yr -1, e.g., blue carbon management) to higher potential (>3 Gt CO2 yr -1, e.g., agroforestry); costs range from lower cost (e.g., –45 to 100 USD tCO2-1 for soil carbon sequestration) to higher cost (e.g., 100 to 300 USD tCO2-1 for direct air carbon dioxide capture and storage) (medium confidence). Estimated storage timescales vary from decades to centuries for methods that store carbon in vegetation and through soil carbon management, to ten thousand years or more for methods that store carbon in geological formations. (high confidence). Afforestation, reforestation, improved forest management, agroforestry and soil carbon sequestration are currently the only widely practiced CDR methods (high confidence). Methods and levels of CDR deployment in global modelled mitigation pathways vary depending on assumptions about costs, availability and constraints (high confidence). {WGIII SPM C.3.5, WGIII SPM C.11.1, WGIII SPM C.11.4}

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Reforestation, improved forest management, soil carbon sequestration, peatland restoration and coastal blue carbon management are examples of CDR methods that can enhance biodiversity and ecosystem functions, employment and local livelihoods, depending on context 139 . However, afforestation or production of biomass crops for bioenergy with carbon dioxide capture and storage or biochar can have adverse socio-economic and environmental impacts, including on biodiversity, food and water security, local livelihoods and the rights of Indigenous Peoples, especially if implemented at large scales and where land tenure is insecure. (high confidence) {WGII SPM B.5.4, WGII SPM C.2.4; WGIII SPM C.11.2; SR1.5 SPM C.3.4, SR1.5 SPM C.3.5; SRCCL SPM B.3, SRCCL SPM B.7.3, SRCCL Figure SPM.3}

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Adoption of practices that build SOC can improve crop resilience to climate-change-related stresses such as agricultural drought. Iizumi and Wagai (2019) found that a relatively small increase in topsoil (0–30 cm) SOC could reduce drought damages to crops over 70% of the global harvested area. The effects of increasing SOC are more positive in drylands owing to more efficient use of rainwater, which can increase drought tolerance (Iizumi and Wagai, 2019). Similarly, Sun et al. (2020) found that, relative to local conventional tillage, conservation agriculture has a win-win outcome of enhanced C sequestration and increased crop yield in arid regions. However, the impact of no-till may be minimal if not supplemented with residue cover and cover crops. As such, this is a highly debated area where some authors argue that no-till has limited effect and the evidence outside drylands is weak. Furthermore, the use of crop residues is constrained by its alternative uses (e.g., fuel, livestock feed, etc.) in much of the developing world. Practices that build up SOC may encourage soil microbial populations, which in turn can increase yield stability under drought conditions (Prudent et al., 2020).

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Soil C sequestration is an important strategy to improve crop and livestock production sustainably that could be applied at large scales and at a low cost, if there was adequate institutional support and labour, using agroforestry, conservation agriculture, mixed cropping and targeted application of fertilizer and compost (high confidence) (Paustian et al., 2016; Kongsager, 2018; Nath et al., 2018; Woolf et al., 2018; Corbeels et al., 2019; Kuyah et al., 2019; Corbeels et al., 2020; Muchane et al., 2020; Sun et al., 2020; Nath et al., 2021). For example, a widespread adoption of agroforestry, conservation agriculture, mixed cropping and balanced application of fertilizer and compost by India’s small landholders could increase annual C sequestration by 70–130 Tg CO2 e (Nath et al., 2018; Nath et al., 2021).

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Also, the distribution and traits of trees are increasingly influenced by climate change, with impacts for local ecosystem service supply. In the USA, a study of 86 tree species/groups over the past three decades showed that more tree species have shifted westward (73%) than poleward (62%) in their abundance (Fei et al., 2017). This was due more to changes in moisture availability than to changes in temperature. As climate has warmed, trees are growing faster with longer growing seasons. However, a study of forests in Central Europe revealed that wood density has decreased since the 1870s (Pretzsch et al., 2018). This means that increasing tree growth might not directly translate to increased total biomass and carbon sequestration.

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Wild foods are important to many communities that live in and adjacent to humid tropical forests, but climate change impacts are mixed (Table 5.10, Dounias et al., 2007; Colfer, 2008; Powell et al., 2015; Rowland et al., 2017; Reyes-García et al., 2019). In some humid tropical forest regions, bushmeat is particularly important (Golden et al., 2011; Nasi et al., 2011; Fa et al., 2015; Powell et al., 2015; Rowland et al., 2017). In humid tropical regions, the impact of climate change on wild food availability, access and consumption is currently unclear and research is limited. There are, however, important interrelationships between climate change and wild food use in humid forests. For example, the loss of large mammals to bushmeat consumption and global trade will likely slow the regeneration of tropical forests in which a large number of tree species are dependent on large mammals for seed dispersal (Brodie and Gibbs, 2009). Conversely, others argue that bushmeat provides local communities with an important incentive to support local maintenance of forest cover and, thus, carbon sequestration (Bennett et al., 2007).

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Marine aquaculture food production is being impacted directly and indirectly by climate change (high confidence) (Bindoff et al., 2019). Ocean pH and oxygen levels are declining, whereas global warming, sea level rise and extreme events are increasing (Cross-Chapter Box SLR in Chapter 3, Canadell et al., 2021; Eyring et al., 2021; Fox-Kemper et al., 2021; Lee et al., 2021;). Marine heatwaves have been increasing in both incidence and longevity over the past century (Frolicher and Laufkotter, 2018; Oliver et al., 2018; Bricknell et al., 2021), with productivity consequences for marine aquaculture (mariculture), carbon sequestration and local species extinctions (high confidence) (Weatherdon et al., 2016; Smale et al., 2019). Temperature increases related to El Niño climatic oscillations have caused mass fish mortalities either through warming waters (e.g., Pacific threadfin in Hawaii (McCoy et al., 2017)) or associated HABs (e.g., 12% loss of Atlantic salmon as well as other fish and shellfish in Chile in 2016, with estimated USD 800 million in losses (high confidence) (Clement et al., 2016; Apablaza et al., 2017; Leon-Munoz et al., 2018; Trainer et al., 2020)). Increases in sea lice parasite infestations on salmon are related to higher salinity and warmer waters (medium confidence) (Groner et al., 2016; Soto et al., 2019). Ocean acidification is having negative impacts on the sustainability of mariculture production (high confidence) (Bindoff et al., 2019), with observed impacts on shellfish causing significant production and economic losses for regions, estimated at losses of nearly USD 110 million by 2015 in the Pacific Northwest (Barton et al., 2015; Ekstrom et al., 2015; Waldbusser et al., 2015; Zhang et al., 2017b; Doney et al., 2020). Ocean oxygen levels are declining due to climate change (Hoegh-Guldberg et al., 2018; IPCC, 2021), and decreased oxygen (hypoxia) has negative impacts on fish physiology (Cadiz et al., 2018; Hvas and Oppedal, 2019; Martos-Sitcha et al., 2019; Perera et al., 2021), fish growth, behaviour and sensitivity to concurrent stressors (high confidence) (Stehfest et al., 2017; Abdel-Tawwab et al., 2019).

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Marine aquaculture provides distinct ecosystem services through provisioning (augmenting wild fishery catches), regulating (coastal protection, carbon sequestration, nutrient removal, improved water clarity), habitat and supporting (artificial habitat) and cultural (livelihoods and tourism) services (Gentry et al., 2020), which vary with species, location and husbandry (Alleway et al., 2019). Projected thermal increases of 1.5°C will reduce ecosystem services, further reduced under 2°C warming, with associated increases in acidification, hypoxia, dead zones, flooding and water restrictions (medium confidence) (Hoegh-Guldberg et al., 2018). Sudden production losses from extreme climate events can exacerbate food security challenges across production sectors, including aquaculture, increasing global hunger (high confidence) (Cottrell et al., 2019; Food Security Information Network, 2020). While aquaculture provides positive influences such as food security and livelihoods, there are negative concerns over environmental impacts (including high nutrient loads from sites) and socioeconomic conflicts (Alleway et al., 2019; Soto et al., 2019), and adoption of ecosystem approaches is dependent on particular user groups and regions (Gentry et al., 2017; Brugère et al., 2019; Gentry et al., 2020). In coastal Bangladesh, projected saline inundation to wetland ecosystem services will result in ecosystem services losses of raw materials and food provisioning, ranging from USD 0 to 20.0 million under RCP2.6 to RCP8.5 scenarios (Mehvar et al., 2019). Mangrove deforestation for shrimp farming in Asia negatively impacts ecosystem services and reduces climate resilience (medium confidence) (Mehvar et al., 2019; Nguyen and Parnell, 2019; Reid et al., 2019; Custódio et al., 2020), while mangrove reforestation efforts may have some effectiveness in re-creating important nursery grounds for aquatic species (low confidence) (Gentry et al., 2017; Chiayarak et al., 2019; Hai et al., 2020). Families are highly vulnerable to climate change where nutritional needs are being met by self-production, such as in Mozambique, Namibia (Villasante et al., 2015), Zambia (Kaminski et al., 2018) and Bangladesh (high confidence) (Pant et al., 2014). Climate change will therefore affect multiple ecosystem services where ultimately decisions on balance or trade-offs will vary with regional perceptions of service value (high confidence).

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Adaptation options at the operational level include species selections, such as cultivation of brackish species (shrimp, crabs) during dry seasons, and rice-finfish in wetter seasons in Thailand (Chiayarak et al., 2019), use of salt-tolerant plants in Viet Nam (Nhung et al., 2019; Paik et al., 2020), converting inundated rice paddies into aquaculture, rotating shrimp, and rice culture (high confidence) (Chiayarak et al., 2019). Species diversification through co-culture, integrated aquaculture–agriculture (e.g., rice–fish) or integrated multi-trophic culture (e.g., shrimp–tilapia–seaweed or finfish–bivalve–seaweed) may maintain farm long-term performance and viability by: creating new aquaculture opportunities; promoting societal and environmental stability; reducing GHG emissions through reduced feed usage and waste; and carbon sequestration (medium confidence) (see Section 5.10, Ahmed et al., 2017; Bunting et al., 2017; Gasco et al., 2018, Soto et al., 2018; Ahmed et al., 2019; Dubois et al., 2019; FAO, 2019c; Li et al., 2019; Freed et al., 2020; Galappaththi et al., 2020b; Prasko et al., 2020; Tran et al., 2020). In practice, most aquaculture operations concentrate on single-species systems (Metian et al., 2020), and barriers such as land availability, freshwater resources and lack of credit access may limit the uptake and success of integrated adaptation approaches to climate change (Ahmed et al., 2019; Tran et al., 2020; Kais and Islam, 2021).

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Shade-grown cocoa and coffee agroforestry systems provide an array of ecosystem services, including regulating pests and diseases, maintaining soil fertility, maintaining biodiversity and carbon sequestration (high confidence) (Jha et al., 2014; Rajab et al., 2016; Cerda et al., 2017; Pham et al., 2019). For example, a comparison of Indonesian cocoa stands found that total carbon stocks above and below ground were five times higher in multi-shade agroforestry stands compared with monoculture stands (57 compared with 11 Mg C ha −1), and total NPP was twice as high (18 compared with 9 Mg C ha −1 yr −1). The extra carbon sequestration was achieved without any notable difference in cocoa yield (Rajab et al., 2016). At higher levels of shade, there can be negative impacts on the yield of the understory crop, but careful management of shade trees allows for both crops to thrive (Andreotti et al., 2018; Blaser et al., 2018; Niether et al., 2020).

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Chen, Y.Z., et al., 2018b: Great uncertainties in modeling grazing impact on carbon sequestration: a multi-model inter-comparison in temperate Eurasian Steppe. Environ. Res. Lett. , 13 (7), 75005, doi:10.1088/1748-9326/aacc75.

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Corbeels, M., et al., 2020: Carbon sequestration potential through conservation agriculture in Africa has been largely overestimated comment on: “Meta-analysis on carbon sequestration through conservation agriculture in Africa”. Soil Tillage Res. , 196, 104300, doi:10.1016/j.still.2019.104300.

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Iizumi, T. and R. Wagai, 2019: Leveraging drought risk reduction for sustainable food, soil and climate via soil organic carbon sequestration. Sci Rep, 9, 19744, doi:10.1038/s41598-019-55835-y.

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Kent, R. and R. Hannay, 2020: Explaining “carbon” in community sequestration projects: a key element in the creation of local carbon knowledges. Environ. Commun. , 14 (3), 364–377, doi:10.1080/17524032.2019.1673459.

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Nath, A.J., et al., 2021: Quantifying carbon stocks and sequestration potential in agroforestry systems under divergent management scenarios relevant to India’s Nationally Determined Contribution. J Clean Prod, 281, doi:10.1016/j.jclepro.2020.124831.

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Paudel, D., K.R. Tiwari, R.M. Bajracharya and N. Raut, 2017: Agroforestry system: an opportunity for carbon sequestration and climate change adaptation in the mid-hills of Nepal. Octa J. Environ. Res. , 5 (1).

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Rajab, Y.A., et al., 2016: Cacao cultivation under diverse shade tree cover allows high carbon storage and sequestration without yield losses. PLoS ONE, 11 (2), e149949.

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Ramachandran Nair, P.K., M. Kumar and V.D. Nair, 2009: Agroforestry as a strategy for carbon sequestration. J Plant Nutr Soil Sci, 172 (1), 10–23, doi:10.1002/jpln.200800030.

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Sun, W., et al., 2020: Climate drives global soil carbon sequestration and crop yield changes under conservation agriculture. Glob Chang Biol, 26 (6), 3325–3335, doi:10.1111/gcb.15001.

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For tropical and subtropical regions, the interplay of atmospheric CO2 with precipitation and temperature becomes of particular importance for future carbon uptake, since in warm and dry environments, elevated CO2 fosters plants with C3 photosynthesis and enhances their water-use efficiency relative to C4 species (Moncrieff et al., 2014a; Midgley and Bond, 2015; Knorr et al., 2016a). As a consequence, enhanced woody cover is expected to occur in the future, especially in mesic savannas, while in xeric savannas an increase in woody cover would occur in regions with enhanced precipitation (Criado et al., 2020). Even though semiarid regions have dominated the global trend in land CO2 uptake in recent decades (Ahlström et al., 2015), so far, most studies that investigated future climate change impacts on savanna ecosystems have concentrated on changes in the extent of land area affected (2.5.2.5) rather than on carbon cycling, with medium confidence for increasing woody cover:grass ratios (Moncrieff et al., 2014a; Midgley and Bond, 2015; Moncrieff et al., 2016; Criado et al., 2020). Increases in woody vegetation in what is now grass-dominated would possibly come with a carbon benefit, for instance, it was found that a broad range of future climate and CO2 changes would enhance vegetation carbon storage on Australian savannas (Scheiter et al., 2015). Results from a number of field experiments indicate, however, that impacts on total ecosystem carbon storage may be smaller due to a loss in below-ground carbon (Coetsee et al., 2013; Wigley et al., 2020). Nunez et al. (2021) critique existing incentives to promote the invasion of non-native trees into treeless areas as a means of carbon sequestration, raising doubts about the effects on fire, albedo, biodiversity and water yield (see Box 2.2).

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Most of this section therefore focusses on human interventions to build the resilience of ecosystems, enable species to survive or to adjust management to climate change. Vulnerability is, in many cases, exacerbated by the degraded state of many ecosystems as a result of human exploitation and LUC, leading to the fragmentation of habitats, the loss of species and impaired ecosystem function. This interaction between climate change and environmental degradation means that protecting ecosystems in a natural or near-natural state will be an important pre-requisite for maintaining resilience and give many species the best chance of persisting in a changed climate (Belote et al., 2017; Arneth et al., 2020; Ferrier et al., 2020; França et al., 2020). Protection from degradation, deforestation and exploitation is also essential to maintain critical ecosystem services, including carbon storage and sequestration and water supply (Dinerstein et al., 2020; Pörtner et al., 2021).

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Large-scale protection and restoration of ecosystems can also make a significant contribution to climate change mitigation (Dinerstein et al., 2020; Roberts et al., 2020a; Soto-Navarro et al., 2020). Globally, there is a 38% overlap between areas of high carbon storage and high intact biodiversity (mainly in the peatland tropical forests of Asia, the western Amazon and the high Arctic), but only 12% of this is protected (high confidence) (see also sections 2.4.4.4.1, 2.4.4.4.3, 2.5.3.4) (Soto-Navarro et al., 2020). Peatlands are particularly important carbon stores but are threatened by human disturbance, LULCC (Leifeld et al., 2019) and fire (sections 2.4.3.8, 2.5.2.8) (Turetsky et al., 2015). Restoration of peatlands is not only an efficient climate solution in terms of emissions of GHGs (Nugent et al., 2019), it may also increase ecosystem resilience (Glenk et al., 2021). Global restoration efforts are ongoing to target degraded temperate peatlands in the Americas and Europe (Chimner et al., 2017) in recognition of their importance for climate change mitigation (Paustian et al., 2016; Bossio et al., 2020; Humpenöder et al., 2020; Drever et al., 2021; Tanneberger et al., 2021). It has been estimated that the global GHG-saving potential of peatland restoration is similar to the most optimistic sequestration potential from all agricultural soils (Leifeld and Menichetti, 2018). However, the pressure on peatlands from human activity remains high in many parts of the world (Humpenöder et al., 2020; Tanneberger et al., 2021). Currently, the rapid destruction of tropical peatlands overshadows any current restoration efforts in temperate peatlands or any potential carbon gain from natural high-latitude peatlands (Roucoux et al., 2017; Wijedasa et al., 2017; Leifeld et al., 2019) (Sections 2.4.3.8, 2.4.4.4.2, 2.4.4.4.4, 2.5.2.8, 2.5.3.4).

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Abreu, R. C. R. et al., 2017: The biodiversity cost of carbon sequestration in tropical savanna. Science advances, 3 (8), e1701284.

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De Stefano, A. and M. G. Jacobson, 2018: Soil carbon sequestration in agroforestry systems: a meta-analysis. Agroforestry systems, 92 (2), 285–299.

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Deb Burman, P. K. et al., 2020: The effect of Indian summer monsoon on the seasonal variation of carbon sequestration by a forest ecosystem over North-East India. SN Applied Sciences, 2 (2), 154, doi:10.1007/s42452-019-1934-x.

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Dommain, R., J. Couwenberg and H. Joosten, 2011: Development and carbon sequestration of tropical peat domes in south-east Asia: links to post-glacial sea-level changes and Holocene climate variability. Quaternary Science Reviews, 30 (7), 999–1010, doi:10.1016/j.quascirev.2011.01.018.

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Du, E. and W. de Vries, 2018: Nitrogen-induced new net primary production and carbon sequestration in global forests. Environmental Pollution, 242, 1476–1487, doi:10.1016/j.envpol.2018.08.041.

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Favero, A., A. Daigneault and B. Sohngen, 2020: Forests: Carbon sequestration, biomass energy, or both?Science Advances, 6 (13), eaay6792, doi:10.1126/sciadv.aay6792.

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Ryan, J., 2019: Changes in organic carbon in long-term rotation and tillage trials in northern Syria. In: Management of carbon sequestration in soil[Lal, R., J. M. Kimble, R. F. Follett and B. A. Stewart (eds.)]. CRC Press, New York, NY, USA, pp. 285–296. ISBN 1351074253.

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Schulte-Uebbing, L. and W. de Vries, 2018: Global-scale impacts of nitrogen deposition on tree carbon sequestration in tropical, temperate, and boreal forests: A meta-analysis. Global Change Biology, 24 (2), E416-E431, doi:10.1111/gcb.13862.

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Shi, L., W. Feng, J. Xu and Y. Kuzyakov, 2018: Agroforestry systems: Meta-analysis of soil carbon stocks, sequestration processes, and future potentials. Land degradation & development , 29 (11), 3886–3897.

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Sun, W. et al., 2020: Climate drives global soil carbon sequestration and crop yield changes under conservation agriculture. Global Change Biology, 26 (6), 3325–3335, doi:10.1111/gcb.15001.

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Syktus, J. I. and C. A. McAlpine, 2016: More than carbon sequestration: Biophysical climate benefits of restored savanna woodlands. Scientific Reports, 6, doi:10.1038/srep29194.

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van der Heijden, G. M., S. A. Schnitzer, J. S. Powers and O. L. Phillips, 2013: Liana Impacts on Carbon Cycling, Storage and Sequestration in Tropical Forests. Biotropica, 45 (6), 682–692.

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Wigley, B. J. et al., 2020: Grasses continue to trump trees at soil carbon sequestration following herbivore exclusion in a semiarid African savanna. Ecology, doi:10.1002/ecy.3008.

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Zhang, Y. L., C. H. Song, L. E. Band and G. Sun, 2019b: No Proportional Increase of Terrestrial Gross Carbon Sequestration From the Greening Earth. Journal of Geophysical Research-Biogeosciences, 124 (8), 2540–2553, doi:10.1029/2018jg004917.

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Estuaries, deltas and lagoons encounter environmental gradients over small spatial scales, generating diverse habitats that support myriad ecosystem services, including food provision, regulation of erosion, nutrient recycling, carbon sequestration, recreation and tourism, and cultural significance (D’Alelio et al., 2021; Keyes et al., 2021). Although these coastal ecosystems have historically been sensitive to erosion-accretion cycles driven by sea level, drought and storms (high confidence) (Peteet et al., 2018; Wang et al., 2018c; Jones et al., 2019b; Urrego et al., 2019; Hapsari et al., 2020; Zhao et al., 2020b), they were impacted for much of the 20th century primarily by non-climate drivers (very high confidence) (Brown et al., 2018b; Ducrotoy et al., 2019; Elliott et al., 2019; He and Silliman, 2019; Andersen et al., 2020; Newton et al., 2020; Stein et al., 2020). Nevertheless, the influence of climate-induced drivers has become more apparent over recent decades (medium confidence) (Table 3.6).

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Biogeographic shifts lead to novel communities and biotic interactions (high confidence) (Zarco-Perello et al., 2017; Pecuchet et al., 2020b), with concomitant changes in ecosystem functioning and servicing (high confidence) (Vergés et al., 2019; Nagelkerken et al., 2020; Peleg et al., 2020). For instance, temperature-driven changes in distribution and abundance of copepods, the dominant zooplankton, were observed between 1960 and 2014 in the North Atlantic. These changes subsequently affect biogenic carbon cycling through alteration of microbial remineralisation and carbon sequestration in deep water (medium confidence) (Section 3.4.3.6; Pitois and Fox, 2006; Brun et al., 2019).

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Coastal vegetated ecosystems are vulnerable to harm from multiple climate-induced and non-climate drivers, and together these have reduced wetland area globally (high confidence) (Section 3.4.2.5) and endangered the services provided by these ecosystems (high confidence). Loss of coastal vegetated ecosystems changes biodiversity (Sections 3.5.2, 3.4.2.3–3.4.2.5; Numbere, 2019; Parreira et al., 2021), increases risk of damage and erosion from SLR and storms (Sections 3.4.2.3–3.4.2.5; Cross-Chapter Box SLR in Chapter 3; Galeano et al., 2017) and impacts provisioning (Sections 3.5.3–3.5.4; Li et al., 2018b; Maina et al., 2021). These changes also strongly determine the quantity and longevity of blue carbon storage (high confidence) (Macreadie et al., 2019; Lovelock and Reef, 2020). Specific site characteristics and ecosystem responses to climate change will determine future local blue carbon storage or loss (high confidence) (see Table Box 3.4.2). For instance, poleward migration of mangroves to areas dominated by salt marshes is expected to increase carbon storage (Kelleway et al., 2016); however, this change in the dominant vegetation and associated faunal changes can modify carbon stocks and sequestration, as well as other ecosystem services (Martinetto et al., 2016; Kelleway et al., 2017; Smee et al., 2017; Macreadie et al., 2019; Macy et al., 2019). Landward range expansion of mangroves, marshes and seagrass in response to gradual RSLR can enhance carbon sequestration (Section 3.4.2.5; Cross-Chapter Box SLR in Chapter 3; Macreadie et al., 2019), but coastal squeeze can limit this (Phan et al., 2015; Schuerch et al., 2018) and RSLR can either submerge and bury or erode and release stored blue carbon (Section 3.4.2.5; Macreadie et al., 2019; Lovelock and Reef, 2020). Gains and losses of mangrove habitat area (and therefore carbon storage) projected for nations under RCP4.5 and RCP8.5 depend primarily on the combination of SLR rate, adaptation scenario (including coastal development) and island or continental status (Lovelock and Reef, 2020). The influence of warming, MHWs and acidification on seagrass meadows (Kendrick et al., 2019; Strydom et al., 2020), and associated coralligenous reefs (Zunino et al., 2019), suggests that future warming and especially MHWs will cause more widespread loss of services from these ecosystems (Section 3.4.2.5). Loss of blue carbon ecosystems will not only halt carbon storage but also release stored carbon: emissions after 2000 due to global mangrove deforestation have been estimated at 23.5–38.7 Tg Cyr –1 (Ouyang and Lee, 2020). Mitigation estimates for avoided conversion and restoration of coastal wetlands and the implications of the impacts of climate change are assessed in WGIII AR6 Section 7.4.

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Alongi, D.M., 2018a: The blue economy: mitigation and adaptation. In: Blue Carbon: Coastal Sequestration for Climate Change Mitigation[Alongi, D.M.(ed.)]. Springer International Publishing, Cham, pp. 59–84. ISBN 978-3319916989.

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Alongi, D.M., 2018b: Kelp forests. In: Blue Carbon: Coastal Sequestration for Climate Change Mitigation[Alongi, D.M.(ed.)]. Springer International Publishing, Cham, pp. 53–57. ISBN 978-3319916989.

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Alongi, D.M., 2018c: Salt marshes. In: Blue Carbon: Coastal Sequestration for Climate Change Mitigation[Alongi, D.M.(ed.)]. Springer International Publishing, Cham, pp. 9–22. ISBN 978-3319916989.

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Alongi, D.M., 2018d: Seagrass meadows. In: Blue Carbon: Coastal Sequestration for Climate Change Mitigation[Alongi, D.M.(ed.)]. Springer International Publishing, Cham, pp. 37–51. ISBN 978-3319916989.

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Bayley, D.T.I., et al., 2021: Valuation of kelp forest ecosystem services in the Falkland Islands: a case study integrating blue carbon sequestration potential. One Ecosyst. , 6, e62811, doi:10.3897/oneeco.6.e62811.

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Boyd, P.W., et al., 2019: Multi-faceted particle pumps drive carbon sequestration in the ocean. Nature, 568 (7752), 327–335, doi:10.1038/s41586-019-1098-2.

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Briggs, N., G. Dall’Olmo and H. Claustre, 2020: Major role of particle fragmentation in regulating biological sequestration of CO2 by the oceans. Science, 367 (6479), 791, doi:10.1126/science.aay1790.

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Hossain, M.M., 2019: Future importance of healthy oceans: ecosystem functions and biodiversity, marine pollution, carbon sequestration, ecosystem goods and services. J. Ocean Coast. Econ. , 6 (2), 4, doi:10.15351/2373-8456.1103.

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Marbà, N., et al., 2015a: Impact of seagrass loss and subsequent revegetation on carbon sequestration and stocks. J. Ecol. , 103 (2), 296–302, doi:10.1111/1365-2745.12370.

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Mariani, G., et al., 2020: Let more big fish sink: Fisheries prevent blue carbon sequestration—half in unprofitable areas. Sci. Adv. , 6 (44), eabb4848, doi:10.1126/sciadv.abb4848.

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Martinetto, P., et al., 2016: Crab bioturbation and herbivory may account for variability in carbon sequestration and stocks in South West Atlantic salt marshes. Front. Mar. Sci. , 3, 122, doi:10.3389/fmars.2016.00122.

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Ortega, A., et al., 2019: Important contribution of macroalgae to oceanic carbon sequestration. Nat. Geosci. , 12 (9), 748–754, doi:10.1038/s41561-019-0421-8.

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Pedersen, M.F., et al., 2021: Carbon sequestration potential increased by incomplete anaerobic decomposition of kelp detritus. Mar. Ecol. Prog. Ser. , 660, 53–67, doi:10.3354/meps13613.

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Wedding, L.M., et al., 2021: Incorporating blue carbon sequestration benefits into sub-national climate policies. Glob. Environ. Change, 69, 102206, doi:10.1016/j.gloenvcha.2020.102206.

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Co-benefits are defined as mitigation benefits resulting from an adaptation response (Deng et al., 2017). Around a quarter of papers that documented positive adaptation outcomes also reported mitigation co-benefits. Agroforestry, community forests and forest-based adaptations are the most oft-cited examples of mitigation co-benefits (Bhatta et al., 2015; Etongo et al., 2015; Weston et al., 2015; Pandey et al., 2017; Sain et al., 2017; Sánchez and Izzo, 2017; Wood et al., 2017; Adhikari et al., 2018a; Hellin et al., 2018; Aniah et al., 2019; Quandt et al., 2019; also see Box 5.11). Other examples include mitigation benefits of climate-smart agricultural practices that reduce input intensity and help in carbon sequestration (Arslan et al., 2015; Somanje et al., 2017), retrofitting buildings in urban areas with energy-efficient devices for lowering electricity bills and emissions (Fitzgerald and Lenhart, 2016) and reuse of treated wastewater for irrigation and urban uses (Morote et al., 2019) (Box 4.5, 4.7.6).

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Bio-energy crop with carbon capture and storage (BECCS) involves CO2 sequestration as biofuel or forest bioenergy (Creutzig et al., 2015). BECCS has profound implications for water resources (Ai et al., 2020), depending on factors including the scale of deployment, land use, and other local conditions. Evaporative losses from biomass irrigation and thermal bioelectricity generation are projected to peak at 183 km 3 yr –1 in 2050 under a low overshoot scenario (Fuhrman et al., 2020). (Senthil Kumar et al., 2020) projected that while BECCS strategies like irrigating biomass plantations can limit global warming by the end of the 21st century to 1.5°C, this will double the global area and population living under severe water stress compared to the current baseline. Both BECCS (Muratori et al., 2016) and DAC can significantly impact food prices via demand for land and water (Fuhrman et al., 2020). The direction and magnitude of price movement will depend on future carbon prices, while vulnerable people in the Global South will be most severely affected (medium evidence, high agreement ).

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Afforestation and reforestation are considered one of the most cost-effective ways of storing carbon. An additional 0.9 billion ha of canopy cover in suitable locations could store 205 Gt of carbon (Bastin et al., 2019), but this estimate is deemed unrealistic. Aggressive afforestation and reforestation efforts can result in trade-offs between biodiversity, carbon sequestration, and water use (Smith et al., 2008). In northern China, ecological restoration by regreening drylands resulted in several environmental and social benefits (Mirzabaev et al., 2019) but also led to increased freshwater use in some pockets (Zhao et al., 2020 ). Afforestation and reforestation with appropriate broad-leaf species in temperate Europe (Schwaab et al., 2020) can offer water quality and quantity-related benefits, mitigate extreme heat, and buffer against drought (Staal et al., 2018). A global assessment on forest and water showed that forests influence the overall water cycle, including downstream water availability via rainfall-runoff dynamics and downwind water availability via recycled rainfall effects (Creed and van Noordwijk, 2018). The study concluded that afforestation and reforestation should be concentrated (Ellison et al., 2017) in water-abundant locations (to offset downstream impacts) and where transpiration can potentially be captured downwind as precipitation (Creed et al., 2019) (Cross-Chapter Box NATURAL in Chapter 2). Overall, extensive BECCS and afforestation/reforestation deployment can alter the water cycle at regional scales (high confidence) (Cross-Chapter Box 5.1 in Chapter 5, WGI, (Canadell et al., 2021)).

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Chen, B., et al., 2021: Dynamic Risk Assessment for Geologic CO2 Sequestration. 15th International Conference on Greenhouse Gas Control Technologies GHGT, Abu Dhabi, UAE, 15-18 March 2021.

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Turner, P.A., et al., 2018: The global overlap of bioenergy and carbon sequestration potential. Clim. Chang. , 148 (1), 1–10, doi:10.1007/s10584-018-2189-z.

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Walker, A.P., et al., 2015: Predicting long-term carbon sequestration in response to CO2 enrichment: how and why do current ecosystem models differ?Global Biogeochem. Cycles, 29 (4), 476–495, doi:10.1002/2014gb004995.

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Biodiversity and ecosystem services play a critical role in socioeconomic development as well as the cultural and spiritual fulfilment of the Asian population (IPBES, 2018). For example, species richness reaches its maximum in the ‘coral triangle’ of Southeast Asia (central Philippines and central Indonesia) (IPCA, 2017), and the extent of mangrove forests in Asia is about 38.7% of the global total (Bunting et al., 2018). These coastal ecosystems provide multiple ecosystem services related to food production by fisheries/aquaculture, carbon sequestration, coastal protection and tourism/recreation (Ruckelshaus et al., 2013).

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Mangroves, tidal marshes and seagrass meadows (collectively called coastal blue carbon ecosystems) have sequestered carbon dioxide from the atmosphere continuously over thousands of years, building stocks of carbon in biomass and organic rich soils. Carbon dynamics in mangrove-converted aquaculture in Indonesia indicate that the mean ecosystem carbon stocks in shrimp ponds are less than half of the relatively intact mangroves (Arifanti et al., 2019). Conversion of mangroves into shrimp ponds in the Mahakam Delta have resulted in a carbon loss equivalent to 226 years of soil carbon accumulation in natural mangroves. In the Philippines, abandoned fishpond reversion to former mangrove has been found to be favourable for enhancing climate change mitigation and adaptation (Duncan et al., 2016). Integrated mangrove-shrimp farming, with deforested areas not exceeding 50% of the total farm area, has been suggested to support both carbon sequestration as well as livelihood (Ahmed et al., 2018).

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In East Asia, the observed changes in agricultural flooding in different parts of China could influence farming systems and crop areas (Zhang et al., 2016b) as extreme events intensify in the context of changing climate. Agricultural management practice in China may also change to optimise soil organic carbon sequestration (Zhang et al., 2016a). A study on projected irrigation requirements under climate change using a soil-moisture model for 29 upland crops in the Republic of Korea showed that water scarcity is a major limiting factor for sustainable agricultural production (Hong et al., 2016). In terms of drought, despite increasing future precipitation in most scenarios, crop-specific agricultural drought is expected to be a significant risk due to rainfall variability (Lim et al., 2019a). On the other hand, a projected rise in water availability in the Korean Peninsula using multiple regional climate models and evapotranspiration methods indicates that it will likely increase agricultural productivity for both rice and corn, but would decrease significantly in rain-fed conditions (Lim et al., 2017b). Thus, irrigation and soil-water management will be a major factor in determining future farming systems and crop areas in the country.

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Financing green growth and low-carbon development can provide resilience benefits (high agreement , limited evidence). Kameyama et al. (2016) have estimated the cost of low-carbon investments that can provide resilience benefits in Asia and reported that such low-carbon development will cost in the range of 125–149 billion USD annually. A combination of public, private, bilateral and multilateral funding sources, and carbon-market offsets, were suggested to achieve this level of funding. In terms of the total resources available, a combination of public, private and bi- and multi-lateral funding could help the region to raise as much as 222.3–412.5 billion USD annually, with a possibility to reach higher amounts depending on the future economic growth of countries in the region. Soil carbon sequestration in agricultural soils was found to be a win–win solution for both mitigation and adaptation as it can help improve soils while increasing farm yields and incomes of smallholders (Aryal et al., 2020a).

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Ahmed, N., S. Thompson and M. Glaser, 2018: Integrated mangrove-shrimp cultivation: Potential for blue carbon sequestration. Ambio, 47 (4), 441–452, doi:10.1007/s13280-017-0946-2.

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Ko, C.-H., et al., 2017: Carbon sequestration potential via energy harvesting from agricultural biomass residues in Mekong River basin, Southeast Asia. Renew. Sustain. Energy Rev. , 68, 1051–1062, doi:10.1016/j.rser.2016.03.040.

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Zhang, L., et al., 2016a: Toward optimal soil organic carbon sequestration with effects of agricultural management practices and climate change in Tai-Lake paddy soils of China. Geoderma, 275, 28–39, doi:10.1016/j.geoderma.2016.04.001.

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Mitigation, including greenhouse gas emissions reductions, avoidance, and removal and sequestration, as well as management of other climate forcing factors (WGIII AR6), is a key element of addressing climate risk and pursuing CRD. There are numerous individual and system mitigation options throughout the economy and within human and natural systems (very high confidence) (Chapter 16; Section 18.5). Limiting global average warming has been found to reduce climate risks (IPCC, 2018a; IPCC, 2019b), and limiting global average warming to any temperature level has also been found to be associated with broad ranges of potential global emissions pathways that represent future uncertainty in the evolution of socioeconomic, technological, market and physical systems (very high confidence) (Rose and Scott, 2018; Rose and Scott, 2020). Pathways consistent with limiting warming to 2°C and below have been found to require significant deployment of mitigation options spanning energy, land use and societal transformation ((Lecocq et al., 2022; Riahi et al., 2022); Section 18.3). and substantial economic, energy, land use, policy and societal transformation (Lecocq et al., 2022; Riahi et al., 2022). Such emissions pathways would represent deviations from current trends that raise issues about their feasibility and therefore plausibility (Rose and Scott, 2018; Rose and Scott, 2020).

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As discussed in Section 18.2.5.3.1, there are synergies and trade-offs in mitigation, adaptation and sustainable development. For instance, the literature on the global cost-effectiveness of mitigation pathways provides insights regarding aggregate synergies and trade-offs between mitigation and sustainable development (e.g., Figure 18.5). Furthermore, linkages between mitigation and adaptation options have been shown, such as expected changes in energy demand due to climate change interacting with energy system development and mitigation options, changes in future agricultural production practices to manage the risks of potential changes in weather patterns affecting land-based emissions and mitigation strategies, or mitigation strategies placing additional demands on resources and markets. This increases pressure on and costs for adaptation, or ecosystem restoration that provides carbon sequestration and natural and managed ecosystem resiliency benefits, but also could constrain mitigation and impact consumer welfare (WGIII AR6).

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Coastal blue carbon ecosystems, such as mangroves, salt marshes and seagrasses, can help reduce the risks and impacts of climate change, with multiple co-benefits. Over 150 countries contain at least one of these coastal blue carbon ecosystems and over 70 contain all three. Successful implementation of measures of carbon storage in coastal ecosystems could assist several countries in achieving a balance between emissions and removal of greenhouse gases. Carbon storage in marine habitats can be up to 1,000 tC ha –1, higher than most terrestrial ecosystems. Conservation of these habitats would also sustain a wide range of ecosystem services, assist with climate adaptation by improving critical habitats for biodiversity, enhance local fishery production and protect coastal communities from sea level rise (SLR) and storm events (IPCC, 2019b). Ecosystem-based adaptation is a cost-effective coastal protection tool that can have many co-benefits, including supporting livelihoods, contributing to carbon sequestration and the provision of a range of other valuable ecosystem services (IPCC, 2019b).

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The emphasis on Green GDP is mirrored by another concept, Blue Growth, that focuses on pursuing sustainable development through the ecosystem services derived from ocean conservation (Mustafa et al., 2019). Synthesis studies suggest that more intensive use of ocean resources, such as scaling up seaweed aquaculture, can be used to enhance CO2-eq sequestration, thereby contributing to GHG mitigation, while also achieving other economic goals (Lillebø et al., 2017; Froehlich et al., 2019). Similarly, Sarker et al. (2018) present a framework for linking Blue Growth and CRD in Bangladesh, with Blue Growth representing an opportunity for adapting to climate change. Bethel et al. (2021) also links Blue Growth to resilience, noting that a Blue economy can help facilitate recovery from the COVID-19 pandemic. Nevertheless, consistent with earlier assessment of enabling conditions for system transitions (Section 18.4.2.1), implementation of Blue Growth initiatives is contingent upon the successful achievement of social innovation as well as creating an inclusive and cooperative governance structure (very high confidence) (Larik et al., 2017; Soma et al., 2018).

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The marine environment is important to the culture, health and well-being of the region’s diverse Indigenous Peoples, including those who had sovereign ownership, governance, resource rights, and stewardship over ‘Sea Country’ for many thousands of years before the current sea level stabilised approximately 6000 years ago and before current coastal ecosystems were established (Rist et al., 2019). Marine environments contribute AUD$69 billion per year to Australia’s economy (Eadie et al., 2011), and NZD$4 billion per year to New Zealand’s economy (MfE, 2016). They have a high proportion of rare and endemic species (Croxall et al., 2012) and provide ecosystem services including food production, coastal protection, tourism and carbon sequestration (Croxall et al., 2012; Kelleway et al., 2017). Half of the species within New Zealand’s seas are endemic (Costello et al., 2010b).

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Future ocean warming, coupled with periodic extreme heat events, is projected to lead to the continued loss of ecosystem services and ecological functions (high confidence) (Smale et al., 2019) as species further shift their distributions and/or decline in abundance (Day et al., 2018). Compounding climate-driven changes in the distribution of habitat-forming species, invasive macroalgae are predicted to exhibit higher growth under all higher pCO2 and lower pH conditions (Roth-Schulze et al., 2018). Corals and mangroves around northern Australia and kelp and seagrass around southern Australia are of critical importance for ecosystem structure and function, fishery productivity, coastal protection and carbon sequestration; these ecosystem services are therefore extremely likely 2 to decline with continued warming. Equally, many species provide important ecosystem structure and function in New Zealand’s seas including in the deep sea (Tracey and Hjorvarsdottir, 2019). The future level of sustainable exploitation of fisheries is dependent on how climate change impacts these ecosystems. Native kelp is projected to further decline in southeastern New Zealand with warming seas (Table 11.6). Climate change could affect New Zealand fisheries’ productivity (Cummings et al., 2021), and both ocean warming and acidification may directly affect shellfish culture (Cunningham et al., 2016; Cummings et al., 2019) and indirectly through changes in phytoplankton production (Pinkerton, 2017).

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Combined impacts from heavy rainfall, soil erosion, drought, fire and pest incursions are projected to increase risks to the permanence of carbon offset and removal strategies in New Zealand for meeting its climate change targets (PCE, 2019; Watt et al., 2019; Anderegg et al., 2020; Schenuit et al., 2021). Effective management of the interactions between mitigation and adaptation policies can be achieved through governance and institutions, including Māori tribal organisations and sectoral adaptation, to ensure effective and continued carbon sequestration and storage as the climate changes (medium confidence) (Lawrence et al., 2020b) (11.4.2) (Box 11.5). The productivity of radiata pine (P. radiata D. Don) in New Zealand due to higher CO2 is projected to increase by 19% by 2040 and 37% by 2090, but greater wind damage to trees is expected (Watt et al., 2019). Changes in the distribution of existing weeds, pests and diseases with potential establishment of new sub-tropical pests and seasonal invasions are projected (Kean et al., 2015; Watt et al., 2019; MfE, 2020a). Increased pathogens such as pitch canker, red needle cast and North American bark beetles could damage plantations (Hauraki Gulf Forum, 2017; Lantschner, 2017; Watt et al., 2019).

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The resilience of Australia’s major cities to flooding and drought has been advanced through a range of economic and physical interventions. Water-sensitive urban design irrigates vegetation with harvested storm water that improves water security, flood risk, carbon sequestration, biodiversity and air and water quality and delivers cooling that can save human lives in heatwaves (Wong et al., 2020). Stormwater harvesting is supported by some councils in New Zealand and can deliver recycled water for households (Attwater and Derry, 2017), improving climate resilience and reducing water demand (White et al., 2017). Addressing infrastructure vulnerability is essential given the long lifetime of assets, criticality of services and high costs of maintenance (Chester et al., 2020; Hughes et al., 2021).

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Thamo, T. et al., 2017: Dynamics and the economics of carbon sequestration: common oversights and their implications. Mitig Adapt Strateg Glob Change, 22 (7), 1095–1111, doi:10.1007/s11027-016-9716-x.

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Gonzalez-Sanchez, E.J., et al., 2019: Meta-analysis on carbon sequestration through conservation agriculture in Africa. Soil Tillage Res. , 190, 22–30, doi:10.1016/j.still.2019.02.020.

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Policies supporting sustainable rangeland management and the livelihood strategies of rangeland users have an outsized influence on both development and climate action (Gharibvand et al., 2015). Climate change adaptation, mitigation practices and livestock production can be supported by policies that encourage diversification of livestock animals (within species), support sustainable foraging and feed varieties (Rivera-Ferre et al., 2016) and strengthen institutions such as agricultural support programmes, markets and intra- and inter-regional trade (Zhang et al., 2017). For example, sustainable pastoralism can contribute to mitigation both by increasing carbon sequestration through improved soil management and by reducing methane emissions through changing the mix and distribution of the herd. Likewise sustainable pastoralism can also contribute to adaptation by changing grazing management, introducing alternative livestock breeds, improving pest management and modifying production structures (Joyce et al., 2013). Another example of rangeland adaptation is diversifying the use of rangelands, such as supplementing with payments for ecosystem services, carbon sequestration, tourism or supplementary assistance for all land-based activities (Gharibvand et al., 2015). However, challenges for climate-smart livestock production systems remain due to a lack of information, limited access to technology and insufficient capital (FAO, 2017). Smallholders in cropping and livestock systems in sub-Saharan Africa and South Asia, for example, face obstacles obtaining climate change mitigation and adaptation synergies due to poor access to markets and relevant knowledge, land tenure insecurity and the common property status of most grazing resources (Descheemaeker et al., 2016). Consequently, the appropriateness of these strategies and measures needs to be further evaluated, particularly in terms of their usefulness for the poor and most vulnerable.

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Forest restoration would certainly contribute substantially towards climate-proofing and achievement of several SDGs as well as the Paris Agreement. There is increasing evidence that diverse, native tree species plantations are more likely to be resilient to climate change in contrast to fast-growing monocultures, (Hulvey et al., 2013) often of exotic species. At the same time, other natural ecosystems such as savannas, grasslands, peatlands, wetlands and mangroves have considerable value in acting as carbon sinks as well as providing other ecosystem services such as hydrological regulation, coastal protection, maintaining biodiversity and contributing to human livelihoods especially pastoralists and fishermen (Veldman et al., 2015; Conant et al., 2017; Leifeld and Menichetti, 2018; Seddon et al., 2019). Coastal and marine ecosystems including wetlands and mangroves have featured prominently in studies of NbS in climate adaptation and mitigation potential for ‘blue carbon’ sequestration (Inoue, 2019; Sections 3.6.2.1; 6.3.3; Cross-Chapter Paper 2.3.2.3). Agroecological practices such as agroforestry, intercropping, rotational grazing, organic manuring, and integrating livestock production with cropping etc can also consider as NbS which contribute to both climate mitigation and adaptation (Altieri and Nicholls, 2017; Webb et al., 2017; Bezner Kerr et al., 2019; Leakey, 2020; Box 5.10).

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Abreu, R.C., et al., 2017: The biodiversity cost of carbon sequestration in tropical savanna. Sci. Adv. , 3 (8), e1701284.

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Inoue, T., 2019: Carbon sequestration in mangroves. In: Blue Carbon in Shallow Coastal Ecosystems: Carbon Dynamics, Policy, and Implementation[Kuwae, T. and M. Hori(eds.)]. Springer Singapore, Singapore, pp. 73–99.

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Fuels, including oil, natural gas, hydrogen, biomass and CO2 prior to sequestration are delivered and distributed by pipeline or transportation by road, rail and shipping. In addition to engineering improvements, adaptation measures also include planning and preparation for service disruption by changing transport patterns, increasing local storage capacities and identifying and prioritising protection of critical transport nodes (Wang et al., 2019b; Panahi, Ng and Pang, 2020).

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As noted in Section 7.3.1.6, air pollution projections indicate ambitious emission reduction scenarios or stabilisation of global temperature change at 2°C or below would yield substantial co-benefits for air quality-related health outcomes. Improvements in air quality could be achieved by the deliberate adoption of a range of adaptation options to complement mitigation measures such as decarbonisation (e.g., renewable energy, fuel switching, energy efficiency gains and carbon capture storage and utilisation) and negative emissions technologies (e.g., bioenergy carbon capture and storage, soil carbon sequestration, afforestation and reforestation and wetland construction and restoration).

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Improved ecosystem care and restoration are cost-effective for carbon sequestration while providing multiple environmental, social and economic co-benefits (Griscom et al., 2017; Shukla et al., 2019). Protecting and restoring natural forests and wetlands reduces flood risk across multiple African countries (Bradshaw et al., 2007). In Kenya, enclosures for rangeland regeneration diversified income sources, which could increase the adaptive capacity of local people (Mureithi et al., 2016; Wairore et al., 2016). Sustainable agroforestry in semi-arid regions provides income sources from fuelwood, fruit and timber and reduces exposure to drought, floods and erosion (Quandt et al., 2017). Forest protection in Zimbabwe maintains honey production during droughts, providing food supply options if crops fail (Lunga and Musarurwa, 2016). Community-based natural resource management in pastoral communities improved institutional governance outcomes through involving community members in decision making, increasing the capacity of these communities to respond to climate change (Reid, 2014).

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Yet many areas targeted by AFR100 erroneously mark Africa’s open ecosystems (grasslands, savannas, shrublands) as degraded and suitable for afforestation (Figure Box 9.3.1; (Veldman et al., 2015; Bond et al., 2019) (high confidence). These ecosystems are not degraded, they are ancient ecosystems that evolved in the presence of disturbances (fire/herbivory) (Maurin et al., 2014; Bond and Zaloumis, 2016; Charles-Dominique et al., 2016). Afforestation prioritises carbon sequestration at the cost of biodiversity and other ecosystem services (Veldman et al., 2015; Bond et al., 2019). Furthermore, it remains uncertain how much carbon can be sequestered as, compared to grassy ecosystems, afforestation can reduce belowground carbon stores and increase aboveground carbon loss to fire and drought (Yang et al., 2019; Wigley et al., 2020b; Nuñez et al., 2021). Thus, afforested areas may store less carbon than ecosystems they replace (Dass et al., 2018; Heilmayr et al., 2020). Afforestation would reduce livestock forage, ecotourism potential and water availability (Gray Emma and Bond William, 2013; Anadón et al., 2014; Cao et al., 2016; Stafford et al., 2017; Du et al., 2021), and may reduce albedo thereby increasing warming (Bright et al., 2015; Baldocchi and Penuelas, 2019).

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Social protection has been used for decades, particularly in eastern and southern Africa, to safeguard poor and vulnerable populations from poverty and food insecurity (Niño-Zarazúa et al., 2012). Instruments of social protection include public works programmes, cash transfers, in-kind transfers, social insurance and microinsurance schemes that assist individuals and households to cope during times of crisis and minimise social inequality. Evidence from Ethiopia, Kenya and Uganda indicates national social protection programmes are effective in improving individual and household resilience to climate-related shocks, regardless of whether they aim specifically to address climate risks (Ulrichs et al., 2019). Strengthening social protection and better integrating climate risk management into design of social protection programmes can help build long-term resilience to climate change (Hallegatte et al., 2016; Agrawal et al., 2019). For example, public works programmes can build climate resilience by targeting soil, water and ecosystem conservation and carbon sequestration, such as South Africa’s Working for Water Programme that restores river catchments to reduce fire risk and increase water supplies (Turpie et al., 2008; Norton et al., 2020).

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Wigley, B. J. et al., 2020b: Grasses continue to trump trees at soil carbon sequestration following herbivore exclusion in a semiarid African savanna. Ecology, 101 (5), e03008, doi: https://doi.org/10.1002/ecy.3008.

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Yang, Y., D. Tilman, G. Furey and C. Lehman, 2019: Soil carbon sequestration accelerated by restoration of grassland biodiversity. Nature Communications, 10 (1), 718, doi:10.1038/s41467-019-08636-w.

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European temperate and boreal forests, wetlands and peatlands hold important carbon stocks (Bukvareva and Zamolodchikov, 2016; Yousefpour et al., 2018). Effects of warming and increasing droughts on soil moisture, respiration and carbon sequestration have been detected across European regions (high confidence) (Figure 13.8; Sanginés de Cárcer et al., 2018; Carnicer et al., 2019; Green et al., 2019; Schuldt et al., 2020). Forest expansion in boreal regions results in net warming (Bright et al., 2017), possibly influencing cloud formation and rainfall patterns (medium confidence) (Teuling et al., 2017). These changes are affecting climate, pollination and soil protection services (Figure 13.8; Verhagen et al., 2018). If not managed through increased reforestation and/or revegetation or peatland restoration, future climate-change impacts will progressively limit the climate regulation capacity of European terrestrial ecosystems (medium confidence) (Figure 13.8), especially in SEU (Peñuelas et al., 2018; Xu et al., 2019). Predominantly positive CO2 fertilisation effects at current warming will change into increasingly negative effects of warming and drought on forests at higher temperatures (medium confidence) (Peñuelas et al., 2017; Green et al., 2019; Ito et al., 2020; Wang 2020; Yu et al., 2021). In NEU and EEU, peatlands are projected to shrink with 1.7°C GWL, and become carbon sources at 3°C GWL (Qiu et al., 2020), peat bogs to lose 50% carbon at 2°C GWL, and blanket peatland to shrink or regionally disappear (Gallego-Sala et al., 2010; Ferretto et al., 2019).

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Appropriately implemented ecosystem-based mitigation, such as reforestation with climate-resilient native species (Section 13.3.1.4), peatland and wetland restoration, and agroecology (Section 13.5.2), can enhance carbon sequestration or storage (medium confidence) (Seddon et al., 2020). Salt marsh protection or recreation can increase carbon storage capacity, enhance coastal flood protection and provide cultural services (Beaumont et al., 2014; Bindoff et al., 2019). Trade-offs between ecosystem protection, their services and human adaptation and mitigation needs can generate challenges, such as loss of habitats, increased emissions from restored wetlands (Günther et al., 2020) and conflicts between carbon capture services, and provisioning of bioenergy, food, timber and water (medium confidence) (Lee et al., 2019; Krause et al., 2020).

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Changes to cultivars and sowing dates can reduce yield losses (Figure 13.15) but are insufficient to fully ameliorate losses projected >3°C GWL, with an increase of risk from north to south and for crops growing later in the season such as maize and wheat (high confidence) (Ruiz-Ramos et al., 2018; Feyen et al., 2020). Adaptations for early maturing reduce yield loss by moving the cycle towards a cooler part of year, and also constrains the increases in irrigation water demands, but reduce the period for photosynthesis and grain filling (high confidence) (Ruiz-Ramos et al., 2018; Holzkämper, 2020). Crop breeding for drought and heat tolerance can improve sustainability of agricultural production under future climate (Costa et al., 2019), particularly in SEU where drought-tolerant varieties provide 30% higher yields than drought-sensitive varieties at 3°C GWL (Senapati et al., 2019). Soil management practices, such as crop residue retention or improved crop rotations, generally undertaken as a mitigation option to increase soil carbon sequestration, are not commonly evaluated for adaptation in European agriculture (Hamidov et al., 2018).

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Forest management has been adopted as a frequent strategy to cope with drought, reduce fire risk, and maintain biodiverse landscapes and rural jobs (Hlásny et al., 2014; Fernández-Manjarrés et al., 2018). Successful adaptation strategies include altering the tree species composition to enhance the resilience of European forests (high confidence) (Schelhaas et al., 2015; Zubizarreta-Gerendiain et al., 2017; Pukkala, 2018). Greater diversity of tree species reduces vulnerability to pests and pathogens (Felton et al., 2016), and increases resistance to natural disturbances (high confidence) (Jactel et al., 2017; Pukkala, 2018; Pardos et al., 2021). Depending on forest successional history (Sheil and Bongers, 2020), tree composition change can increase carbon sequestration (high confidence) (Liang et al., 2016), biodiversity and water quality (Felton et al., 2016). Conservation areas can also help climate-change adaptation by keeping the forest cover intact, creating favourable microclimates and protecting biodiversity (low confidence) (Jantke et al., 2016).

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Beaumont, N.J., et al., 2014: The value of carbon sequestration and storage in coastal habitats. Estuar. Coast. Shelf Sci. , 137, 32–40, doi:10.1016/j.ecss.2013.11.022.

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Filipchuk, A., B. Moiseev, N. Malysheva and V. Strakhov, 2018: Russian forests: a new approach to the assessment of carbon stocks and sequestration capacity. Environ. Dev. , 26, 68–75, doi:10.1016/j.envdev.2018.03.002.

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Climate-informed post-fire ecosystem recovery measures (e.g., strategic seeding, planting, natural regeneration), restoration of habitat connectivity and managing for carbon sequestration (e.g., soil conservation through erosion control, preservation of old growth forests, sustainable agroforestry) are critical to maximise long-term adaptation potential and reduces future risk through co-benefits with carbon mitigation (Davis et al., 2019; Hurteau et al., 2019; Coop et al., 2020; Stewart et al., 2021). Innovation in and scaling up the use of prescribed fire and thinning approaches are contributing to pre- and post-fire resilience goals, including use of Indigenous Peoples burning practices that are receiving a new level of awareness (see Box 14.1; Kolden, 2019; Marks-Block et al., 2019; Long et al., 2020b).

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Abbas, F., et al., 2017: Agroforestry: a sustainable environmental practice for carbon sequestration under the climate change scenarios—a review. Environ. Sci. Pollut. Res. , 24 (12), 11177–11191, doi:10.1007/s11356-017-8687-0.

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Buotte, P.C., B.E. Law, W.J. Ripple and L.T. Berner, 2020: Carbon sequestration and biodiversity co-benefits of preserving forests in the western United States. Ecol. Appl, 30 (2), doi:10.1002/eap.2039.

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Livestock production is for small farmers one of the main sources of protein and contributes to food security (Rodríguez et al., 2016). The importance of this sub-sector in CSA will continue to increase as the demand for meat products does as well in the coming years, driven by growing incomes in the region (OECD and FAO, 2019). However, the increase in animal production has been associated with land degradation, triggered by the conversion of native vegetation to pastureland and aggravated by overgrazing and abandoning of the degraded pastures (Baumann et al., 2017; ECLAC, 2018; Müller-Hansen et al., 2019). Sá et al. (2017) simulated the adoption of agricultural systems based on LCA strategies towards 2050. According to the simulation, the adoption of LCA strategies in the SA region can alter the growing trend of land use and land use change emissions, and at the same time, it can increase meat production by 55 Mt for the entire period (2016–2050). The restoration of degraded pasture and livestock intensification account for 71.2% and integrated crop–livestock–forestry system contributes 28.8% of total meat production for the entire period. These results indicate that combined actions in agricultural management systems in SA can result in synergistic responses that can be used to make agriculture and livestock production an important part of the solution of global climate change and advance food security (medium confidence: insufficient evidence and high agreement ) (Zu Ermgassen et al., 2018; Pompeu et al., 2021). Crop–livestock–forestry systems are also important for climate-change adaptation as they provide multiple benefits, including the coproduction of food, animal feed, organic fertilizers and soil organic carbon sequestration (Sharma et al., 2016; Rodríguez et al., 2021), achieving mitigation and adaptation goals (high confidence) (Picasso et al., 2014; Modernel et al., 2016, 2019; Rolla et al., 2019; Locatelli et al., 2020). A recent analysis of agroforestry in Brazil showed positive and relevant impacts on the heads/pasture area rate in livestock production and that the system may have also stimulated a shift towards other production activities with higher gross added value (Gori Maia et al., 2021). Agroforestry has also proven to have protective benefits to obtain more stable, less fluctuating yields due to climate-related damage in coffee production (high confidence) (Bacon et al., 2017; Durand-Bessart et al., 2020; Ovalle-Rivera et al., 2020). In the same way, the production of plant-based fibre can be less vulnerable to economic and climatic variability through farming system diversification. Textile fibre crops for the case of cotton include crop rotation, agroecological intercropping and agroforestry (Oliveira Duarte et al., 2019).

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Climate-smart practices provide a framework to operationalise actions aimed at understanding synergies among productivity, adaptation and mitigation. A significant amount of evidence supports the potential for climate-smart-practice technologies to produce such triple wins as natural pastoral systems in the southern region of SA. Such systems allow for the combination of food production and environmental sustainability. The production of meat based on native grasslands with grazing management that optimises forage allowance can achieve high production levels while providing multiple ecosystem benefits. Optimal forage allowance means offering animals enough forage in order to meet requirements while avoiding overgrazing. This management practice simultaneously increases productivity, reduces GHG emissions while improving soil carbon sequestration and minimises other environmental impacts such as excess of nutrients, fossil-based energy use and biodiversity loss. Pastoral farming systems that manage grazing and feeding efficiently are an example of the integration of food security, environmental conservation and nature-based adaptation to climate change.

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Estrada, G.C.D., M.L.G. Soares, V. Fernadez and P.M.M. de Almeida, 2015: The economic evaluation of carbon storage and sequestration as ecosystem services of mangroves: a case study from southeastern Brazil. Int. J. Biodivers. Sci. Ecosyst. Serv. Manag. , 11 (1), 29–35, doi:10.1080/21513732.2014.963676.

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Kern, J., et al., 2019: What can we learn from ancient fertile anthropic soil (Amazonian Dark Earths, shell mounds, Plaggen soil) for soil carbon sequestration?CATENA, doi:10.1016/j.catena.2018.08.008.

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Mohan, D., et al., 2018: Biochar production and applications in soil fertility and carbon sequestration – a sustainable solution to crop-residue burning in India. RSC Adv. , 8 (1), 508–520, doi:10.1039/c7ra10353k.

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In addition to the temperature classification, each scenario is assigned to one of the following policy categories: (P0) diagnostic scenarios – 99 of 1686 vetted scenarios; (P1) scenarios with no globally coordinated policy (500) and (P1a) no climate mitigation efforts – 124, (P1b) current national mitigation efforts – 59, (P1c) Nationally Determined Contributions (NDCs) – 160, or (P1d) other non-standard assumptions – 153; (P2) globally coordinated climate policies with immediate action (634) and (P2a) without any transfer of emission permits – 435, (P2b) with transfers – 70; or (P2c) with additional policy assumptions – 55; (P3) globally coordinated climate policies with delayed (i.e., from 2030 onwards or after 2030) action (451), preceded by (P3a) no mitigation commitment or current national policies – 7, (P3b) NDCs – 426, (P3c) NDCs and additional policies – 18; (P4) cost-benefit analysis (CBA) – 2. The policy categories were identified using text pattern matching on the scenario metadata and calibrated on the best-known scenarios from model intercomparisons, with further validation against the related literature, reported emission and carbon price trajectories, and exchanges with modellers. If the information available is enough to qualify a policy category number but not sufficient for a subcategory, then only the number is retained (e.g., P2 instead of P2a/b/c). A suffix added after P0 further qualifies a diagnostic scenario as one of the other policy categories. To demonstrate the diversity of the scenarios, the vetted scenarios were classified into different categories along the dimensions of population, GDP, energy, and cumulative emissions (Figure 3.4). The number of scenarios in each category provides some insight into the current literature, but this does not indicate a higher probability of that category occurring in reality. For population, the majority of scenarios are consistent with the SSP2 ‘middle of the road’ category, with very few scenarios exploring the outer extremes. GDP has a slightly larger variation, but overall most scenarios are around the SSP2 socio-economic assumptions. The level of CCS and CDR is expected to change depending on the extent of mitigation, but there remains extensive use of both CDR and CCS in scenarios. CDR is dominated by bioenergy with CCS (BECCS) and sequestration on land, with relatively few scenarios using direct air capture with carbon storage (DACCS) and even less with enhanced weathering (EW) and other technologies (not shown). In terms of energy consumption, final energy has a much smaller range than primary energy as conversion losses are not included in final energy. Both mitigation and reference scenarios are shown, so there is a broad spread in different energy carriers represented in the database. Bioenergy has a number of scenarios at around 100 EJ, representing a constraint used in many model intercomparisons.

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Climate change can impact the potential for AFOLU mitigation action by altering terrestrial carbon uptake, crop yields and bioenergy potential (Chapter 7). Carbon sequestration in forests may be positively or adversely affected by climate change and CO2 fertilisation. On the one hand, elevated CO2 levels and higher temperatures could enhance tree growth rates, carbon sequestration, and timber and biomass production (Beach et al. 2015; Kim et al. 2017; Anderegg et al. 2020). On the other hand, climate change could lead to greater frequency and intensity of disturbance events in forests, such as fires, prolonged droughts, storms, pests and diseases (Kim et al. 2017; Anderegg et al. 2020). The impact of climate change on crop yields could also indirectly impact the availability of land for mitigation and AFOLU emissions (Calvin et al. 2013; Bajželj and Richards 2014; Kyle et al. 2014; Beach et al. 2015; Meijl et al. 2018). The impact is, however, uncertain, as discussed in AR6 WGII Chapter 5. A few studies estimate the effect of climate impacts on AFOLU on mitigation, finding increases in carbon prices or mitigation costs by 1–6% in most scenarios (Calvin et al. 2013; Kyle et al. 2014).

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Mitigation in one sector can also result in additional emissions in another. One example is electrification of end use which can result in increased emissions from energy supply. However, one comparitively well-researched example of this linkage is bioenergy. An increase in demand for bioenergy within the energy system has the potential to influence emissions in the AFOLU sector through the intensification of land and forest management and/or via land-use change (Daioglou et al. 2019; Smith et al. 2019; Smith et al. 2020a; IPCC 2019a). The effect of bioenergy and BECCS on mitigation depends on a variety of factors in modelled pathways. In the energy system, the emissions mitigation depends on the scale of deployment, the conversion technology, and the fuel displaced (Calvin et al. 2021). Limiting or excluding bioenergy and/or BECCS increases mitigation cost and may limit the ability of a model to reach a low warming level (Edmonds et al. 2013; Calvin et al. 2014b; Luderer et al. 2018; Muratori et al. 2020). In AFOLU, bioenergy can increase or decrease terrestrial carbon stocks and carbon sequestration, depending on the scale, biomass feedstock, land management practices, and prior land use (Calvin et al. 2014c; Wise et al. 2015; IPCC 2019a; Smith et al. 2019, 2020a; Calvin et al. 2021).

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Pathways with very high biomass production for energy use typically include very high carbon prices in the energy system (Popp et al. 2017; Rogelj et al. 2018 b), little or no land policy (Calvin et al. 2014b), a high discount rate (Emmerling et al. 2019), and limited non-BECCS CDR options (e.g., afforestation, DACCS) (Chen and Tavoni 2013; Calvin et al. 2014b; Marcucci et al. 2017; Realmonte et al. 2019; Fuhrman et al. 2020). Higher levels of bioenergy consumption are likely to involve trade-offs with mitigation in other sectors, notably in construction (i.e., wood for material and structural products) and AFOLU (carbon stocks and future carbon sequestration), as well as trade-offs with sustainability (Section 3.7) and feasibility concerns (Section 3.8). Not all of these trade-offs are fully represented in all IAMs. Based on sectoral studies, the technical potential for bioenergy, when constraints for food security and environmental considerations are included, are 5–50 EJ yr –1 and 50–250 EJ yr –1 in 2050 for residues and dedicated biomass production systems, respectively (Chapter 7). Bioenergy deployment in IAMs is within the range of these potentials, with between 75 and 248 EJ yr –1 in 2050 in pathways that limit warming to 1.5°C with no or limited overshoot. Finally, IAMs do not include all potential feedstock and management practices, and have limited representation of institutions, governance, and local context (Brown et al. 2019; Butnar et al. 2020; Calvin et al. 2021).

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The inclusion of CDR options, like BECCS, can affect the timing of emissions mitigation in IAM scenarios, that is, delays in mitigations actions are compensated by net negative emissions in the second half of the century. However, studies with limited net negative emissions in the long term require very rapid declines in emissions in the near term (van Vuuren et al. 2017). Especially in forest-based systems, increased harvesting of forests can perturb the carbon balance of forestry systems, increasing emissions for some period; the duration of this period of increased emissions, preceding net emissions reductions, can be very variable (Mitchell et al. 2012; Lamers and Junginger 2013; Röder et al. 2019; Hanssen et al. 2020; Cowie et al. 2021). However, the factors contributing to differences in recovery time are known (Mitchell et al. 2012; Zanchi et al. 2012; Lamers and Junginger 2013; Laganière et al. 2017; Röder et al. 2019). Some studies that consider market-mediated effects find that an increased demand for biomass from forests can provide incentives to maintain existing forests and potentially to expand forest areas, providing additional carbon sequestration as well as additional biomass (Dwivedi et al. 2014; Kim et al. 2018; Baker et al. 2019; Favero et al. 2020). However, these responses are uncertain and likely to vary geographically.

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Some AFOLU mitigation options can enhance vegetation and soil carbon stocks such as reforestation, restoration of degraded ecosystems, protection of ecosystems with high carbon stocks and changes to agricultural land management to increase soil carbon ( high confidence) (Griscom et al. 2017; de Coninck et al. 2018; Fuss et al. 2018; Smith et al. 2019) (AR6 WGIII Chapter 7). The time scales associated with these options indicate that carbon sinks in terrestrial vegetation and soil systems can be maintained or enhanced so as to contribute towards long-term mitigation ( high confidence); however, many AFOLU mitigation options do not continue to sequester carbon indefinitely (Fuss et al. 2018; de Coninck et al. 2018; IPCC 2019a) (AR6 WGIII Chapter 7). In the very long term (the latter part of the century and beyond), it will become more challenging to continue to enhance vegetation and soil carbon stocks, so that the associated carbon sinks could diminish or even become sources ( high confidence) (de Coninck et al. 2018; IPCC 2019a) (AR6 WGI Chapter 5). Sustainable forest management, including harvest and forest regeneration, can help to remediate and slow any decline in the forest carbon sink, for example by restoring degraded forest areas, and so go some way towards addressing the issue of sink saturation (IPCC 2019) (AR6 WGI Chapter 5; and Chapter 7 in this report). The accumulated carbon resulting from mitigation options that enhance carbon sequestration (e.g., reforestation, soil carbon sequestration) is also at risk of future loss due to disturbances (e.g., fire, pests) (Boysen et al. 2017; de Coninck et al. 2018; Fuss et al. 2018; Smith et al. 2019; IPCC 2019a; Anderegg et al. 2020) (AR6 WGI Chapter 5). Maintaining the resultant high vegetation and soil carbon stocks could limit future land-use options, as maintaining these carbon stocks would require retaining the land use and land-cover configuration implemented to achieve the increased stocks.

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Very few studies and pathways include other CDR options (Table 3.5). Pathways with DACCS include potentially large removal from DACCS (up to 37 GtCO2 yr –1 in 2100) in the second half of the century (Chen and Tavoni 2013; Marcucci et al. 2017; Realmonte et al. 2019; Fuhrman et al. 2020, 2021; Shayegh et al. 2021; Akimoto et al. 2021) and reduced cost of mitigation (Bistline and Blanford 2021; Strefler et al. 2021a). At large scales, the use of DACCS has substantial implications for energy use, emissions, land, and water; substituting DACCS for BECCS results in increased energy usage, but reduced land-use change and water withdrawals (Fuhrman et al., 2020, 2021) (Chapter 12.3.2; AR6 WGI Chapter 5). The level of deployment of DACCS is sensitive to the rate at which it can be scaled up, the climate goal or carbon budget, the underlying socio-economic scenario, the availability of other decarbonisation options, the cost of DACCS and other mitigation options, and the strength of carbon-cycle feedbacks (Chen and Tavoni 2013; Fuss et al. 2013; Honegger and Reiner 2018; Realmonte et al. 2019; Fuhrman et al. 2020; Bistline and Blanford 2021; Fuhrman et al. 2021; Strefler et al. 2021a) (AR6 WGI Chapter 5). Since DACCS consumes energy, its effectiveness depends on the type of energy used; the use of fossil fuels would reduce its sequestration efficiency (Creutzig et al. 2019; NASEM 2019; Babacan et al. 2020). Studies with additional CDR options in addition to DACCS (e.g., enhanced weathering, BECCS, afforestation, biochar, and soil carbon sequestration) find that CO2 removal is spread across available options (Holz et al. 2018; Strefler et al. 2021a). Similar to DACCS, the deployment of deep-ocean storage depends on cost and the strength of carbon-cycle feedbacks (Rickels et al. 2018).

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Table 3.5 |Carbon dioxide removal in assessed pathways. Scenarios are grouped by temperature categories, as defined in Section 3.2.4. Quantity indicates the median and 5–95th percentile range of cumulative sequestration from 2020 to 2100 in GtCO2. Count indicates the number of scenarios with positive values for that option. Source: AR6 Scenarios Database.

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Baker, J.S., C.M. Wade, B.L. Sohngen, S. Ohrel, and A.A. Fawcett, 2019: Potential complementarity between forest carbon sequestration incentives and biomass energy expansion. Energy Policy, 126 (August 2018), 391–401, doi:10.1016/j.enpol.2018.10.009.

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Favero, A., A. Daigneault, and B. Sohngen, 2020: Forests: Carbon sequestration, biomass energy, or both?Sci. Adv. , 6(13) , eaay6792, doi:10.1126/sciadv.aay6792.

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Mitchell, S.R., M.E. Harmon, and K.E.B. O’Connell, 2012: Carbon debt and carbon sequestration parity in forest bioenergy production. GCB Bioenergy, 4, 818–827, doi:10.1111/j.1757-1707.2012.01173.x.

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NASEM, 2019: Negative Emissions Technologies and Reliable Sequestration: A Research Agenda. National Academies of Sciences, Engineering, and Medicine, National Academies Press, Washington D.C, USA, 510 pp.

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Overall, the literature shows that pathways considered consistent with below 2°C (>67%) or 1.5°C (Box 4.2) – including inter alia 80% reduction of GHG emissions in 2050 relative to 1990 or 100% renewable electricity scenarios – are technically feasible (Lund and Mathiesen 2009; Mathiesen et al. 2011; Esteban and Portugal-Pereira 2014; Young and Brans 2017; Esteban et al. 2018; Child et al. 2019; Hansen et al. 2019). They entail increased end-use energy efficiency, significant increases in low-carbon energy, electrification, other new and transformative technologies in demand sectors, adoption of carbon capture and sequestration (CCS) to reduce gross emissions, and contribution to net negative emissions through carbon dioxide removal (CDR) and carbon sinks. For these pathways to be realised, the literature assumes higher carbon prices, combined in policy packages with a range of other policy measures.

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A substantial literature detailing trade-offs and synergies between mitigation and adaptation exists and is summarised in the IPCC SR1.5 including energy system transitions; land and ecosystem transitions (including addressing food system efficiency, sustainable agricultural intensification, ecosystem restoration); urban and infrastructure system transitions (including land use planning, transport systems, and improved infrastructure for delivering and using power); industrial system transitions (including energy efficiency, bio-based and circularity, electrification and hydrogen, and industrial carbon capture, utilisation and storage (CCUS); and carbon dioxide removal (including bioenergy with CCS, afforestation and reforestation, soil carbon sequestration, and enhanced weathering) (IPCC 2018: Table 4.SM.5.1). Careful design of policies to shift development pathways towards sustainability can increase synergies and manage trade-offs between mitigation and adaptation (robust evidence, medium agreement).

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Other research gaps concern the open ocean and blue carbon. There is limited knowledge about quantification of the blue carbon stocks. Research is required into what happens if the sequestration capacity of the ocean and marine ecosystems is damaged by climate change to the tipping point until the sink becomes an emitter, and on how to manage blue carbon (Section 4.4.2).

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Fennessy, M.S. et al., 2019: Environmental controls on carbon sequestration, sediment accretion, and elevation change in the Ebro River Delta: Implications for wetland restoration. Estuar. Coast. Shelf Sci. , 222, 32–42, doi:10.1016/J.ECSS.2019.03.023.

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Powlson, D.S., C.M. Stirling, C. Thierfelder, R.P. White, and M.L. Jat, 2016: Does conservation agriculture deliver climate change mitigation through soil carbon sequestration in tropical agro-ecosystems?Agric. Ecosyst. Environ. , 220, 164–174, doi:10.1016/j.agee.2016.01.005.

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Schrobback, P., D. Adamson, and J. Quiggin, 2011: Turning Water into Carbon: Carbon Sequestration and Water Flow in the Murray–Darling Basin. Environ. Resour. Econ. , 49(1) , 23–45, doi:10.1007/s10640-010-9422-1.

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Energy use and its deployment are sovereign matters. State responsibilities over the control and use of natural resources concern both current and future generations (Carney 2016). Climate change impacts will disable the food, water and energy systems of the most vulnerable. Therefore, the resources required to enable a just transition are predicated on good leadership and governance institutions that will support quality and justice-based transitions. Beyond energy systems, changes to land systems can benefit from sustainable land management in ways that will reduce the pressure on land for food and at the same time support carbon storage. With land coming under increased pressure, land and forest management are critical for carbon sequestration, as well as other ecosystem benefits. Extractive processes have impacts on land, and often there are few if any redistributive benefits for communities in regions where extraction takes place. In addition, extraction of strategic minerals such as cobalt, copper and lithium have been linked to violence, human rights abuses and conflict (Cronin et al. 2021).

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Chapter 7 emphasises the high expectations on land to deliver mitigation, yet the pressures on land have grown with population, dietary changes, the impacts of climate change and the conversion of uncultivated land to agriculture and other land uses. Agriculture, forestry and other land uses (AFOLU) are expected to play a vital role in the portfolio of mitigation options across all sectors. The AFOLU sector is also the only one in which it is currently feasible to achieve carbon dioxide removal (CDR) from the atmosphere, including afforestarion/reforestation (A/R), improved forest management and soil carbon sequestration (SCR) (Chapters 7 and 12). The AFOLU sector has a significant mitigation potential, with many scenarios showing a shift to net-negative CO2 emissions during the 21st century. Total cumulative AFOLU CO2 sequestration varies widely across scenarios, with as much as 415 GtCO2 being sequestered between 2010 and 2100 in the most stringent mitigation scenarios. The largest share of net-GHG emissions reductions from AFOLU in both the 1.5°C and 2°C scenarios is from forestry-related measures, such as afforestation, reforestation and reduced deforestation. Afforestation, reforestation and forest management result in substantial CDR in many scenarios. CO2 and CH4 show larger and more rapid declines than N2O, an indication of the difficulties of reducing N2O emissions in agriculture (Chapter 3).

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Loss of biodiversity has been highlighted in several studies as a major trade-off of the low stabilisation scenarios (Prudhomme et al. 2020). A wide range of mitigation and adaptation responses – for example, preserving natural ecosystems such as peatland, coastal lands and forests, reducing the competition for land, fire management, soil management and most risk-management options – have the potential to make positive contributions to sustainable development, ecosystems services and other social goals (McElwee et al. 2020). (Smith et al. 2019 a) also stressed that agricultural practices (e.g., improving yields, agroforestry), forest conservation (e.g., afforestation, reforestation), soil carbon sequestration (e.g., biochar addition to soils) and the removal of carbon dioxide (e.g., BECCS) could contribute to climate change mitigation (Smith et al. 2019 a). However, there are also options that could improve biodiversity if they were implemented jointly with climate change mitigation in AFOLOU. In their study, (Leclère et al. 2020) show that increasing conservation management, restoring degraded land and generalised landscape-level conservation planning could be positive for biodiversity. In general, the ambitious conservation efforts and transformations of food systems are central to an effective post-2020 biodiversity strategy.

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In conclusion, the AFOLU sector offers many low-cost mitigation options, which, however, can also create trade-offs between land use for food, energy, forest and biodiversity. Some options can help to mitigate such trade-offs, like agricultural practices (e.g., improved yields, agroforestry), forest conservation (e.g., afforestation, reforestation), soil carbon sequestration (e.g., biochar addition to soils) and the removal of carbon dioxide (e.g., BECCS), which could contribute to climate change mitigation. Lifestyle changes, including dietary changes and reduced food waste, are tightly embedded in modes of behaviour that are influenced by the immediate environment (e.g., household, farm), the indirect environment (e.g., community) and macro-environmental factors (e.g., political, financial and economic contexts). Achieving zero food waste could reduce the demands for land (SDG 15), water use (SDG 6) and chemical fertilisers (SDG 9), leading to GHG emissions reductions (SDG 13) by encouraging sustainable consumption and production practices (SDG 12).

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As discussed in AR6 WGII, Section 18.4, there are synergies and trade-offs between adaptation and sustainable development, as well as between mitigation and sustainable development, which is supported by comprehensive assessments such as that by Dovie (2019) and Sharifi (2020). Links between mitigation and adaptation options are identified in Chapter 18 in AR6 WGII, such as expected changes in energy demand due to climate change interacting with energy-system development and mitigation options, changes to agricultural production practices to manage the risks of potential changes in weather patterns affecting land-based emissions and mitigation strategies, or mitigation strategies that place additional demands on resources and markets. This increases the pressures on and costs of adaptation or ecosystem restoration linked to carbon sequestration and the benefits in terms of the resilience of natural and managed ecosystems, but it also could restrict mitigation options and increase costs. Chapter 3 of AR6 WGIII similarly concludes that the connectedness and coherence of actions to mitigate climate change could support the conservation and adaptation of ecosystems and meet the Sustainable Development Goals more widely.

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Based on a literature review, (Berry, P et al. 2015) identified water-saving and irrigation techniques in agriculture as attractive adaptation options that have positive synergies with mitigation in increasing soil carbon, reducing energy consumption and reducing CH4 emissions from intermittent rice-paddy irrigation. These measures could, however, reduce water flows in rivers and adversely affect wetlands and biodiversity. The study also concluded that afforestation could reduce peak water flows and increase carbon sequestration, but trade-offs could emerge in relation to the increased demand for water.

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From a cross-sectoral perspective, it can be concluded that the AFOLU sector offers many low-cost mitigation options with synergetic SDG impacts, which, however, can also create trade-offs between land use for food, energy, forest and biodiversity. Some options can help to mitigate such trade-offs, like agricultural practices, forest conservation and soil carbon sequestration. Lifestyle changes, including dietary changes and reduced food waste, could jointly support the SDGs and mitigation. Industry also offers several mitigation options with SDG synergies, for example, related to energy efficiency and the circular economy. Some of the renewable-energy options in industry could indicate some trade-offs in relation to land use, with implications for food- and water security and costs. Cities provide a promising basis for implementing mitigation with SDG synergies, particularly if urban planning, transportation, infrastructure and settlements are coordinated jointly. Similarly, studies of the building sector have identified many synergies between the SDGs and mitigation, but there are issues related to the costs of new technologies. Also, in relation to households and buildings, important equity issues emerge due to the ability of low-income groups to afford the introduction of new technologies. Altogether these cross-sectoral conclusions create a need for policies to address both synergies and trade-offs, as well as for coordination between different sectoral domains. Context-specific assessments of synergies and trade-offs are here important, as is sharing the benefits and costs associated with mitigation policies.

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Privitera, R. and D. La Rosa, 2017: Enhancing carbon sequestration potential of urban green spaces through transfer of development rights strategy. Acta Geobalcanica, 4(1) , 17–23, doi:10.18509/agb.2018.02.

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Schrobback, P., D. Adamson, and J. Quiggin, 2011: Turning Water into Carbon: Carbon Sequestration and Water Flow in the Murray-Darling Basin. Environ. Resour. Econ. , 49(1) , 23–45, doi:10.1007/s10640-010-9422-1.

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Technologies to produce advanced biofuels from lignocellulosic feedstocks have suffered from slow technology development and are still struggling to achieve full commercial scale. Their uptake is likely to require carbon pricing and/or other regulatory measures, such as clean fuel standards in the transport sector or blending mandates. Several commercial-scale advanced biofuels projects are in development in many parts of the world, encompassing a wide selection of technologies and feedstock choices, including carbon capture and sequestration (CCS) that supports carbon dioxide removal. The success of these projects is vital to moving forward the development of advanced biofuels and bringing many of the advanced biofuels value chains closer to the market (IEA 2021b). Finally, biofuel production and distribution supply chains involve notable transport and logistical challenges that need to be overcome (Mawhood et al. 2016; Skeer et al. 2016; IEA 2017a; Puricelli et al. 2021).

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There are widely varying estimates of the capacity of CCU to reduce GHG emissions and meet the net zero objective. According to Hepburn et al. (2019), the estimated potential for the scale of CO2 utilisation in fuels varies widely, from 1 to 4.2 GtCO2 yr –1, reflecting uncertainties in potential market penetration, requiring carbon prices of around USD40 to 80 tCO2–1, increasing over time. The high end represents a future in which synthetic fuels have sizeable market shares, due to cost reductions and policy drivers. The low end – which is itself considerable – represents very modest penetration into the methane and fuels markets, but it could also be an overestimate if CO2-derived products do not become cost competitive with alternative clean energy vectors such as hydrogen or ammonia, or with direct sequestration. Brynolf et al. (2018) indicates that a key cost variable will be the cost of electrolysers for producing hydrogen. Kätelhön et al. (2019) estimate that up to 3.5 GtC yr –1 could be displaced from chemical production by 2030 using CCU, but this would require clean electricity equivalent to 55% of estimated global power production, at the same time other sectors’ demand would also be rising. Mac Dowell et al. (2017) suggest that while CCU, and specifically CO2-based enhanced oil recovery, may be an important economic incentive for early CCS projects (up to 4–8% of required mitigation by 2050), it is unlikely the chemical conversion of CO2 for CCU will account for more than 1% of overall mitigation.

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Kaliyavaradhan, S.K. and T.-C. Ling, 2017: Potential of CO2 sequestration through construction and demolition (C&D) waste – An overview. J. CO2Util. , 20 (June), 234–242, doi:10.1016/j.jcou.2017.05.014.

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Sanchez, D.L., N. Johnson, S.T. McCoy, P.A. Turner, and K.J. Mach, 2018: Near-term deployment of carbon capture and sequestration from biorefineries in the United States. Proc. Natl. Acad. Sci. , 115(19) , 4875–4880, doi:10.1073/pnas.1719695115.

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Urban land areas could triple between 2015 and 2050, with significant implications for future carbon lock-in. There is a large range in the forecasts of urban land expansion across scenarios and models, which highlights an opportunity to shape future urban development towards low- or net-zero GHG emissions and minimise the loss of carbon stocks and sequestration in the agriculture, forestry and other land use (AFOLU) sector due to urban land conversion (medium confidence). By 2050, urban areas could increase up to 211% over the 2015 global urban extent, with the median projected increase ranging from 43% to 106%. While the largest absolute amount of new urban land is forecasted to occur in Asia and Pacific, and in Developed Countries, the highest rate of urban land growth is projected to occur in Africa, Eastern Europe and West-Central Asia, and in the Middle East. The infrastructure that will be constructed concomitant with urban land expansion will lock-in patterns of energy consumption that will persist for decades if not generations. Furthermore, given past trends, the expansion of urban areas is likely to take place on agricultural lands and forests, with implications for the loss of carbon stocks and sequestration. {8.3.1, 8.3.4, 8.4.1, 8.6}

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Urban green and blue infrastructure can mitigate climate change through carbon sequestration, avoided emissions, and reduced energy use while offering multiple co-benefits (robust evidence, high agreement). Urban green and blue infrastructure, including urban forests and street trees, permeable surfaces, and green roofs 3 offer potential to mitigate climate change directly through sequestering and storing carbon, and indirectly by inducing a cooling effect that reduces energy demand and reducing energy use for water treatment. Global urban trees store approximately 7.4 billion tonnes of carbon, and sequester approximately 217 million tonnes of carbon annually, although urban tree carbon storage and sequestration are highly dependent on biome. Among the multiple co-benefits of green and blue infrastructure are reducing the urban heat island (UHI) effect and heat stress, reducing stormwater runoff, improving air quality, and improving mental and physical health of urban dwellers. {8.2, 8.4.4}

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Measures from different sectors that can provide both mitigation and adaptation benefits involve urban planning (Section 8.4.2), buildings (Sections 8.4.3.2 and 8.4.4), energy (Section 8.4.3), green and blue infrastructure (Section 8.4.4), transportation (Section 8.4.2), socio-behavioural aspects (Section 8.4.5), urban governance (Section 8.5), waste (Section 8.4.5.2), and water (Section 8.4.6). In addition to their energy-saving and carbon-sequestration benefits, many measures can also enhance adaptation to climate threats, such as extreme heat, energy shocks, floods, and droughts (Sharifi 2021). Existing evidence is mainly related to urban green infrastructure, urban planning, transportation, and buildings. There has been more emphasis on the potential co-benefits of measures, such as proper levels of density, building energy efficiency, distributed and decentralised energy infrastructure, green roofs and facades, and public/active transport modes. Renewable-based distributed and decentralised energy systems improve resilience to energy shocks and can enhance adaptation to water stress considering the water-energy nexus. By further investment on these measures, planners and decision makers can ensure enhancing achievement of mitigation/adaptation co-benefits at the urban level (Sharifi 2021).

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The initial carbon debt incurred in the production stage, even in sustainable buildings, can take decades to offset through operational stage energy efficiencies alone. Increased reduction in the energy demands and GHG emissions associated with the manufacture of mineral-based construction materials will be challenging, as these industries have already optimised their production processes. Among the category of primary structural materials, it is estimated that final energy demand for steel production can be reduced by nearly 30% compared to 2010 levels, with 12% efficiency improvement for cement (Lechtenböhmer et al. 2016). Even when industries are decarbonised, residual CO2 emissions will remain from associated chemical reactions that take place in calcination and use of coke from coking coal to reduce iron oxide (Davis et al. 2018). Additionally, carbon sequestration by cement occurs over the course of the building lifecycle in quantities that would offset only a fraction of their production stage carbon spike (Xi et al. 2016; Davis et al. 2018). Moreover, there are collateral effects on the carbon cycle related to modern construction and associated resource extraction. The production of cement, asphalt, and glass requires large amounts of sand extracted from beaches, rivers, and seafloors, disturbing aquatic ecosystems and reducing their capacity to absorb atmospheric carbon. The mining of ore can lead to extensive local deforestation and soil degradation (Sonter et al. 2017). Deforestation significantly weakens the converted land as a carbon sink and in severe cases may even create a net emissions source.

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Expansion of agroforestry practices may help to reduce land-use conflicts between forestry and agriculture. Harvesting pressures on forests can be reduced through the reuse and recycling of wooden components from dismantled timber buildings. Potential synergies between the carbon sequestration capacity of forests and the associated carbon storage capacity of dense mid-rise cities built from engineered timber offer the opportunity to construct carbon sinks deployed at the scale of landscapes, sinks that are at least as durable as other buildings (Churkina et al. 2020). Policies and practices promoting design for disassembly and material reuse will increase their durability.

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Global urban tree carbon storage is approximately 7.4 billion tonnes (GtC) given 363 million hectares of urban land, 26.5% tree cover, and an average carbon storage density of urban tree cover of 7.69 kgC m –2 (kilograms carbon per square metre) (Nowak et al. 2013; World Bank et al. 2013). Estimated global annual carbon sequestration by urban trees is approximately 217 million tonnes (MtC) given an average carbon sequestration density per unit urban tree cover of 0.226 kgC m –2 (Nowak et al. 2013). With an average plantable (non-tree and non-impervious) space of 48% globally (Nowak and Greenfield 2020), the carbon storage value could nearly triple if all this space is converted to tree cover. In Europe alone, if 35% of the urban surfaces (26,450 km 2) were transformed into green surfaces, the mitigation potential based on carbon sequestration would be an estimated 25.9 MtCO2 yr −1 with the total mitigation benefit being 55.8 MtCO2 yr −1, including an energy saving of about 92 TWh yr −1 (Quaranta et al. 2021). Other co-benefits include reducing urban runoff by about 17.5% and reducing summer temperatures by 2.5°C–6°C (Quaranta et al. 2021).

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Urban tree carbon storage is highly dependent on biome. For example, carbon sequestered by vegetation in Amazonian forests is two to five times higher compared to boreal and temperate forests (Blais et al. 2005). At the regional level, the estimated carbon storage density rates of tree cover include a range of 3.14–14.1 kgC m –2 in the United States, 3.85–5.58 kgC m –2 in South Korea, 1.53–9.67 kgC m –2 in Barcelona, Spain, 28.1–28.9 kgC m –2 in Leicester, England, and an estimated 6.82 kgC m –2 in Leipzig, Germany and 4.28 kgC m –2 in Hangzhou, China (Nowak et al. 2013). At the local scale, above- and below-ground tree carbon densities can vary substantially, as with carbon in soils and dead woody materials. The conservation of natural mangroves has been shown to provide urban mitigation benefits through carbon sequestration, as demonstrated in the Philippines (Abino et al. 2014). Research on urban carbon densities from the Southern Hemisphere will contribute to better estimates.

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On a per-tree basis, urban trees offer the most potential to mitigate climate change through both carbon sequestration and GHG emissions reduction from reduced energy use in buildings (Nowak et al. 2017). Maximum possible street tree planting among 245 world cities could reduce residential electricity use by about 0.9–4.8% annually (McDonald et al. 2016). Urban forests in the United States reduce building energy use by 7.2%, equating to an emissions reduction of 43.8 MtCO2 annually (Nowak et al. 2017).

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Rapidly growing cities have entry points into an integrated strategy based on spatial planning, urban form and infrastructure (Figure 8.21). For rapidly growing cities that may be co-located and walkable at present, remaining compact is better ensured when co-location and mixed land use, as well as TOD, continues to be prioritised (Section 8.4.2). Concurrently, ensuring that electricity and energy carriers are decarbonised while electrifying mobility, heating and cooling will support the mitigation potential of these cities. Along with an integrated approach across other illustrative strategies, switching to net-zero materials and supply chains holds importance (Section 8.4.3). Cities that remain compact and walkable can provide a greater array of locational and mobility options to the inhabitants that can be adopted for mitigation benefits. Rapidly growing cities that may currently be dispersed and auto-centric can capture high mitigation potential through urban infill and densification. Conserving existing green and blue assets, thereby protecting sources of carbon storage and sequestration, as well as biodiversity, have high potential for both kinds of existing urban form, especially when the rapid growth can be controlled.

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The most impactful urban mitigation plans reduce urban GHG emissions by considering the long lifespan of urban layout and urban infrastructures (Sections 8.4.1 and 8.6). Chapter 8 identifies three overarching mitigation strategies with the largest potential to decrease current, and avoid future, urban emissions: (i) reducing or changing urban energy and material use towards more sustainable production and consumption across all sectors including through spatial planning and infrastructure that supports compact, walkable urban form (Section 8.4.2); (ii) decarbonise through electrification of the urban energy system, and switch to net-zero-emissions resources (i.e., low-carbon infrastructure) (Section 8.4.3); and (iii) enhance carbon sequestration through urban green and blue infrastructure (e.g., green roofs, urban forests and street trees), which can also offer multiple co-benefits like reducing ground temperatures and supporting public health and well-being (Section 8.4.4). Integrating these mitigation strategies across sectors, geographic scales, and levels of governance will yield the greatest emissions savings (Sections 8.4 and 8.5).

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Abino, A.C., J.A.A. Castillo, and Y.J. Lee, 2014: Assessment of species diversity, biomass and carbon sequestration potential of a natural mangrove stand in Samar, the Philippines. Forest Sci. Technol. , 10(1) , 2–8, doi:10.1080/21580103.2013.814593.

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Nowak, D.J., E.J. Greenfield, R.E. Hoehn, and E. Lapoint, 2013: Carbon storage and sequestration by trees in urban and community areas of the United States. Environ. Pollut. , 178, 229–236, doi:10.1016/j.envpol.2013.03.019.

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National Research Council, 2015a: Climate Intervention: Carbon Dioxide Removal and Reliable Sequestration. The National Academies Press, Washington, DC, USA.

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The theoretical global geologic storage potential is about 10,000 GtCO2, with more than 80% of this capacity existing in saline aquifers (medium confidence). Not all the storage capacity is usable because geologic and engineering factors limit the actual storage capacity to an order of magnitude below the theoretical potential, which is still more than the CO2 storage requirement through 2100 to limit temperature change to 1.5°C (Martin-Roberts et al. 2021) ( high confidence). One of the key limiting factors associated with geologic CO2 storage is the global distribution of storage capacity (Table 6.2). Most of the available storage capacity exists in saline aquifers. Capacity in oil and gas reservoirs and coalbed methane fields is limited. Storage potential in the USA alone is >1000 GtCO2, which is more than 10% of the world total (NETL 2015). The Middle East has more than 50% of global enhanced oil recovery potential (Selosse and Ricci 2017). It is likely that oil and gas reservoirs will be developed as geologic sinks before saline aquifers because of existing infrastructure and extensive subsurface data (Alcalde et al. 2019; Hastings and Smith 2020). Notably, not all geologic storage is utilisable. In places with limited geologic storage, international CCS chains are being considered, where sources and sinks of CO2 are located in two or more countries (Sharma and Xu 2021). For economic long-term storage, the desirable conditions are a depth of 800–3000 m, thickness of greater than 50 m and permeability greater than 500 mD (Chadwick et al. 2008; Singh et al. 2021). Even in reservoirs with large storage potential, the rate of injection might be limited by the subsurface pressure of the reservoir (Baik et al. 2018). It is estimated that geologic sequestration is reliable with overall leakage rates at <0.001% yr –1 (Alcalde et al. 2018). In many cases, geological storage resources are not located close to CO2 sources, increasing costs and reducing viability (Garg et al. 2017a).

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It is unlikely that resource constraints will lead to a phase-out of fossil fuels, and instead, such a phase-out would require policy action. Around 80% of coal, 50% of gas, and 20% of oil reserves are likely to remain unextractable under 2°C constraints (McGlade and Ekins 2015; Pellegrini et al. 2020). Reserves are more likely to be utilised in a low-carbon transition if they can be paired with CCS. Availability of CCS technology not only allows continued use of fossil fuels as a capital resource for countries but also paves the way for CDR through BECCS (Haszeldine 2016; Pye et al. 2020). While the theoretical geologic CO2 sequestration potential is vast, there are limits on how much resource base could be utilised based on geologic, engineering, and source-sink mapping criteria (Budinis et al. 2017).

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There is considerable flexibility regarding the overall quantity of liquid and gaseous fuels that will be required in net-zero energy systems ( high confidence) (Figure 6.22 and Section 6.7.4). This will be determined by the relative value of such fuels as compared to systems which rely more or less heavily on zero-emissions electricity. In turn, the share of any fuels that are fossil or fossil-derived is uncertain and will depend on the feasibility of CCS and CDR technologies and long-term sequestration as compared to alternative, carbon-neutral fuels. Moreover, to the extent that physical, biological, and/or socio-political factors limit the availability of CDR (Smith et al. 2015; Field and Mach 2017), carbon management efforts may prioritise residual emissions related to land use and other non-energy sources.

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There are multiple possible strategies to transform the energy system to reach net-zero CO2 emissions and to limit warming to 2°C (>67%) or lower. All pathways rely on the strategies for net-zero CO2 energy systems highlighted in Section 6.6.2, but they vary in the emphasis that they put on different aspects of these strategies and the pace at which they approach net-zero emissions. The pathway that any country or region might follow will depend on a wide variety of factors (Section 6.6.4), including, for example, resource endowments, trade and integration with other countries and regions, carbon sequestration potential, public acceptability of various technologies, climate, the nature of domestic industries, the degree of urbanisation, and the relationship with other societal priorities such as energy access, energy security, air pollution, and economic competitiveness. The Illustrative Mitigation Pathways presented in this box demonstrate four distinct strategies for energy system transformations and how each plays out for a different region, aligned with global strategies that would limit warming to 2.0°C (>67%) or to 1.5°C (>50%). Each pathway represents a very different vision of a net-zero energy system. Yet, all these pathways share the common characteristic of a dramatic system-wide transformation over the coming decades.

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Klapperich, R.J. et al., 2014: The Nexus of Water and CCS: A Regional Carbon Sequestration Partnership Perspective. Energy Procedia, 63, 7162–7172, doi:10.1016/j.egypro.2014.11.752.

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National Academies of Sciences, Engineering, and Medicine, 2019: Negative Emissions Technologies and Reliable Sequestration. The National Academies Press, Washington, DC, USA.

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Sanchez, D.L., N. Johnson, S.T. McCoy, P.A. Turner, and K.J. Mach, 2018: Near-term deployment of carbon capture and sequestration from biorefineries in the United States. Proc. Natl. Acad. Sci. , 115(19) , 4875 LP – 4880, doi:10.1073/pnas.1719695115.

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Woolf, D., J. Lehmann, and D.R. Lee, 2016: Optimal bioenergy power generation for climate change mitigation with or without carbon sequestration. Nat. Commun. , 7, 13160, doi:10.1038/ncomms13160.

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Bioenergy and other bio-based options represent an important share of the total mitigation potential. The range of recent estimates for the technical bioenergy potential when constrained by food security and environmental considerations is5–50and50–250EJ yr–1by 2050 for residues and dedicated biomass production system respectively. These estimates fall within previously estimated ranges (medium agreement). Poorly planned deployment of biomass production and afforestation options for in-forest carbon sequestration may conflict with environmental and social dimensions of sustainability ( high confidence). The global technical CDR potential of BECCS by 2050 (considering only the technical capture of CO2 and storage underground) is estimated at 5.9 mean (0.5–11.3) GtCO2 yr –1, of which 1.6 (0.8–3.5) GtCO2 yr –1 is available at below USD100 tCO2–1 (medium confidence). Bioenergy and other bio-based products provide additional mitigation through the substitution of fossil fuels fossil-based products ( high confidence). These substitution effects are reported in other sectors. Wood used in construction may reduce emissions associated with steel and concrete use. The agriculture and forestry sectors can devise management approaches that enable biomass production and use for energy in conjunction with the production of food and timber, thereby reducing the conversion pressure on natural ecosystems (medium confidence). {7.4}

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AFOLU mitigation or land-based climate change mitigation (used in this chapter interchangeably) are a variety of land management or demand management practices that reduce GHG emissions and/or enhance carbon sequestration within the land system (i.e., in forests, wetlands, grasslands, croplands and pasturelands). If implemented with benefits to human well-being and biodiversity, land-based mitigation measures are often referred to as nature-based solutions and/or natural climate solutions (Glossary). Measures that result in a net removal of GHGs from the atmosphere and storage in either living or dead organic material, or in geological stores, are known as CDR, and in previous IPCC reports were sometimes referred to as greenhouse gas removal (GGR) or negative emissions technologies (NETs) (Rogelj et al. 2018a; Jia et al. 2019). This section evaluates current knowledge and latest scientific literature on AFOLU mitigation measures and potentials, including land-based CDR measures. Section 7.4.1 provides an overview of the approaches for estimating mitigation potential, the co-benefits and risks from land-based mitigation measures, estimated global and regional mitigation potential and associated costs according to literature published over the last decade. Subsequent subsections assess literature on 20 key AFOLU mitigation measures specifically providing:

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Mitigation potentials for AFOLU measures are estimated by calculating the scale of emissions reductions or carbon sequestration against a counterfactual scenario without mitigation activities. The types of mitigation potential estimates in recent literature include: (i) technical potential (the biophysical potential or amount possible with current technologies); (ii) economic potential (constrained by costs, usually by a given carbon price (Table 7.3); (iii) sustainable potential (constrained by environmental safeguards and/or natural resources, e.g., limiting natural forest conversion), and (iv) feasible potential (constrained by environmental, socio-cultural, and/or institutional barriers), however, there are no set definitions used in literature. In addition to types of mitigation estimates, there are two AFOLU mitigation categories often calculated: supply-side measures (land management interventions) and demand-side measures (interventions that require a change in consumer behaviour).

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Table 7.3 | Estimated annual mitigation potential (GtCO2-eq yr–1) in2020–2050of AFOLU mitigation options by carbon price. Estimates reflect sectoral studies based on a comprehensive literature review updating data from (Roe et al. 2019) and integrated assessment models using the IPCC AR6 database (Section 7.5). Values represent the mean, and full range of potential. Sectoral mitigation estimates are averaged for the years 2020–2050 to capture a wider range of literature, and the IAM estimates are given for 2050 as many model assumptions delay most land-based mitigation to mid-century. The sectoral potentials are the sum of global estimates for the individual measures listed for each option. IAM potentials are given for mitigation options with available data; for example, net land-use CO2 for total forests and other ecosystems, and land sequestration from A/R, but not reduced deforestation (protect). Sectoral estimates predominantly use GWP100 IPCC AR5 values (CH4= 28, N2O = 265), although some use GWP100 IPCC AR4 values (CH4= 25, N2O = 298); and the IAMs use GWP100 IPCC AR6 values (CH4= 27, N2O = 273). The sectoral and IAM estimates reflected here do not account for the substitution effects of avoiding fossil fuel emissions nor emissions from other more energy intensive resources/materials. For example, BECCS estimates only consider the carbon dioxide removal (CDR) via geological storage component and not potential mitigation derived from the displacement of fossil fuel use in the energy sector. Mitigation potential from substitution effects are included in the other sectoral chapters like energy, transport, buildings and industry. The total AFOLU sectoral estimate aggregates potential from agriculture, forests and other ecosystems, and diverted agricultural production from avoided food waste and diet shifts (excluding land-use impacts to avoid double counting). Because of potential overlaps between measures, sectoral values from BECCS and the full value chain potential from demand-side measures are not summed with AFOLU. IAMs account for land competition and resource optimisation and can therefore sum across all available categories to derive the total AFOLU potential. Key: ND = no data; Sectoral = as assessed by sectoral literature review; IAM = as assessed by integrated assessment models; EJ = exajoule primary energy.

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IPCC SRCCL (2019). The SRCCL assessed the full range of technical, economic and sustainability mitigation potentials in AFOLU for the period 2030–2050 and identified reduced deforestation and forest degradation to have greatest potential for reducing supply-side emissions (0.4 to 5.8 GtCO2-eq yr –1) ( high confidence) followed by combined agriculture measures, 0.3 to 3.4 GtCO2-eq yr –1 (medium confidence) (Jia et al. 2019). For the demand-side estimates, shifting towards healthy, sustainable diets (0.7 to 8.0 GtCO2-eq yr –1) ( high confidence) had the highest potential, followed by reduced food loss and waste (0.8 to 4.5 GtCO2-eq yr –1) ( high confidence). Measures with greatest potential for CDR were afforestation/reforestation (0.5 to 10.1 GtCO2-eq yr –1) (medium confidence), soil carbon sequestration in croplands and grasslands (0.4 to 8.6 GtCO2-eq yr –1) (medium confidence) and BECCS (0.4 to 11.3 GtCO2-eq yr –1) (medium confidence). The SRCCL did not explore regional potential, associated feasibility nor provide detailed analysis of costs.

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IPCC AR6. This assessment concludes the likely range of global land-based mitigation potential is approximately 8–14 GtCO2-eq yr –1 between 2020–2050 with carbon prices up to USD100 tCO2-eq –1, about half of the technical potential (medium evidence, medium agreement ). About 30–50% could be achieved <USD20 tCO2-eq –1 (Table 7.3). The global economic potential estimates in this assessment are slightly higher than the AR5 range. Since AR5, there have been numerous new global assessments of sectoral land-based mitigation potential (Fuss et al. 2018; Griscom et al. 2017, 2020; Roe et al. 2019; Jia et al. 2019; Griscom et al. 2020; Roe et al. 2021) as well as IAM estimates of mitigation potential (Riahi et al. 2017; Popp et al. 2017; Rogelj et al. 2018a; Frank et al. 2019; Johnston and Radeloff 2019; Baker et al. 2019), expanding the scope of AFOLU mitigation measures included and substantially improving the robustness and spatial resolution of mitigation estimates. A recent development is an assessment of country-level technical and economic (USD100 tCO2-eq –1) mitigation potential for 20 AFOLU measures, including for demand-side and soil organic carbon sequestration in croplands and grasslands, not estimated before (Roe et al. 2021). Estimates on costs, feasibility, sustainability, benefits, and risks have also been developed for some mitigation measures, and they continue to be active areas of research. Developing more refined sustainable potentials at a country-level will be an important next step. Although most mitigation estimates still do not consider the impact of future climate change, there are some emerging studies that do (Sonntag et al. 2016; Doelman et al. 2019). Given the IPCC WG1 finding that the land sink is continuing to increase although its efficiency is decreasing with climate change, it will be critical to better understand how future climate will affect mitigation potentials, particularly from CDR measures.

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Most mitigation options are available and ready to deploy. Emissions reductions can be unlocked relatively quickly, whereas CDR need upfront investment to generate sequestration over time. The protection of natural ecosystems, carbon sequestration in agriculture, sustainable healthy diets and reduced food waste have especially high co-benefits and cost efficiency. Avoiding the conversion of carbon-rich primary peatlands, coastal wetlands and forests is particularly important as most carbon lost from those ecosystems are irrecoverable through restoration by the 2050 timeline of achieving net zero carbon emissions (Goldstein et al. 2020). Sustainable intensification, shifting diets, reducing food waste could enhance efficiencies and reduce agricultural land needs, and are therefore critical for enabling supply-side measures such as reduced deforestation, restoration, as well as reducing N2O and CH4 emissions from agricultural production – as seen in the Illustrative Mitigation Pathway (IMP-SP) (Section 7.5.6). Although agriculture measures that reduce non-CO2, particularly of CH4, are important for near-term emissions reductions, they have less economic potential due to costs. Demand-side measures may be able to deliver non-CO2 emissions reductions more cost efficiently.

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Regionally, economic mitigation potential up to USD100 tCO2-eq –1 is estimated to be greatest in tropical countries in Asia and Pacific (34%), Latin America and the Caribbean (24%), and Africa and the Middle East (18%) because of the large potential from reducing deforestation and sequestering carbon in forests and agriculture (Figure 7.11). However, there is also considerable potential in Developed Countries (18%) and more modest potential in Eastern Europe and West-Central Asia (5%). These results are in line with the IAM regional mitigation potentials (Figure 7.11). The protection of forests and other ecosystems is the dominant source of mitigation potential in tropical regions, whereas carbon sequestration in agricultural land and demand-side measures are important in Developed Countries and Asia and Pacific. The restoration and management of forests and other ecosystems is more geographically distributed, with all regions having significant potential. Regions with large livestock herds (Developed Countries, Latin America) and rice paddy fields (Asia and Pacific) have potential to reduce CH4. As expected, the highest total potential is associated with countries and regions with large land areas, however when considering mitigation density (total potential per hectare), many smaller countries, particularly those with wetlands have disproportionately high levels of mitigation for their size (Roe et al. 2021). As global commodity markets connect regions, AFOLU measures may create synergies and trade-offs across the world, which could make national demand-side measures for example, important in mitigating supply-side emissions elsewhere (Kallio et al. 2018).

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Developments since AR5 and IPCC Special Reports (SR1.5, SROCC and SRCCL). Since SRCCL, additional studies have been published on A/R mitigation potential by Bastin et al. (2019), Lewis et al. (2019), Doelman et al. (2019), Favero et al. (2020) and Austin et al. (2020). These studies are within the range reported in the SRCCL stretching the potentials at the higher range. The rising public interest in nature-based solutions, along with high profile initiatives being launched (UN Decade on Restoration announced in 2019, the Bonn challenge on 150 million ha of restored forest in 2020 and the one trillion trees campaign launched by the World Economic Forum in 2020), has prompted intense discussions on the scale, effectiveness, and pitfalls of A/R and tree planting for climate mitigation (Luyssaert et al. 2018; Bond et al. 2019; Anderegg et al. 2020; Heilmayr et al. 2020; Holl and Brancalion 2020). The sometimes sole attention on afforestation and reforestation suggesting it may solve the climate problem to large extent, in combination with the very high estimates of potentials have led to polarisation in the debate, resulting in criticism to these measures or an emphasis on nature restoration only (Lewis et al. 2019). Our assessment based on most recent literature produced regional economic mitigation potential at USD100 tCO2–1 estimate of 100–400 MtCO2 yr –1 in Africa, 210–266 MtCO2 yr –1 in Asia and Pacific, 291 MtCO2-eq yr –1 in Developed Countries (87% in North America), 30 MtCO2-eq yr –1 in Eastern Europe and West-Central Asia, and 345–898 MtCO2-eq yr –1 in Latin America and Caribbean (Roe et al. 2021), which totals to about 1200 MtCO2 yr –1, leaning to the lower range of the potentials in earlier IPCC reports. A recent global assessment of the aggregate costs for afforestation and reforestation suggests that at USD100 tCO2–1, 1.6 GtCO2 yr –1 could be sequestered globally for an annual cost of USD130 billion (Austin et al. 2020). Sectoral studies that are able to deal with local circumstances and limits estimate A/R potentials at 20 MtCO2 yr –1 in Russia (Eastern Europe and West-Central Asia) (Romanovskaya et al. 2020) and 64 MtCO2 yr –1 in Europe (Nabuurs et al. 2017). (Domke et al. 2020) estimated for the USA an additional 20% sequestration rate from tree planting to achieve full stocking capacity of all understocked productive forestland, in total reaching 187 MtCO2 yr –1 sequestration. A new study on costs in the USA estimates 72–91 MtCO2 yr –1 could be sequestered between now and 2050 for USD100 tCO2–1 (Wade et al. 2019). The tropical and subtropical latitudes are the most effective for forest restoration in terms of carbon sequestration because of the rapid growth and lower albedo of the land surface compared with high latitudes (Lewis et al. 2019). Costs may be higher if albedo is considered in North America, Russia, and Africa (Favero et al. 2017). In addition, a wide variety of sequestration rates have been collected and published in the IPCC Good Practice Guidance for the AFOLU sector (IPCC 2006).

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Generally, measures can consist of one or combination of longer rotations, less intensive harvests, continuous-cover forestry, mixed stands, more adapted species, selected provenances, high quality wood assortments, and so on. Further, there is a trade-off between management in various parts of the forest product value chain, resulting in a wide range of results on the role of managed forests in mitigation (Agostini et al. 2013; Braun et al. 2016; Soimakallio et al. 2016; Gustavsson et al. 2017; Erb et al. 2017; Favero et al. 2020; Hurmekoski et al. 2020). Some studies conclude that reduction in forest carbon stocks due to harvest exceeds for decades the joint sequestration of carbon in harvested wood product stocks and emissions avoided through wood use (Soimakallio et al. 2016; Seppälä et al. 2019), whereas others emphasise country level examples where investments in forest management have led to higher growing stocks while producing more wood (Schulze et al. 2020; Ouden et al. 2020; Cowie et al. 2021).

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Critical assessment and conclusion. There is medium confidence that the global technical mitigation potential for improved forest management by 2050 is 1.7 (1–2.1) GtCO2 yr –1, and the economic mitigation potential (<USD100 tCO2–1) is 1.1 (0.6–1.9) GtCO2 yr –1. Efforts to change forest management do not only require, for example, a carbon price incentive, but especially require knowledge, institutions, skilled labour, good access and so on. These requirements outline that although the potential is of medium size, we estimate a feasible potential towards the lower end. The net effect is also difficult to assess, as management changes impact not only the forest biomass, but also the wood chain and substitution effects. Further, leakage can arise from efforts to change management for carbon sequestration. Efforts, for example to set aside large areas of forest, may be partly counteracted by higher harvesting pressures elsewhere (Kallio et al. 2018). Studies such as (Austin et al. 2020) implicitly account for leakage and thus suggest higher costs than other studies. We therefore judge the mitigation potential at medium potential with medium agreement .

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Activities, co-benefits, risks and implementation opportunities and barriers. Grasslands cover approximately 40.5% of the terrestrial area (i.e., 52.5 million km 2) divided as 13.8% woody savanna and savanna; 12.7% open and closed shrub; 8.3% non-woody grassland; and 5.7% is tundra (White et al. 2000). Sub-Saharan Africa and Asia have the most extensive total area, 14.5 and 8.9 million km 2, respectively. A review by Conant et al. (2017) reported based on data on grassland area (FAO 2013) and grassland soil carbon stocks (Sombroek et al. 1993) a global estimate of about 343 PgC (in the top 1 m), nearly 50% more than is stored in forests worldwide (FAO 2007). Reducing the conversion of grasslands and savannas to croplands prevents soil carbon losses by oxidation, and to a smaller extent, biomass carbon loss due to vegetation clearing (SRCCL, Chapter 6). Restoration of grasslands through enhanced soil carbon sequestration, including (i) management of vegetation, (ii) animal management, and (iii) fire management, was also included in the SRCCL and is covered in Section 7.4.3.1. Similar to other measures that reduce conversion, conserving carbon stocks in grasslands and savannas can be achieved by controlling conversion drivers (e.g., commercial and subsistence agriculture, see Section 7.3) and improving policies and management. In addition to mitigation, conserving grasslands provide various socio-economic, biodiversity, water cycle and other environmental benefits (Claassen et al. 2010; Ryals et al. 2015; Bengtsson et al. 2019). Annual operating costs, and opportunity costs of income foregone by undertaking the activities needed for avoiding conversion of grasslands making costs one of the key barriers for implementation (Lipper et al. 2010).

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Critical assessment and conclusion. There is low confidence that the global technical mitigation potential for reduced grassland and savanna conversion by 2050 is 0.2 (0.1–0.4) GtCO2 yr –1, and the economic mitigation potential (<USD100 tCO2–1) is 0.04 GtCO2 yr –1. Most of the carbon sequestration potential is in below-ground biomass and soil organic matter. However, estimates of potential are still based on few studies and vary according to the levels of soil carbon, and ecosystem productivity (e.g., in response to rainfall distribution). Conservation of grasslands presents significant benefits for desertification control, especially in arid areas (SRCCL, Chapter 3). Policies supporting avoided conversion can help protect at-risk grasslands, reduce GHG emissions, and produce positive outcomes for biodiversity and landowners (Ahlering et al. 2016). In comparison to tropical rainforest regions that have been the primary target for mitigation policies associated to natural ecosystems (e.g., REDD+), conversion grasslands and savannas has received less national and international attention, despite growing evidence of concentrated cropland expansion into these areas with impacts of carbon losses.

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Developments since AR5 and IPCC Special Reports (SR1.5, SROCC and SRCCL). Recent studies emphasise the time frame needed to achieve the full mitigation potential (Duarte et al. 2020; Taillardat et al. 2020). The first project-derived estimate of the net GHG benefit from seagrass restoration found 1.54 tCO2-eq (0.42 MgC) ha –1 yr –110 years after restoration began (Oreska et al. 2020); comparable to the default emission factor in the Wetlands Supplement (Kennedy et al. 2014). Recent studies of rehabilitated mangroves also indicate that annual carbon sequestration rates in biomass and soils can return to natural levels within decades of restoration (Cameron et al. 2019; Sidik et al. 2019). A meta-analysis shows increasing carbon sequestration rates over the first 15 years of mangrove restoration with rates stabilising at 25.7 ± 7.7 tCO2-eq (7.0 ± 2.1 MgC) ha –1 yr –1 through forty years, although success depends on climate, sediment type, and restoration methods (Sasmito et al. 2019). Overall, 30% of mangrove soil carbon stocks and 50–70% of marsh and seagrass carbon stocks are unlikely to recover within 30 years of restoration, underscoring the importance of preventing conversion of coastal wetlands (Goldstein et al. 2020) (Section 7.4.2.8).

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There is high site-specific variation in carbon sequestration rates and uncertainties regarding the response to future climate change (Jennerjahn et al. 2017; Nowicki et al. 2017) (IPCC AR6 WGII Box 3.4). Changes in distributions (Kelleway et al. 2017; Wilson and Lotze 2019), methane release (Al-Haj and Fulweiler 2020), carbonate formation (Saderne et al. 2019), and ecosystem responses to interactive climate stressors are not well-understood (Short et al. 2016; Fitzgerald and Hughes 2019; Lovelock and Reef 2020).

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Critical assessment and conclusion. There is medium confidence that coastal wetland restoration has a technical potential of 0.3 (0.04–0.84) GtCO2-eq yr –1 of which 0.1 (0.05–0.2) GtCO2-eq yr –1 is available up to USD100 tCO2–1. There is high confidence that coastal wetlands, especially mangroves, contain large carbon stocks relative to other ecosystems and medium confidence that restoration will reinstate pre-disturbance carbon sequestration rates. There is low confidence on the response of coastal wetlands to climate change; however, there is high confidence that coastal wetland restoration will provide a suite of valuable co-benefits.

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Activities, co-benefits, risks and implementation opportunities and barriers. Increasing soil organic matter in croplands are agricultural management practices that include (i) crop management: for example, high input carbon practices such as improved crop varieties, crop rotation, use of cover crops, perennial cropping systems (including agroforestry; see Section 7.4.3.3), integrated production systems, crop diversification, agricultural biotechnology; (ii) nutrient management including fertilisation with organic amendments/green manures (Section 7.4.3.6); (iii) reduced tillage intensity and residue retention, (iv) improved water management: including drainage of waterlogged mineral soils and irrigation of crops in arid/semi-arid conditions, (v) improved rice management (Section 7.4.3.5) and (vi) biochar application (P. Smith et al. 2019 a) (Section 7.4.3.2). For increased soil organic matter in grasslands, practices include (i)management of vegetation: including improved grass varieties/sward composition, deep rooting grasses, increased productivity, and nutrient management, (ii)livestock management : including appropriate stocking densities fit to carrying capacity, fodder banks, and fodder diversification, and (iii)fire management : improved use of fire for sustainable grassland management, including fire prevention and improved prescribed burning (Smith et al. 2014, 2019b). All these measures are recognised as Sustainable Soil Management Practices by FAO (Baritz et al. 2018). While there are co-benefits for livelihoods, biodiversity, water provision and food security (P. Smith et al. 2019 a), and impacts on leakage, indirect land-use change and foregone sequestration do not apply (since production in not displaced), the climate benefits of soil carbon sequestration in croplands can be negated if achieved through additional fertiliser inputs (potentially causing increased N2O emissions; (Guenet et al. 2021), and both saturation and permanence are relevant concerns. When considering implementation barriers, soil carbon management in croplands and grasslands is a low-cost option at a high level of technology readiness (it is already widely deployed globally) with low socio-cultural and institutional barriers, but with difficulty in monitoring and verification proving a barrier to implementation (Smith et al. 2020a).

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Conclusions from AR5 and IPCC Special Reports (SR1.5, SROCC and SRCCL); mitigation potential, costs, and pathways. Building on AR5, the SRCCL reported the global mitigation potential for soil carbon management in croplands to be 1.4–2.3 GtCO2-eq yr –1 (Smith et al. 2014), though the full literature range was 0.3–6.8 GtCO2-eq yr –1 (Sommer and Bossio 2014; Powlson et al. 2014; Dickie et al. 2014b; Henderson et al. 2015; Herrero et al. 2016; Paustian et al. 2016; Zomer et al. 2016; Frank et al. 2017; Conant et al. 2017; Griscom et al. 2017; Hawken 2017; Sanderman et al. 2017; Fuss et al. 2018; Roe et al. 2019). The global mitigation potential for soil organic carbon management in grasslands was assessed to be 1.4–1.8 GtCO2-eq yr –1, with the full literature range being 0.1–2.6 GtCO2-eq yr –1 (Herrero et al. 2013; 2016; Conant et al. 2017; Roe et al. 2019). Lower values in the range represented economic potentials, while higher values represented technical potentials – and uncertainty was expressed by reporting the whole range of estimates. The SR1.5 outlined associated costs reported in literature to range from USD –45 to 100 tCO2–1, describing enhanced soil carbon sequestration as a cost-effective measure (IPCC 2018). Despite significant mitigation potential, there is limited inclusion of soil carbon sequestration as a response option within IAM mitigation pathways (Rogelj et al. 2018a).

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Developments since AR5 and IPCC Special Reports (SR1.5, SROCC and SRCCL). No recent literature has been published which conflict with the mitigation potentials reported in the SRCCL. Relevant papers include Lal et al. (2018) which estimated soil carbon sequestration potential to be 0.7–4.1 GtCO2-eq yr –1 for croplands and 1.1–2.9 GtCO2-eq yr –1 for grasslands. Bossio et al. (2020) assessed the contribution of soil carbon sequestration to natural climate solutions and found the potential to be 5.5 GtCO2 yr –1 across all ecosystems, with only small portions of this (0.41 GtCO2-eq yr –1 for cover cropping in croplands; 0.23, 0.15, 0.15 GtCO2-eq yr –1 for avoided grassland conversion, optimal grazing intensity and legumes in pastures, respectively) arising from croplands and grasslands. Regionally, soil carbon management in croplands is feasible anywhere, but effectiveness can be limited in very dry regions (Sanderman et al. 2017). For soil carbon management in grasslands the feasibility is greatest in areas where grasslands have been degraded (e.g., by overgrazing) and soil organic carbon is depleted. For well managed grasslands, soil carbon stocks are already high and the potential for additional carbon storage is low. Roe et al. (2021) estimate the greatest economic (up to USD100 tCO2–1) potential between 2020 and 2050 for croplands to be in Asia and the Pacific (339.7 MtCO2 yr –1) and for grasslands, in Developed Countries (253.6 MtCO2 yr –1).

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Activities, co-benefits, risks and implementation opportunities and barriers. Agroforestry is a set of diverse land management systems that integrate trees and shrubs with crops and/or livestock in space and/or time. Agroforestry accumulates carbon in woody vegetation and soil (Ramachandran Nair et al. 2010) and offers multiple co-benefits such as increased land productivity, diversified livelihoods, reduced soil erosion, improved water quality, and more hospitable regional climates (Ellison et al. 2017; Kuyah et al. 2019; Mbow et al. 2020; Zhu et al. 2020). Incorporation of trees and shrubs in agricultural systems, however, can affect food production, biodiversity, local hydrology and contribute to social inequality (Amadu et al. 2020; Fleischman et al. 2020; Holl and Brancalion 2020). To minimise risks and maximise co-benefits, agroforestry should be implemented as part of support systems that deliver tools, and information to increase farmers’ agency. This may include reforming policies, strengthening extension systems and creating market opportunities that enable adoption (Jamnadass et al. 2020; Sendzimir et al. 2011; P. Smith et al. 2019 a). Consideration of carbon sequestration in the context of food and fuel production, as well as environmental co-benefits at the farm, local, and regional scales can further help support decisions to plant, regenerate and maintain agroforestry systems (Kumar and Nair 2011; Miller et al. 2020). In spite of the advantages, biophysical and socio-economic factors can limit the adoption (Pattanayak et al. 2003). Contextual factors may include, but are not limited to; water availability, soil fertility, seed and germplasm access, land policies and tenure systems affecting farmer agency, access to credit, and to information regarding the optimum species for a given location.

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Developments since AR5 and IPCC Special Reports (SR1.5, SROCC and SRCCL). Updated estimates of agroforestry’s technical mitigation potential and synthesised estimates of carbon sequestration across agroforestry systems have since been published. The most recent global analysis estimates technical potential of 9.4 GtCO2-eq yr –1 (Chapman et al. 2020) of agroforestry on 1.87 and 1.89 billion ha of crop and pasture lands below median carbon content, respectively. This estimate is at least 68% greater than the largest estimate reported in the SRCCL (Hawken 2017) and represents a new conservative upper bound as Chapman et al. (2020) only accounted for above-ground carbon. Considering both above- and below-ground carbon of windbreaks, alley cropping and silvopastoral systems at a more limited areal extent (Griscom et al. 2020), the economic potential of agroforestry was estimated to be only about 0.8 GtCO2-eq yr –1. Variation in estimates primarily result from assumptions on the agroforestry systems including, extent of implementation and estimated carbon sequestration potential when converting to agroforestry.

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These synergies and trade-offs between complexity, multifunctionality and scalability are studied in the CANOPIES (Co-existence of Agriculture and Nature: Optimisation and Planning of Integrated Ecosystem Services) project, a collaboration between Wageningen University (NL), the University of São Paulo and EMBRAPA (both Brazil). Soil and management data are collected on farms of varying complexity to evaluate carbon sequestration and other ecosystem services, economic performance and labour demands.

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The trade-off between complexity and labour demand is less pronounced in EMBRAPA’s integrated crop-livestock-forestry (ICLF) systems, where grains and pasture are planted between widely spaced tree rows. Here, barriers for implementation relate mostly to livestock and grain farmers’ lack of knowledge on forestry management and financing mechanisms 5 (Gil et al. 2015). Additionally, linking these financing mechanisms to carbon sequestration remains a Monitoring, Reporting and Verification challenge (Smith et al. 2020b).

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Activities, co-benefits, risks and implementation opportunities and barriers. Improved crop nutrient management can reduce N2O emissions from cropland soils. Practices include optimising fertiliser application delivery, rates and timing, utilising different fertiliser types (i.e., organic manures, composts and synthetic forms), and using slow or controlled-released fertilisers or nitrification inhibitors (Smith et al. 2014; Griscom et al. 2017; P. Smith et al. 2019 a). In addition to individual practices, integrated nutrient management that combines crop rotations including intercropping, nitrogen biological fixation, reduced tillage, use of cover crops, manure and bio-fertiliser application, soil testing and comprehensive nitrogen management plans, is suggested as central for optimising fertiliser use, enhancing nutrient uptake and potentially reducing N2O emissions (Bationo et al. 2012; Lal et al. 2018; Bolinder et al. 2020; Jensen et al. 2020; Namatsheve et al. 2020). Such practices may generate additional mitigation by indirectly reducing synthetic fertiliser manufacturing requirements and associated emissions, though such mitigation is accounted for in the Industry Sector and not considered in this chapter. Tailored nutrient management approaches, such as 4R nutrient stewardship, are implemented in contrasting farming systems and contexts and supported by best management practices to balance and match nutrient supply with crop requirements, provide greater stability in fertiliser performance and to minimise N2O emissions and nutrient losses from fields and farms (Fixen 2020; Maaz et al. 2021). Co-benefits of improved nutrient management can include enhanced soil quality (notably when manure, crop residues or compost is utilised), carbon sequestration in soils and biomass, soil water holding capacity, adaptation capacity, crop yields, farm incomes, water quality (from reduced nitrate leaching and eutrophication), air quality (from reduced ammonia emissions) and in certain cases, it may facilitate land sparing (Sapkota et al. 2014; Johnston and Bruulsema 2014; Zhang et al. 2017; P. Smith et al. 2019 a; Mbow et al. 2019).

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There is robust evidence and high agreement that agriculture needs to change to facilitate environment conservation while maintaining and where appropriate, increase overall production. The SRCCL identified several farming system approaches, deemed alternative to conventional systems (Olsson et al. 2019; Mbow et al. 2019; L.G. Smith et al. 2019). These may incorporate several of the mitigation measures described in Section 7.4.3, while potentially also delivering environmental co-benefits. This Box assesses evidence specifically on the mitigation capacity of some such system approaches. The approaches are not mutually exclusive, may share similar principles or practices and can be complimentary. In all cases, mitigation may result from either (i) emission reductions or (ii) enhanced carbon sequestration, via combinations of management practices as outlined in Figure 1 within this Box. The approaches will have pros and cons concerning multiple factors, including mitigation, yield and co-benefits, with trade-offs subject to the diverse contexts and ways in which they are implemented.

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There is limited discussion on the mitigation potential of AE (Gliessman 2013; Altieri and Nicholls 2017), but robust evidence that AE can improve system resilience and bring multiple co-benefits (Altieri et al. 2015; Mbow et al. 2019; Aguilera et al. 2020; Tittonell 2020; Wanger et al. 2020) (AR6 WGII Box 5.10). Limited evidence concerning the mitigation capacity of AE at a system level (Saj et al. 2017; Snapp et al. 2021) makes conclusions difficult, yet studies into specific practices that may be incorporated, suggest AE may have mitigation potential (medium confidence) (Section 7.4.3). However, AE, that incorporates management practices used in organic farming (see below), may result in reduced yields, driving compensatory agricultural production elsewhere. Research into GHG mitigation by AE as a system and impacts of wide-scale implementation is required. Despite absence of a universally accepted definition (see Annex I), RA is gaining increasing attention and shares principles of AE. Some descriptions include carbon sequestration as a specific aim (Elevitch et al. 2018). Few studies have assessed mitigation potential of RA at a system level (e.g., Colley et al. 2020). Like AE, it is likely that RA can contribute to mitigation, the extent to which is currently unclear and by its case-specific design, will vary (medium confidence).

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The SRCCL noted both positive and inconclusive results regarding CA and soil carbon, with sustained sequestration dependent on productivity and residue returns (Jia et al. 2019; Mirzabaev et al. 2019; Mbow et al. 2019). Recent research is in broad agreement (Ogle et al. 2019; Corbeels et al. 2020, 2019; Gonzalez-Sanchez et al. 2019; Munkholm et al. 2020) with greatest mitigation potential suggested in dry regions (Sun et al. 2020). Theoretically, CA may facilitate improved nitrogen use efficiency (limited evidence) (Lal 2015; Powlson et al. 2016), though CA appears to have mixed effects on soil N2O emission (Six et al. 2004; Mei et al. 2018). CA is noted for its adaptation benefits, with wide agreement that CA can enhance system resilience to climate related stress, notably in dry regions. There is evidence that CA can contribute to mitigation, but its contribution is depended on multiple factors including climate and residue returns (high confidence).

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OF can be considered a form of AE (Lampkin et al. 2017) though it is discussed separately here as it is guided by specific principles and associated regulations (Annex I). OF is perhaps noted more for potential co-benefits, such as enhanced system resilience and biodiversity promotion, than mitigation. Several studies have reviewed the emissions footprint of organic compared to conventional systems (Mondelaers et al. 2009; Tuomisto et al. 2012; Skinner et al. 2014; Meier et al. 2015; Seufert and Ramankutty 2017; Clark and Tilman 2017; Meemken and Qaim 2018; Bellassen et al. 2021). Acknowledging potential assessment limitations (Meier et al. 2015; van der Werf et al. 2020), evidence suggests organic production to typically generate lower emissions per unit of area, while emissions per unit of product vary and depend on the product ( high agreement , medium evidence). OF has been suggested to increase soil carbon sequestration (Gattinger et al. 2012), though definitive conclusions are challenging (Leifeld et al. 2013). Fewer studies consider impacts of large-scale conversion from conventional to organic production globally. Though context specific (Seufert and Ramankutty 2017), OF is reported to typically generate lower yields (Seufert et al. 2012; De Ponti et al. 2012; Kirchmann 2019; Biernat et al. 2020). Large-scale conversion, without fundamental changes in food systems and diets (Muller et al. 2017; Theurl et al. 2020), may lead to increases in absolute emissions from land-use change, driven by greater land requirements to maintain production (L.G. Smith et al. 2019; Leifeld 2016; Meemken and Qaim 2018).

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In most regions, CH4 and N2O emission are both lower in mitigation pathways that limit warming to 3°C (>50%) or lower (C1–C6) compared to scenarios with <4°C (Popp et al. 2017; Rogelj et al. 2018a). In particular, the reduction of CH4 emissions from enteric fermentation in ASIA and AFRICA is profound. Land-related CO2 emissions, which include emissions from deforestation as well as removals from afforestation, are slightly negative (i.e., AFOLU systems turn into a sink) in <1.5°C, <2°C and <3°C mitigation pathways compared to <4°C scenarios. Carbon sequestration via BECCS is most prominent in ASIA, LAM, AFRICA and OECD90+EU, which are also the regions with the highest bioenergy area.

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For different models and scenarios from the AR6 database, the amount of mitigated emissions is presented together with the respective carbon price (Figure 7.15). To determine mitigation potentials, scenarios are compared to a benchmark scenario which usually assumes business-as-usual trends and no explicit additional mitigation efforts. Scenarios have been excluded, if they do not have an associated benchmark scenario or fail the vetting according to the AR6 scenario database, or if they do not report carbon prices and CO2 emissions from AFOLU. Scenarios with contradicting assumptions (for example, fixing some of the emissions to baseline levels) are excluded. Furthermore, only scenarios with consistent 3 regional and global level results are considered. Mitigation potentials are computed by subtracting scenario specific emissions and sequestration amounts from their respective benchmark scenario values. This difference accounts for the mitigation that can be credited to the carbon price which is applied in a scenario. A few benchmark scenarios, however, apply already low carbon prices. For consistency reasons, a carbon price that is applied in a benchmark scenario is subtracted from the respective scenario specific carbon price. This may generate a bias because low carbon prices tend to have a stronger marginal impact on mitigation than high carbon prices. Scenarios with carbon prices which become negative due to the correction are not considered. The analysis considers all scenarios from the AR6 database which pass the criteria and should be considered as an ensemble of opportunity (Huppmann et al. 2018).

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This approach is close to integrated assessment marginal abatement cost curves (MACCs) as described in the literature (Fujimori et al. 2016; Frank et al. 2018, 2019; Harmsen et al. 2019) in the sense that it incorporates besides the technical mitigation options also structural options, as well as behavioural changes and market feedbacks. Furthermore, indirect emission changes and interactions with other sectors can be highly relevant (Daioglou et al. 2019; Kalt et al. 2020) and are also accounted for in the presented potentials. Hereby, some sequestration efforts can occur in other sectors, while leading to less mitigation in the AFOLU sector. For instance, as an integral part of many scenarios, BECCS deployment will lead to overall emissions reductions, and even provision of CDR as a result of the interplay between three direct components (i) LULUCF emissions/sinks, (ii) reduction of fossil fuel use/emissions, (iii) carbon capture and sequestration. Since the latter two effects can compensate for the LULUCF effect, BECCS deployment in ambitious stabilisation scenarios may lead to reduced sink/increased emissions in LULUCF (Kalt et al. 2020). The same holds for trade-offs between carbon sequestration in forests versus harvested wood products both for enhancing the HWP pool and for material substitution. The strengths of the competition between biomass use and carbon sequestration will depend on the biomass feedstocks considered (Lauri et al. 2019).

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In the individual cases, the accounting of all these effects is dependent on the respective underlying model and its coverage of inter-relations of different sectors and sub-sectors. The presented potentials cover a wide range of models, and additionally, a wide range of background assumptions on macro-economic, technical, and behavioural developments as well as policies, which the models have been fed with. Subsequently, the range of the resulting marginal abatement costs is relatively wide, showing the full range of expected contributions from land-use sector mitigation and sequestration in applied mitigation pathways.

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Land use is at the centre of the interdependencies with other sectors, including energy. Some models see a strong competition between BECCS deployment with its respective demand for biomass, and CO2 mitigation/sequestration potentials in the land sector. Biomass demand may lead to an increase in CO2 emissions from land use despite the application of a carbon price when land-use expansion for dedicated biomass production, such as energy plantations, comes from carbon rich land use/land cover alternatives, or when increased extraction of biomass from existing land uses, such as forest management, leads to reduction of the carbon sink (Daioglou 2019; Luderer et al. 2018) and can explain the high variety of observations in some cases. Overall, the large variety of observations shows a large variety of plausible results, which can go back to different model structures and assumptions, showing a robust range of plausible outcomes (Kriegler et al. 2015).

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Table 7.4 | Estimates of achieved emission offsets or reductions in AFOLU through 2018. Data include CDM, voluntary carbon standards, compliance markets, and reduced deforestation from official UNFCCC reports. Carbon sequestration due to other government policies not included.

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The USA spends USD4 billion yr –1 on conservation programs, or 12% of net farm income (Department of Agriculture 2020). In real terms, this expenditure has remained constant for 15 years, supporting 12 Mha of permanent grass or woodland cover in the Conservation Reserve Program (CRP), which has increased soil carbon sequestration by 3 tCO2 ha –1 yr –1 (Conant et al. 2017; Paustian et al. 2019), as well as other practices that could lower net emissions. Gross GHG emissions from the agricultural sector in the US, however, have increased since 1990 (USEPA 2020) due to reductions in the area of land in the US CRP programme and changes in crop rotations, both of which caused soil carbon stocks to decline (USEPA 2020). When combined with increased non-CO2 gas emissions, the emission intensity of US agriculture increased from 1.5 to 1.7 tCO2 ha –1 between 2005 and 2018 (high confidence).

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To date, there has been significantly less investment in agricultural projects than forestry projects to reduce net carbon emissions (Table 7.4). For example, the economic potential (available up to USD100 tCO2–1) for soil carbon sequestration in croplands is 1.9 (0.4–6.8) GtCO2 yr –1 (Section 7.4.3.1), however, less than 2% of the carbon in Table 7.4 is derived from soil carbon sequestration projects. While reductions in CH4 emissions due to enteric fermentation constitute a large share of potential agricultural mitigation reported in Section 7.4, agricultural CH4 emission reductions so far have been relatively modest compared to forestry sequestration. The protocols to quantify emission reductions in the agricultural sector are available and have been tested, and the main limitation appears to be the lack of available financing or the unwillingness to re-direct current subsidies (medium confidence).

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If biomass energy production expands and shifts to carbon capture and storage (e.g., BECCS) during the century, there could be a significant increase in the area of crop and forestland used for biomass energy production (Sections 7.4 and 7.5). BECCS is not projected to be widely implemented for several decades, but in the meantime, policy efforts to advance land-based measures including reforestation and restoration activities (Strassburg et al. 2020) combined with sustainable management and provision of agricultural and wood products are widely expected to increase the terrestrial pool of carbon (Cross-Working Group Box 3 in Chapter 12). Carbon sequestration policies, sustainable land management (forest and agriculture), and biomass energy policies can be complementary (Favero et al. 2017; Baker et al. 2019). However, if private markets emerge for biomass and BECCS on the scale suggested in the SR1.5, policy efforts must ramp up to substantially value, encourage, and protect terrestrial carbon stocks and ecosystems to avoid outcomes inconsistent with many SDGs ( high confidence).

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Sommer, R. and D. Bossio, 2014: Dynamics and climate change mitigation potential of soil organic carbon sequestration. J. Environ. Manage. , 144, 83–87, doi:10.1016/j.jenvman.2014.05.017.

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Sun, W. et al., 2020: Climate drives global soil carbon sequestration and crop yield changes under conservation agriculture. Glob. Change Biol. , 26(6) , 3325–3335, doi:10.1111/gcb.15001.

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Turner, P.A. et al., 2018b: The global overlap of bioenergy and carbon sequestration potential. Clim. Change, 148(1–2) , 1–10, doi:10.1007/s10584-018-2189-z.

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Woolf, D., J. Lehmann, and D.R. Lee, 2016: Optimal bioenergy power generation for climate change mitigation with or without carbon sequestration. Nat. Commun. , 7(1) , 13160, doi:10.1038/ncomms13160.

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A large number of policies and policy instruments can affect GHG emissions and/or sequestration, whether their primary purpose is climate change mitigation or not. Consequently, consistent with the approach in this chapter, this section adopts a broad interpretation to what is considered mitigation policy. Also, the section recognises the multiplicity of policies that overlap and interact.

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The main gaps in current mitigation policy coverage are non-CO2 emissions and CO2 emissions associated with production of industrial materials and chemical feedstocks, which are connected to broader questions of shifting to cleaner production systems (Bataille et al. 2018a; Davis et al. 2018). Sequestration policies focus mainly on forestry and carbon capture and storage (CCS) with limited support for other carbon dioxide removal and use options (Geden et al. 2019; Vonhedemann et al. 2020).

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Synergies between adaptation and mitigation are included in many of the NDCs submitted to the UNFCCC, as part of overall low-emissions climate-resilient development strategies (UNFCCC Secretariat 2016). a majority of developing countries have agreed to develop National Adaptation Plans (NAPs) in which many initiatives contribute simultaneously to the SDGs (Schipper et al. 2020) as well to mitigation efforts (Hönle et al. 2019; Atteridge et al. 2020). For example, developing countries recognise that adaptation actions in sectors such as agriculture, forestry and land-use management can reduce GHGs. Nevertheless, other more complex trade-offs also exist between bioenergy production or reforestation and the land needed for agricultural adaptation and food security (African Development Bank 2019; Hönle et al. 2019; Nyiwul 2019) (Chapter 7). For some of the Small Islands Development States (SIDS), forestry and coastal management, including mangrove planting, saltmarsh and seagrass are sectors that intertwine both mitigation and adaptation (Duarte et al. 2013; Atteridge et al. 2020). Integrated efforts also occur at the city level, such as the Climate Change Action Plan of Wellington City, which includes enhancing forest sinks to increase carbon sequestration while at the same time protecting biodiversity and reducing groundwater runoff as rainfall increases (Grafakos et al. 2019).

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To fully maximise their potential co-benefits and trade-offs of integrating adaptation and mitigation, these should be explicitly sought, rather than accidentally discovered (Spencer et al. 2017; Berry et al. 2015), and policies designed to account for both (robust evidence, high agreement ) (Caetano et al. 2020). For example, the REDD+ initiative focus on mitigation by carbon sequestration was set up to provide co-benefits such as: nature protection, political inclusion, monetary income, economic opportunities. However, some unintended trade-offs may have occurred such as physical displacement, loss of livelihoods, increased human–wildlife conflicts, property claims, food security concerns, and an unequal distribution of benefits to local population groups (Bushley 2014; Duguma et al. 2014a; Gebara et al. 2014; Kongsager and Corbera 2015; Anderson et al. 2016; Di Gregorio et al. 2016, 2017). Ultimately, ecosystem (or nature-based) strategies, such as the use of wetlands to create accessible recreational areas that improve public health while improving biodiversity, sinking carbon and protecting neighbourhoods from extreme flooding events, may lead to more efficient and cost-effective policies (Klein et al. 2005; Locatelli et al. 2011; Kongsager et al. 2016; Mills‐Novoa and Liverman 2019).

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Mason, C.F. and A.J. Plantinga, 2013: The additionality problem with offsets: Optimal contracts for carbon sequestration in forests. J. Environ. Econ. Manage. , 66(1) , 1–14, doi:10.1016/j.jeem.2013.02.003.

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Carbon dioxide removal (CDR) refers to a cluster of technologies, practices, and approaches that remove and sequester carbon dioxide from the ocean and atmosphere and durably store it in geological, terrestrial, or ocean reservoirs, or in products (Table 12.6). In contrast to SRM, CDR does not necessarily impose transboundary risks, except insofar as misleading accounting of its use and deployment could give a false picture of countries’ overall mitigation efforts. CDR is clearly a form of climate change mitigation, and as described in Chapter 12 is needed to counterbalance residual GHG emissions that may prove hard to abate (e.g., from industry, aviation or agriculture) in the context of reaching net zero emissions both globally – in the context of Article 4 of the Paris Agreement – and nationally. CDR could also later be used for reducing atmospheric CO2 concentrations by providing net negative emissions at the global level (Fuglestvedt et al. 2018; Bellamy and Geden 2019). Despite the common feature of removing carbon dioxide, technologies like afforestation/reforestation, soil carbon sequestration, bioenergy with carbon capture and storage, direct air capture with carbon storage, enhanced weathering, ocean alkalinity enhancement or ocean fertilisation are very different, as are the governance challenges. Chapter 12 highlights the sustainable development risks associated with land and water use that are connected to the biological approaches to CDR. As a public good which largely lacks incentives to be pursued as a business case, most types of CDR require a suite of dedicated policy instruments that address both near-term needs as well as long-term continuity at scale (Honegger et al. 2021b).

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CDR methods other than afforestation/reforestation and soil carbon sequestration have only played a minor role in UNFCCC negotiations so far (Fridahl 2017; Rumpel et al. 2020). To accelerate, and indeed better manage CDR globally, stringent rules and practices regarding emissions accounting, measuring, reporting and verifying and project-based market mechanisms have been proposed (Honegger and Reiner 2018; Mace et al. 2018). Given their historic responsibility, it can be expected that developed countries would carry the main burden of researching, developing, demonstrating and deploying CDR, or finance such projects in other countries (Fyson et al. 2020; Pozo et al. 2020). McLaren et al. (2019) suggest that there is a rationale for separating the international commitments for net negative emissions from those for emissions reductions.

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In 2010, the Parties to the CBD adopted the Strategic Plan for Biodiversity 2011–2020 which included 20 targets known as the Aichi Biodiversity targets (Marques et al. 2014). Of relevance to the forestry sector, Aichi Target 15 sets the goal of enhancing ecosystem resilience and the contribution of biodiversity to carbon stocks though conservation and restoration, including ‘restoration of at least 15% of degraded ecosystems’ (UNCBD 2010). The plan elaborates milestones, including the development of national plans for potential restoration levels and contributions to biodiversity protection, carbon sequestration, and climate adaptation to be integrated into other national strategies, including REDD+. In 2020, however, the CBD found that while progress was evident for the majority of the Aichi Biodiversity Targets, it was not sufficient for the achievement of the targets by 2020 (CBD 2020).

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In the same vein, in 2010 FAO delivered the Framework for Assessing and Monitoring Forest Governance. The Framework draws on several approaches currently in use or under development in major forest governance-related processes and initiatives, including the World Bank’s Framework for Forest Governance Reform. The Framework builds on the understanding that governance is both the context and the product of the interaction of a range of actors and stakeholders with diverse interests (FAO 2010). For example, UNFCCC and the UN-REDD programme focus on REDD+ and UNEP focuses on The Economics of Ecosystems and Biodiversity (TEEB), institutional mechanisms that have been conceptualised as a ‘win-win-win’ for mitigating climate, protecting biodiversity and conserving indigenous culture by institutionalising payments on carbon sequestration and biodiversity conservation values of ecosystems services from global to local communities. These mechanisms include public-private partnership, and NGO participation. REDD+ and TEEB allocation policies will be interventions in a highly complex system, and will inevitably involve trade-offs; therefore, it is important to question the ‘win-win-win’ discourse (Zia and Kauffman 2018; Goulder et al. 2019). The initial investment and the longer periods of recovery of investment are sometimes barriers to private investment. In this sense, it is important to have government incentives and encourage public-private investment (Ivanova and Lopez 2013).

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Rumpel, C. et al., 2020: The 4p1000 initiative: Opportunities, limitations and challenges for implementing soil organic carbon sequestration as a sustainable development strategy. Ambio, 49(1) pp. 350–360, doi:10.1007/s13280-019-01165-2.

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Despite limitedcurrent deployment, moderate to large future mitigation potentials are estimated for direct air carbon capture and sequestration (DACCS), enhanced weathering (EW) and ocean-based CDR methods (including ocean alkalinity enhancement and ocean fertilisation) (medium evidence, medium agreement). The potential for DACCS (5–40 GtCO2 yr –1) is limited mainly by requirements for low-carbon energy and by cost (USD100–300 (full range: USD84–386) tCO2–1). DACCS is currently at a medium technology readiness level. EW has the potential to remove 2–4 (full range: <1 to about 100) GtCO2 yr –1, at costs ranging from USD50 to 200 (full range: USD24–578) tCO2–1. Ocean-based methods have a combined potential to remove 1–100 GtCO2 yr –1 at costs of USD40–500 tCO2–1, but their feasibility is uncertain due to possible side effects on the marine environment. EW and ocean-based methods are currently at a low technology readiness level. {12.3}

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Despite these uncertainties, clearly a number of options with high potentials can be identified, including solar energy, wind energy, reducing conversion of forests and other natural ecosystems, and restoration of forests and other natural ecosystems. As mid-range values, they each represent 4 to 7% of total reference emissions for 2030. Soil carbon sequestration in agriculture and fuel switching in industry can also be considered as options with high potential, although it should be noted that these options consist of a number of discernible sub-options, see Table 12.3. It can be observed that for each sector, a variety of options is available. Many of the smaller options each make up 1 to 2% of the reference emissions for 2030. Within this group of smaller options there are some categories that, summed together, stand out as substantial: the energy efficiency options and the methane mitigations options.

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For the AFOLU sector, the sectoral studies provide net emissions reduction potentials comparable with projections from the IAMs at costs levels up to USD50 tCO2-eq –1. However, beyond that level the mitigation potential found in the sectoral analysis is larger than in the IAMs. For agriculture, it can be explained by the fact that carbon sequestration options, like soil carbon, biochar and agroforestry, have little to no representation in IAMs. Similarly, for forestry and other land use-related options, the protection and restoration of other ecosystems than forests (peatland, coastal wetlands and savannas) are not represented in IAMs. Also note that some IAM baselines already have small carbon prices, which induce land-based mitigation, while in others, mitigation, particularly from reduced deforestation, is part of the storyline even without an implemented carbon price. Both of these effects dampen the mitigation potential available in the USD100 tCO2-eq –1 carbon price scenario from IAMs. Furthermore, estimates of mitigation through forestry and other land use-related options from the AR6 IAM scenario database represent the net emissions from A/R and deforestation, thus are likely to be lower than the sectoral estimates of A/R potential expressed as gross removals.

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A number of CDR methods (e.g., afforestation/reforestation (A/R), bioenergy with carbon capture and storage (BECCS), soil carbon sequestration (SCS), biochar, wetland/peatland restoration and coastal restoration) are dealt with elsewhere in this report (Chapters 6 and 7). These methods are synthesised in Section 12.3.2. Others, not dealt with elsewhere, – direct air carbon capture and storage (DACCS), enhanced weathering (EW) of minerals and ocean-based approaches including ocean fertilisation (OF) and ocean alkalinity enhancement (OAE) – are discussed in Sections 12.3.1.1 to 12.3.1.3 below (see also IPCC 2019b and AR6 WGI, Section 5.6). Some methods, such as BECCS and DACCS, involve carbon storage in geological formations, which is discussed in Chapter 6. The climate system and the carbon cycle responses to CDR deployment and each method’s physical and biogeochemical characteristics such as storage form and duration are assessed in Chapters 4 and 5 of the AR6 WGI report.

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Risks and impacts: DACCS requires a considerable amount of energy ( high confidence), depending on the type of technology, water, and make-up sorbents, while its land footprint is small compared to other CDR methods (Smith et al. 2016). Yet, depending on the source of energy for DACCS (e.g., renewables vs nuclear), DACCS could require a significant land footprint (NASEM 2019; Sekera and Lichtenberger 2020). The theoretical minimum energy requirement for separating CO2 gas from the air is about 0.5 GJ tCO2–1 (Socolow et al. 2011). Fasihi et al. (2019) reviewed the published estimates of energy requirements and found that for the current technologies, the total energy requirement is about 4–10 GJ tCO2–1, with heat accounting for about 80% and electricity about 20% (McQueen et al. 2021). At a 10 GtCO2 yr –1 sequestration scale, this would translate into 40–100 exajoules (EJ) yr –1 of energy consumption (32–80 EJ yr –1 for heat and 8–20 EJ yr –1 electricity), which can be contrasted with the current primary energy supply of about 600 EJ yr –1 and electricity generation of about 100 EJ yr –1. For the solid sorbent technology, low-temperature heat could be sourced from heat pumps powered by low-carbon sources such as renewables (Breyer et al. 2020), waste heat (Beuttler et al. 2019), and nuclear energy (Sandalow et al. 2018). Unless sourced from a clean source, this amount of energy could cause environmental damage (Jacobson 2019). Because DACCS is an open system, water lost from evaporation must be replenished. Water loss varies, depending on technology (including adjustable factors such as the concentration of the liquid solvent) as well as environmental conditions (e.g., temperate vs tropical climates). For a liquid solvent system, it can be 0–50 tH2O tCO2–1 (Fasihi et al. 2019). A water loss rate of about 1–10 tH2O tCO2–1 (Socolow et al. 2011) would translate into about 10–100 GtH2O (10–100 km 3) to capture 10 GtCO2 from the atmosphere. Some solid sorbent technologies actually produce water as a by-product, for example 0.8–2 tH2O tCO2–1 for a solid-sorbent technology with heat regeneration (Beuttler et al. 2019; Fasihi et al. 2019). Large-scale deployment of DACCS would also require a significant quantity of materials, and energy to produce them (Chatterjee and Huang 2020). Hydroxide solutions are currently being produced as a by-product of chlorine but replacement (make-up) requirement of such materials at scale exceeds the current market supply (Realmonte et al. 2019). The land requirements for DAC units are not large enough to be of concern (Madhu et al. 2021). Furthermore, these can be placed on unproductive lands, in contrast to biological CDR. Nevertheless, to ensure that CO2-depleted air does not enter the air contactor of an adjacent DAC system, there must be enough space between DAC units, similar to wind power turbines. Considering this, Socolow et al. (2011) estimated a land footprint of 1.5 km 2MtCO2–1. In contrast, large energy requirements can lead to significant footprints if low-density energy sources (e.g., solar PV) are used (Smith et al. 2016). For the issues associated with CO2 utilisation and storage, see Chapter 6.

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Potentials: In a systematic review of the costs and potentials of enhanced weathering, Fuss et al. (2018) report a wide range of potentials (limited evidence, low agreement ). The highest reported regional sequestration potential, 88.1 GtCO2 yr −1, is reported for the spreading of pulverised rock over a very large land area in the tropics, a region considered promising given the higher temperatures and greater rainfall (Taylor et al. 2016). Considering cropland areas only, the potential carbon removal was estimated by Strefler et al. (2018) to be 95 GtCO2 yr −1 for dunite and 4.9 GtCO2 yr −1 for basalt. Slightly lower potentials were estimated by Lenton (2014) where the potential of carbon removal by enhanced weathering (including adding carbonate and olivine to both oceans and soils) was estimated to be 3.7 GtCO2 yr –1 by 2100, but with mean annual removal an order of magnitude less at 0.2 GtC-eq yr –1 (Lenton 2014). The estimates reported in Smith et al. (2016) are based on the potential estimates of Lenton (2014). Beerling et al. (2020) estimate that up to 2 GtCO2 yr –1 could be removed by 2050 by spreading basalt onto 35–59% (weighted mean 53%) of agricultural land of 12 countries. Fuss et al. (2018) provide an author judgement range for potential of 2–4 GtCO2 yr −1 for 2050.

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Costs: Ocean fertilisation costs depend on nutrient production and its delivery to the application area (Jones 2014). The costs range from USD2 tCO2–1 for fertilisation with iron (Boyd 2008) to USD457 tCO2–1 for nitrate (Harrison 2013). Reported costs for macronutrient application at USD20 tCO2–1 (Jones 2014) contrast with higher estimates by (Harrison 2013) reporting that low costs are due to overestimation of sequestration capacity and underestimation of logistical costs. The median of OF cost estimates, USD230 tCO2–1 (Gattuso et al., 2021) indicates low cost-effectiveness, albeit uncertainties are large.

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Potentials: Theoretical calculations indicate that organic carbon export increases 2–20 kg per gram of iron added, but experiments indicate much lower efficiency: a significant part of the CO2 can be emitted back the atmosphere because much of the organic carbon produced is remineralised in the upper ocean. Efficiency also varies with location (Bopp et al. 2013). Between studies, there are substantial differences in the ratio of iron added to carbon fixed photosynthetically, and in the ratio of iron added to carbon eventually sequestered (Trull et al. 2015), which has implications both for the success of this strategy and its cost. Estimates indicate potentially achievable net sequestration rates of 1–3 GtCO2 yr –1 for iron fertilisation, translating into cumulative CDR of 100–300 GtCO2 by 2100 (Ryaboshapko and Revokatova 2015; Minx et al. 2018), whereas OF with macronutrients has a higher theoretical potential of 5.5 GtCO2 yr –1 (Harrison 2017; Gattuso et al. 2021). Modelling studies show a maximum effect on atmospheric CO2 of 15–45 parts per million volume in 2100 (Zeebe and Archer 2005; Aumont and Bopp 2006; Keller et al. 2014; Gattuso et al. 2021).

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CDR through ‘ocean alkalinity enhancement’ or ‘artificial ocean alkalinisation’ (Renforth and Henderson 2017) can be based on: (i) the dissolution of natural alkaline minerals that are added directly to the ocean or coastal environments; (ii) the dissolution of such minerals upstream from the ocean (e.g., enhanced weathering, Section 12.3.1.2); (iii) the addition of synthetic alkaline materials directly to the ocean or upstream; and (iv) electrochemical processing of seawater. In the case of (ii), minerals are dissolved on land and the dissolution products are conveyed to the ocean through runoff and river flow. These processes result in chemical transformation of CO2 and sequestration as bicarbonate and carbonate ions (HCO3, CO32–) in the ocean. Imbalances between the input and removal fluxes of alkalinity can result in changes in global oceanic alkalinity and therefore the capacity of the ocean to store carbon. Such alkalinity-induced changes in partitioning of carbon between atmosphere and ocean are thought to play an important role in controlling climate change on timescales of 1000 years and longer (e.g., Zeebe 2012). The residence time of dissolved inorganic carbon in the deep ocean is around 100,000 years. However, residence time may decrease if alkalinity is reduced by a net increase in carbonate minerals by either increased formation (precipitation) or reduced dissolution of carbonate (Renforth and Henderson 2017). The alkalinity of seawater could potentially also be increased by electrochemical methods, either directly by reactions at the cathode that increase the alkalinity of the surrounding solution that can be discharged into the ocean, or by forcing the precipitation of solid alkaline materials (e.g., hydroxide minerals) that can then be added to the ocean (e.g., Rau et al. 2013; La Plante et al. 2021).

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Potentials: For OAE, the ocean theoretically has the capacity to store thousands of GtCO2 (cumulatively) without exceeding pre-industrial levels of carbonate saturation (Renforth and Henderson 2017) if the impacts were distributed evenly across the surface ocean. The potential of increasing ocean alkalinity may be constrained by the capability to extract, process, and react minerals (Section 12.3.1.2); the demand for co-benefits (see below), or to minimise impacts around points of addition. Important challenges with respect to the detailed quantification of the CO2 sequestration efficiency include nonstoichiometric dissolution, reversed weathering and potential pore water saturation in the case of adding minerals to shallow coastal environments (Meysman and Montserrat 2017). Fuss et al. (2018) suggest storage potentials of 1–100 GtCO2 yr –1. (González and Ilyina 2016) suggested that addition of 114 picomoles of alkalinity to the surface ocean could remove 3400 GtCO2 from the atmosphere.

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The term ‘blue carbon’ was used originally to refer to biological carbon sequestration in all marine ecosystems, but it is increasingly applied to CDR associated with rooted vegetation in the coastal zone, such as tidal marshes, mangroves and seagrasses. Potential for carbon sequestration in other coastal and non-coastal ecosystems, such as macroalgae (e.g., kelp), is debated (Krause-Jensen and Duarte, 2016; Krause-Jensen et al., 2018). In this report, blue carbon refers to CDR through coastal blue carbon management.

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Status: In recent years, there has been increasing research on the potential, effectiveness, risks, and possibility of enhancing CO2 sequestration in shallow coastal ecosystems (Duarte, 2017). About 20% of the countries that are signatories to the Paris Agreement refer to blue carbon approaches for climate change mitigation in their NDCs and are moving toward measuring blue carbon in inventories. About 40% of those same countries have pledged to manage shallow coastal ecosystems for climate change adaptation (Kuwae and Hori 2019).

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Potentials: Globally, the total potential carbon sequestration rate through blue carbon CDR is estimated in the range 0.02–0.08 GtCO2 yr –1 (Wilcox et al. 2017; National Academies of Sciences 2019). Gattuso et al. (2021) estimate the theoretical cumulative potential of coastal blue carbon management by 2100 to be 95 GtCO2, taking into account the maximum area that can be occupied by these habitats and historic losses of mangroves, seagrass and salt marsh ecosystems.

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Risks and impacts: For blue carbon management, potential risks relate to the high sensitivity of coastal ecosystems to external impacts associated with both degradation and attempts to increase carbon sequestration. Under expected future warming, sea level rise and changes in coastal management, blue carbon ecosystems are at risk, and their stored carbon is at risk of being lost (Bindoff et al. 2019).

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Co-benefits: Blue carbon management provides many non-climatic benefits and can contribute to ecosystem-based adaptation, also reducing emissions associated with habitat degradation and loss (Howard et al. 2017; Hamilton and Friess 2018). Shallow coastal ecosystems have been severely affected by human activity; significant areas have already been deforested or degraded and continue to be denuded. These processes are accompanied by carbon emissions. The conservation and restoration of coastal ecosystems, which will lead to increased carbon sequestration, is also essential for the preservation of basic ecosystem services, and healthy ecosystems tend to be more resilient to the effects of climate change.

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Status: BECCS, afforestation/reforestation (A/R), soil carbon sequestration (SCS) and biochar are land-based biological CDR methods (Smith et al. 2016). BECCS combines biomass use for energy with CCS to capture and store the biogenic carbon geologically (Section 6.4.2.6); A/R and SCS involve fixing atmospheric carbon in biomass and soils, and biochar involves converting biomass to biochar and using it as a soil amendment. These CDR methods can be associated with both co-benefits and adverse side effects (Smith et al. 2016; Hurlbert et al. 2019; Mbow et al. 2019; Olsson et al. 2019; Schleicher et al. 2019; Smith et al. 2019b; Babin et al. 2021; Dooley et al. 2021) (Sections 7.4 and 12.5).

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Trade-offs and spillovers: Some land-based biological CDR methods, such as BECCS and A/R, demand land. Combining mitigation strategies has the potential to increase overall carbon sequestration rates (Humpenöder et al. 2014). However, these CDR methods may also compete for resources (Frank et al. 2017). Land-based mitigation approaches currently propose the use of forests (i) as a source of woody biomass for bioenergy and various biomaterials and (ii) for carbon sequestration in vegetation, soils, and forest products. Forests are therefore required to provide both provisioning (biomass feedstock) and regulating (carbon sequestration) ecosystem services. This multifaceted strategy has the potential to result in trade-offs (Makkonen et al. 2015). Some land-based mitigation options could conflict with biodiversity goals, e.g., A/R using monoculture plantations can reduce species richness when introduced into (semi-)natural grasslands (Smith et al. 2019a; Dooley et al. 2021). When trade-offs exist between biodiversity protection and mitigation objectives, biodiversity is typically given a lower priority, especially if the mitigation option is considered risk-free and economically feasible (Pörtner et al. 2021). Approaches that promote synergies, such as sustainable forest management, reducing deforestation rates, cultivation of perennial crops for bioenergy in sustainable farming practices, and mixed-species forests in A/R, can mitigate biodiversity impacts and even improve ecosystem capacity to support biodiversity while mitigating climate change (Pörtner et al. 2021) (Section 12.5). Systematic land-use planning could help to deliver land-based mitigation options that also limit trade-offs with biodiversity (Longva et al. 2017) (Cross-Working Group Box 3: Mitigation and Adaptation via the Bioeconomy, in this chapter).

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GHG emissions from food systems can be reduced by targeting direct or indirect GHG emissions in the supply chain including enhanced carbon sequestration, by introducing sustainable production methods such as agroecological approaches which can reduce system-level GHG emissions of conventional food production and also enhance resilience (HLPE 2019), by substituting food products with high GHG intensities with others of lower GHG intensities, by reducing food over-consumption, and/or by reducing food loss and waste. The substitution of food products with others that are more sustainable and/or healthier is often called ‘dietary shift’.

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Global integrated assessment models (IAMs) provide insights into the roles of land-based mitigation in pathways limiting warming to 1.5°C or 2°C; interaction between land-based and other mitigation options such as wind and solar power; influence of land-based mitigation on food markets, land use and land carbon; and the role of BECCS vis-à-vis other CDR options (Chapter 3). However, IAMs do not capture more subtle changes in land management and in the associated industrial/energy systems due to relatively coarse temporal and spatial resolution, and limited representation of land quality and feedstocks/management practices, interactions between biomass production and conversion systems, and local context, for example, governance of land use (Daioglou et al. 2019; Rose et al. 2020; Welfle et al. 2020; Calvin et al. 2021). A/R have generally been modelled as forests managed for carbon sequestration alone, rather than forestry providing both carbon sequestration and biomass supply (Calvin et al. 2021). Because IAMs do not include options to integrate new biomass production with existing agricultural and forestry systems (Paré et al. 2016; Mansuy et al. 2018; Cossel et al. 2019; Braghiroli and Passarini 2020; Djomo et al. 2020; Moreira et al. 2020; Strapasson et al. 2020; Rinke Dias de Souza et al. 2021), they may over-estimate the total additional land area required for biomass production. On the other hand, some integrated biomass production systems may prove less attractive to landholders than growing biomass crops in large blocks, from logistic, economic, or other points of view (Ssegane et al. 2016; Busch 2017; Ferrarini et al. 2017).

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Biomass-based systems and A/R can contribute to addressing land degradation through land rehabilitation or restoration (Box 12.3). Land-based mitigation options that produce biomass for bioenergy/BECCS or biochar through land rehabilitation rather than land restoration imply a trade-off between production / carbon sequestration and biodiversity outcomes (Hua et al. 2016; Cowie et al. 2018). Restoration, seeking to establish native vegetation with the aim to maximise ecosystem integrity, landscape connectivity, and conservation of on-ground carbon stock, will have higher biodiversity benefits than rehabilitation measures (Lin et al. 2013). However, sequestration rate declines as forests mature, and the sequestered carbon is vulnerable to loss through disturbance such as wildfire, so there is a higher risk of reversal of the mitigation benefit compared with use of biomass for substitution of fossil fuels and GHG-intensive building materials (Russell and Kumar 2017; Dugan et al. 2018; Anderegg et al. 2020). Trade-offs between different ecosystem services, and between societal objectives including climate change mitigation and adaptation, can be managed through integrated landscape approaches that aim to create a mosaic of land uses, including conservation, agriculture, forestry and settlements (Freeman et al. 2015; Nielsen 2016; Reed et al. 2016; Sayer et al. 2017) where each is sited with consideration of land potential and socio-economic objectives and context (Cowie et al. 2018) (limited evidence, high agreement ).

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Reservoir hydropower projects submerge areas as dams are established for water storage. Hydropower can be associated with significant and highly varying land occupation and carbon footprint (Poff and Schmidt 2016; Scherer and Pfister 2016a; dos Santos et al. 2017; Ocko and Hamburg 2019). The flooding of land causes CH4 emissions due to the anaerobic decomposition of submerged vegetation and there is also a loss of carbon sequestration due to mortality of submerged vegetation. The size of GHG emissions depends on the amount of vegetation submerged. The carbon in accumulated sediments in reservoirs may be released to the atmosphere as CO2 and CH4 upon decommissioning of dams, and while uncertain, estimates indicate that these emissions can make up a significant part of the cumulative GHG emissions of hydroelectric power plants (Moran et al. 2018; Almeida et al. 2019; Ocko and Hamburg 2019). Positive radiative forcing due to lower albedo of hydropower reservoirs compared to surrounding landscapes can reduce mitigation contribution significantly (Wohlfahrt et al. 2021).

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The land sector (Chapter 7) contributes to mitigation via emissions reduction and enhancement of land carbon sinks, and by providing biomass for mitigation in other sectors. Key challenges for governance of land-based mitigation include social and environmental safeguards (Duchelle et al. 2017; Sills et al. 2017; Larson et al. 2018); insufficient financing (Turnhout et al. 2017); capturing co-benefits; ensuring additionality; addressing non-permanence of carbon sequestration; monitoring, reporting, and verification (MRV) of emissions reduction and carbon dioxide removals; and avoiding leakage or spillover effects. Governance approaches to addressing these challenges are discussed in Section 7.6, and include MRV systems and integrity criteria for project-level emissions trading; payments for ecosystem services; land-use planning and land zoning; certification schemes, standards and codes of practice.

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At the same time, saving electricity in all sectors reduces the demand for electricity, thereby reducing mitigation potential of renewables and CCS. Demand-side flexibility measures and electrification of vehicle fleets are supportive of more intermittent renewable energy supply options (Sections 6.3.7, 6.4.3.1 and 10.3.4). Production of maize, wheat, rice and fresh produce requires lower energy inputs on a lifecycle basis than poultry, pork and ruminant-based meats (Clark and Tilman 2017) (Section 12.4). It also requires less land area per kilocalorie or protein output (Clark and Tilman 2017; Poore and Nemecek 2018), so replacing meat with these products makes land available for sequestration, biodiversity or other societal needs. However, production of co-products of the meat industry, such as leather and wool, is reduced, resulting in a need for substitutes. Further discussion and examples of cross-sectoral implications of mitigation, with respect to cost and potentials, are presented in Section 12.2. One final example on this topic included here is that of circular economy (Box 12.4).

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DACCS uses chemicals that bind to CO2 directly from the air; the CO2 is then removed from the sorbent and stored underground or mineralised. Enhanced weathering involves the mining of rocks containing minerals that naturally absorb CO2 from the atmosphere over geological timescales, which are crushed to increase the surface area and spread on soils (or elsewhere) where they absorb atmospheric CO2. Ocean alkalinity enhancement involves the extraction, processing, and dissolution of minerals and addition to the ocean where they enhance sequestration of CO2 as bicarbonate and carbonate ions in the ocean.

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Cipolla, G., S. Calabrese, L.V. Noto, and A. Porporato, 2021: The role of hydrology on enhanced weathering for carbon sequestration II. From hydroclimatic scenarios to carbon-sequestration efficiencies. Adv. Water Resour. , 154, 103949, doi:10.1016/j.advwatres.2021.103949.

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Favero, A., A. Daigneault, and B. Sohngen, 2020: Forests: Carbon sequestration, biomass energy, or both?Sci. Adv. , 6(13) , doi:10.1126/sciadv.aay6792.

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Haque, F., R.M. Santos, A. Dutta, M. Thimmanagari, and Y.W. Chiang, 2019: Co-Benefits of Wollastonite Weathering in Agriculture: CO2 Sequestration and Promoted Plant Growth. ACS Omega, 4(1) , 1425–1433, doi:10.1021/acsomega.8b02477.

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Kelland, M.E. et al., 2020: Increased yield and CO2 sequestration potential with the C4 cereal Sorghum bicolor cultivated in basaltic rock dust-amended agricultural soil. Glob. Chang. Biol. , 26(6) , 3658–3676, doi:10.1111/GCB.15089.

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Krause-Jensen, D. and C.M. Duarte, 2016: Substantial role of macroalgae in marine carbon sequestration. Nat. Geosci. , 9(10) , 737–742, doi:10.1038/ngeo2790.

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Krause-Jensen, D. et al., 2018: Sequestration of macroalgal carbon: the elephant in the Blue Carbon room. Biol. Lett. , 14(6) , 20180236, doi:10.1098/rsbl.2018.0236.

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McQueen, N. et al., 2020: Cost Analysis of Direct Air Capture and Sequestration Coupled to Low-Carbon Thermal Energy in the United States ̀. Environ. Sci. Technol. , 54, 7542–7551, doi:10.1021/acs.est.0c00476.

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Miller, L.A. and P.M. Orton, 2021: Achieving negative emissions through oceanic sequestration of vegetation carbon as Black Pellets. Clim. Change, 167(3–4) , 29, doi:10.1007/s10584-021-03170-5.

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Montserrat, F. et al., 2017: Olivine Dissolution in Seawater: Implications for CO2 Sequestration through Enhanced Weathering in Coastal Environments. Environ. Sci. Technol. , 51(7) , 3960–3972, doi:10.1021/acs.est.6b05942.

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NASEM, 2019: Negative Emissions Technologies and Reliable Sequestration: A Research Agenda. National Academy of Sciences Engineering and Medicine, Washington DC, USA, 510 pp.

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Renforth, P. and G. Henderson, 2017: Assessing ocean alkalinity for carbon sequestration. Rev. Geophys. , 55(3) , 636–674, doi:10.1002/2016RG000533.

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Sanchez, D.L., N. Johnson, S.T. McCoy, P.A. Turner, and K.J. Mach, 2018: Near-term deployment of carbon capture and sequestration from biorefineries in the United States. Proc. Natl. Acad. Sci. U. S. A. , 115(19) , 4875–4880, doi:10.1073/pnas.1719695115.

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Siegel, D.A., T. Devries, S.C. Doney, and T. Bell, 2021: Assessing the sequestration time scales of some ocean-based carbon dioxide reduction strategies. Environ. Res. Lett. , 16(10) , 104003, doi:10.1088/1748-9326/AC0BE0.

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Strand, S.E. and G. Benford, 2009: Ocean Sequestration of Crop Residue Carbon: Recycling Fossil Fuel Carbon Back to Deep Sediments. Environ. Sci. Technol. , 43(4) , 1000–1007, doi:10.1021/es8015556.

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Wang, N., K. Akimoto, and G.F. Nemet, 2021: What went wrong? Learning from three decades of carbon capture, utilization and sequestration (CCUS) pilot and demonstration projects. Energy Policy, 158, 112546, doi:10.1016/j.enpol.2021.112546.

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Wedding, L.M. et al., 2021: Incorporating blue carbon sequestration benefits into sub-national climate policies. Glob. Environ. Change, 69, 102206, doi:10.1016/j.gloenvcha.2020.102206.

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In addition to deep, rapid, and sustained emission reductions, CDR can fulfil three complementary roles: lowering net CO2 or net GHG emissions in the near term; counterbalancing ‘ hard-to-abate’ residual emissions (e.g., some emissions from agriculture , aviation, shipping, industrial processes) to help reach net zero CO2 or GHG emissions, and achieving net negative CO2 or GHG emissions if deployed at levels exceeding annual residual emissions (high confidence) . CDR methods vary in terms of their maturity, removal process, time scale of carbon storage, storage medium, mitigation potential, cost, co-benefits, impacts and risks, and governance requirements. (high confidence). Specifically, maturity ranges from lower maturity (e.g., ocean alkalinisation) to higher maturity (e.g., reforestation); removal and storage potential ranges from lower potential (<1 Gt CO2 yr -1, e.g., blue carbon management) to higher potential (>3 Gt CO2 yr -1, e.g., agroforestry); costs range from lower cost (e.g., –45 to 100 USD tCO2-1 for soil carbon sequestration) to higher cost (e.g., 100 to 300 USD tCO2-1 for direct air carbon dioxide capture and storage) (medium confidence). Estimated storage timescales vary from decades to centuries for methods that store carbon in vegetation and through soil carbon management, to ten thousand years or more for methods that store carbon in geological formations. (high confidence). Afforestation, reforestation, improved forest management, agroforestry and soil carbon sequestration are currently the only widely practiced CDR methods (high confidence). Methods and levels of CDR deployment in global modelled mitigation pathways vary depending on assumptions about costs, availability and constraints (high confidence). {WGIII SPM C.3.5, WGIII SPM C.11.1, WGIII SPM C.11.4}

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Reforestation, improved forest management, soil carbon sequestration, peatland restoration and coastal blue carbon management are examples of CDR methods that can enhance biodiversity and ecosystem functions, employment and local livelihoods, depending on context 139 . However, afforestation or production of biomass crops for bioenergy with carbon dioxide capture and storage or biochar can have adverse socio-economic and environmental impacts, including on biodiversity, food and water security, local livelihoods and the rights of Indigenous Peoples, especially if implemented at large scales and where land tenure is insecure. (high confidence) {WGII SPM B.5.4, WGII SPM C.2.4; WGIII SPM C.11.2; SR1.5 SPM C.3.4, SR1.5 SPM C.3.5; SRCCL SPM B.3, SRCCL SPM B.7.3, SRCCL Figure SPM.3}

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In addition to the temperature classification, each scenario is assigned to one of the following policy categories: (P0) diagnostic scenarios – 99 of 1686 vetted scenarios; (P1) scenarios with no globally coordinated policy (500) and (P1a) no climate mitigation efforts – 124, (P1b) current national mitigation efforts – 59, (P1c) Nationally Determined Contributions (NDCs) – 160, or (P1d) other non-standard assumptions – 153; (P2) globally coordinated climate policies with immediate action (634) and (P2a) without any transfer of emission permits – 435, (P2b) with transfers – 70; or (P2c) with additional policy assumptions – 55; (P3) globally coordinated climate policies with delayed (i.e., from 2030 onwards or after 2030) action (451), preceded by (P3a) no mitigation commitment or current national policies – 7, (P3b) NDCs – 426, (P3c) NDCs and additional policies – 18; (P4) cost-benefit analysis (CBA) – 2. The policy categories were identified using text pattern matching on the scenario metadata and calibrated on the best-known scenarios from model intercomparisons, with further validation against the related literature, reported emission and carbon price trajectories, and exchanges with modellers. If the information available is enough to qualify a policy category number but not sufficient for a subcategory, then only the number is retained (e.g., P2 instead of P2a/b/c). A suffix added after P0 further qualifies a diagnostic scenario as one of the other policy categories. To demonstrate the diversity of the scenarios, the vetted scenarios were classified into different categories along the dimensions of population, GDP, energy, and cumulative emissions (Figure 3.4). The number of scenarios in each category provides some insight into the current literature, but this does not indicate a higher probability of that category occurring in reality. For population, the majority of scenarios are consistent with the SSP2 ‘middle of the road’ category, with very few scenarios exploring the outer extremes. GDP has a slightly larger variation, but overall most scenarios are around the SSP2 socio-economic assumptions. The level of CCS and CDR is expected to change depending on the extent of mitigation, but there remains extensive use of both CDR and CCS in scenarios. CDR is dominated by bioenergy with CCS (BECCS) and sequestration on land, with relatively few scenarios using direct air capture with carbon storage (DACCS) and even less with enhanced weathering (EW) and other technologies (not shown). In terms of energy consumption, final energy has a much smaller range than primary energy as conversion losses are not included in final energy. Both mitigation and reference scenarios are shown, so there is a broad spread in different energy carriers represented in the database. Bioenergy has a number of scenarios at around 100 EJ, representing a constraint used in many model intercomparisons.

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Climate change can impact the potential for AFOLU mitigation action by altering terrestrial carbon uptake, crop yields and bioenergy potential (Chapter 7). Carbon sequestration in forests may be positively or adversely affected by climate change and CO2 fertilisation. On the one hand, elevated CO2 levels and higher temperatures could enhance tree growth rates, carbon sequestration, and timber and biomass production (Beach et al. 2015; Kim et al. 2017; Anderegg et al. 2020). On the other hand, climate change could lead to greater frequency and intensity of disturbance events in forests, such as fires, prolonged droughts, storms, pests and diseases (Kim et al. 2017; Anderegg et al. 2020). The impact of climate change on crop yields could also indirectly impact the availability of land for mitigation and AFOLU emissions (Calvin et al. 2013; Bajželj and Richards 2014; Kyle et al. 2014; Beach et al. 2015; Meijl et al. 2018). The impact is, however, uncertain, as discussed in AR6 WGII Chapter 5. A few studies estimate the effect of climate impacts on AFOLU on mitigation, finding increases in carbon prices or mitigation costs by 1–6% in most scenarios (Calvin et al. 2013; Kyle et al. 2014).

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Mitigation in one sector can also result in additional emissions in another. One example is electrification of end use which can result in increased emissions from energy supply. However, one comparitively well-researched example of this linkage is bioenergy. An increase in demand for bioenergy within the energy system has the potential to influence emissions in the AFOLU sector through the intensification of land and forest management and/or via land-use change (Daioglou et al. 2019; Smith et al. 2019; Smith et al. 2020a; IPCC 2019a). The effect of bioenergy and BECCS on mitigation depends on a variety of factors in modelled pathways. In the energy system, the emissions mitigation depends on the scale of deployment, the conversion technology, and the fuel displaced (Calvin et al. 2021). Limiting or excluding bioenergy and/or BECCS increases mitigation cost and may limit the ability of a model to reach a low warming level (Edmonds et al. 2013; Calvin et al. 2014b; Luderer et al. 2018; Muratori et al. 2020). In AFOLU, bioenergy can increase or decrease terrestrial carbon stocks and carbon sequestration, depending on the scale, biomass feedstock, land management practices, and prior land use (Calvin et al. 2014c; Wise et al. 2015; IPCC 2019a; Smith et al. 2019, 2020a; Calvin et al. 2021).

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Pathways with very high biomass production for energy use typically include very high carbon prices in the energy system (Popp et al. 2017; Rogelj et al. 2018 b), little or no land policy (Calvin et al. 2014b), a high discount rate (Emmerling et al. 2019), and limited non-BECCS CDR options (e.g., afforestation, DACCS) (Chen and Tavoni 2013; Calvin et al. 2014b; Marcucci et al. 2017; Realmonte et al. 2019; Fuhrman et al. 2020). Higher levels of bioenergy consumption are likely to involve trade-offs with mitigation in other sectors, notably in construction (i.e., wood for material and structural products) and AFOLU (carbon stocks and future carbon sequestration), as well as trade-offs with sustainability (Section 3.7) and feasibility concerns (Section 3.8). Not all of these trade-offs are fully represented in all IAMs. Based on sectoral studies, the technical potential for bioenergy, when constraints for food security and environmental considerations are included, are 5–50 EJ yr –1 and 50–250 EJ yr –1 in 2050 for residues and dedicated biomass production systems, respectively (Chapter 7). Bioenergy deployment in IAMs is within the range of these potentials, with between 75 and 248 EJ yr –1 in 2050 in pathways that limit warming to 1.5°C with no or limited overshoot. Finally, IAMs do not include all potential feedstock and management practices, and have limited representation of institutions, governance, and local context (Brown et al. 2019; Butnar et al. 2020; Calvin et al. 2021).

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The inclusion of CDR options, like BECCS, can affect the timing of emissions mitigation in IAM scenarios, that is, delays in mitigations actions are compensated by net negative emissions in the second half of the century. However, studies with limited net negative emissions in the long term require very rapid declines in emissions in the near term (van Vuuren et al. 2017). Especially in forest-based systems, increased harvesting of forests can perturb the carbon balance of forestry systems, increasing emissions for some period; the duration of this period of increased emissions, preceding net emissions reductions, can be very variable (Mitchell et al. 2012; Lamers and Junginger 2013; Röder et al. 2019; Hanssen et al. 2020; Cowie et al. 2021). However, the factors contributing to differences in recovery time are known (Mitchell et al. 2012; Zanchi et al. 2012; Lamers and Junginger 2013; Laganière et al. 2017; Röder et al. 2019). Some studies that consider market-mediated effects find that an increased demand for biomass from forests can provide incentives to maintain existing forests and potentially to expand forest areas, providing additional carbon sequestration as well as additional biomass (Dwivedi et al. 2014; Kim et al. 2018; Baker et al. 2019; Favero et al. 2020). However, these responses are uncertain and likely to vary geographically.

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Some AFOLU mitigation options can enhance vegetation and soil carbon stocks such as reforestation, restoration of degraded ecosystems, protection of ecosystems with high carbon stocks and changes to agricultural land management to increase soil carbon ( high confidence) (Griscom et al. 2017; de Coninck et al. 2018; Fuss et al. 2018; Smith et al. 2019) (AR6 WGIII Chapter 7). The time scales associated with these options indicate that carbon sinks in terrestrial vegetation and soil systems can be maintained or enhanced so as to contribute towards long-term mitigation ( high confidence); however, many AFOLU mitigation options do not continue to sequester carbon indefinitely (Fuss et al. 2018; de Coninck et al. 2018; IPCC 2019a) (AR6 WGIII Chapter 7). In the very long term (the latter part of the century and beyond), it will become more challenging to continue to enhance vegetation and soil carbon stocks, so that the associated carbon sinks could diminish or even become sources ( high confidence) (de Coninck et al. 2018; IPCC 2019a) (AR6 WGI Chapter 5). Sustainable forest management, including harvest and forest regeneration, can help to remediate and slow any decline in the forest carbon sink, for example by restoring degraded forest areas, and so go some way towards addressing the issue of sink saturation (IPCC 2019) (AR6 WGI Chapter 5; and Chapter 7 in this report). The accumulated carbon resulting from mitigation options that enhance carbon sequestration (e.g., reforestation, soil carbon sequestration) is also at risk of future loss due to disturbances (e.g., fire, pests) (Boysen et al. 2017; de Coninck et al. 2018; Fuss et al. 2018; Smith et al. 2019; IPCC 2019a; Anderegg et al. 2020) (AR6 WGI Chapter 5). Maintaining the resultant high vegetation and soil carbon stocks could limit future land-use options, as maintaining these carbon stocks would require retaining the land use and land-cover configuration implemented to achieve the increased stocks.

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Very few studies and pathways include other CDR options (Table 3.5). Pathways with DACCS include potentially large removal from DACCS (up to 37 GtCO2 yr –1 in 2100) in the second half of the century (Chen and Tavoni 2013; Marcucci et al. 2017; Realmonte et al. 2019; Fuhrman et al. 2020, 2021; Shayegh et al. 2021; Akimoto et al. 2021) and reduced cost of mitigation (Bistline and Blanford 2021; Strefler et al. 2021a). At large scales, the use of DACCS has substantial implications for energy use, emissions, land, and water; substituting DACCS for BECCS results in increased energy usage, but reduced land-use change and water withdrawals (Fuhrman et al., 2020, 2021) (Chapter 12.3.2; AR6 WGI Chapter 5). The level of deployment of DACCS is sensitive to the rate at which it can be scaled up, the climate goal or carbon budget, the underlying socio-economic scenario, the availability of other decarbonisation options, the cost of DACCS and other mitigation options, and the strength of carbon-cycle feedbacks (Chen and Tavoni 2013; Fuss et al. 2013; Honegger and Reiner 2018; Realmonte et al. 2019; Fuhrman et al. 2020; Bistline and Blanford 2021; Fuhrman et al. 2021; Strefler et al. 2021a) (AR6 WGI Chapter 5). Since DACCS consumes energy, its effectiveness depends on the type of energy used; the use of fossil fuels would reduce its sequestration efficiency (Creutzig et al. 2019; NASEM 2019; Babacan et al. 2020). Studies with additional CDR options in addition to DACCS (e.g., enhanced weathering, BECCS, afforestation, biochar, and soil carbon sequestration) find that CO2 removal is spread across available options (Holz et al. 2018; Strefler et al. 2021a). Similar to DACCS, the deployment of deep-ocean storage depends on cost and the strength of carbon-cycle feedbacks (Rickels et al. 2018).

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Table 3.5 |Carbon dioxide removal in assessed pathways. Scenarios are grouped by temperature categories, as defined in Section 3.2.4. Quantity indicates the median and 5–95th percentile range of cumulative sequestration from 2020 to 2100 in GtCO2. Count indicates the number of scenarios with positive values for that option. Source: AR6 Scenarios Database.

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Baker, J.S., C.M. Wade, B.L. Sohngen, S. Ohrel, and A.A. Fawcett, 2019: Potential complementarity between forest carbon sequestration incentives and biomass energy expansion. Energy Policy, 126 (August 2018), 391–401, doi:10.1016/j.enpol.2018.10.009.

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Favero, A., A. Daigneault, and B. Sohngen, 2020: Forests: Carbon sequestration, biomass energy, or both?Sci. Adv. , 6(13) , eaay6792, doi:10.1126/sciadv.aay6792.

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Mitchell, S.R., M.E. Harmon, and K.E.B. O’Connell, 2012: Carbon debt and carbon sequestration parity in forest bioenergy production. GCB Bioenergy, 4, 818–827, doi:10.1111/j.1757-1707.2012.01173.x.

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NASEM, 2019: Negative Emissions Technologies and Reliable Sequestration: A Research Agenda. National Academies of Sciences, Engineering, and Medicine, National Academies Press, Washington D.C, USA, 510 pp.

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Enhanced weathering (EW) is based on naturally occurring weathering processes of silicate and carbonate rocks, removing CO2 from the atmosphere. Weathering is accelerated by spreading ground rocks on soils, coasts or oceans. EW increases the alkalinity and pH of natural waters, helps dampen ocean acidification and increases ocean carbon uptake (Beerling et al., 2018). The dissolution of minerals stimulates biological productivity of croplands (Hartmann et al., 2013; Beerling et al., 2018), but can also liberate toxic trace metals (such as nickel, chromium, copper) into soil or water bodies (Keller et al., 2018a; Strefler et al., 2018). EW can also contribute to freshwater salinization as a result of increased salt inputs and cation exchange in watersheds, and so adversely affecting drinking water quality (low confidence) (Kaushal et al., 2018). With amedium confidence, amendment of soils with minerals will have lower N2O emissions (Kantola et al., 2017; Blanc-Betes et al., 2020) but will not have a marked effect on evapotranspiration or albedo (Fuss et al., 2018; de Oliveira Garcia et al., 2020). The mining of minerals can cause adverse impacts on biodiversity, however, the use of waste materials such as concrete demolition or steel slags for EW can reduce the need for mining (Renforth, 2019). The spreading of minerals on land has a neutral impact on biodiversity (P. Smith et al., 2018).

albedoresources/ipcc/cleaned_content/wg1/Chapter02/html_with_ids.html#2.2.7_p1

The AR5 assessed that land use change very likely increased the Earth’s albedo with a radiative forcing of –0.15 (± 0.10) W m–2. AR5 also assessed that a net cooling of the surface, accounting for processes that are not limited to the albedo, was about as likely as not . The SRCCL concluded with medium confidence that the biophysical effects of land cover change (mainly increased albedo) had a cooling effect on surface temperatures. The SRCCL also concluded with very high confidence that the biogeochemical effects of land cover change (i.e., GHG emissions) resulted in a mean annual surface warming.

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The impact of historical land-cover change on global climate is assessed with model simulations that consider multiple climate and biophysical processes (e.g., changes in albedo, evapotranspiration, and roughness) and/or biogeochemical processes (e.g., changes in atmospheric composition such as carbon release from deforestation). The dominant biophysical response to land cover changes is albedo, which is estimated (using a MODIS albedo product and a historical land-use harmonization product) to have increased gradually prior to the mid-19th century and then strongly through the mid-20th century, with a slightly slower rise thereafter (Ghimire et al., 2014). Recent radiative forcing estimates arising from biophysical processes generally fall at the lower end of the AR5 assessed range. For instance, based on historical simulations from 13 CMIP6 models, C.J. Smith et al. (2020) estimated that the ERF from surface albedo changes (including snow cover and leaf area) was –0.08 [–0.22 to +0.06] W m–2 since 1850. Similarly, based on simulations from 13 CMIP5 models, Lejeune et al. (2020) estimated the radiative forcing from transitions between trees, crops, and grasslands was –0.11 [–0.16 to +0.04] W m–2 since 1860. Andrews et al. (2017) identified an ERF of –0.40 W m–2 since 1860, ascribing much of the effect to increases in albedo (including the unmasking of underlying snow cover); notably, however, the analysis was based on a single model with a known tendency to overestimate the ERF (Collins et al., 2011). Ward et al. (2014) examined the combined effects of biophysical and biogeochemical processes, obtaining an RF of 0.9 ± 0.5 W m–2 since 1850 that was driven primarily by increases in land-use related GHG emissions from deforestation and agriculture (Ward and Mahowald, 2015). According to a large suite of historical simulations, the biophysical effects of changes in land cover (i.e., increased surface albedo and decreased turbulent heat fluxes) led to a net global cooling of 0.10°C ± 0.14°C at the surface (SRCCL). Available model simulations suggest that biophysical and biogeochemical effects jointly may have contributed to a small global warming of 0.078°C ± 0.093°C at the surface over about the past two centuries (SRCCL), with a potentially even larger warming contribution over the Holocene as a whole (He et al., 2014).

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In summary, biophysical effects from historical changes in land use have an overall negative ERF (medium confidence). The best-estimate ERF from the increase in global albedo is –0.15 W m–2 since 1700 and –0.12 W m–2 since 1850 (medium confidence) (Section 7.3.4.1). Biophysical effects of land-use change likely resulted in a net global cooling of about 0.1°C since 1750 (medium confidence) (Section 7.3.5.3).

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Land use and land cover changes (Section 2.2.7) over the industrial period introduce a negative radiative forcing by increasing the surface albedo. This effect increased since 1750, reaching current values of about –0.20 W m–2 (medium confidence). This ERF value is taken from Section 7.3.4.1 and is different from the assessment in Section 2.2.7 in that it also includes the effect of irrigation. It also includes uncertain rapid adjustments and thus there is low confidence in its magnitude. Biogeochemical feedbacks can be substantial (Section 5.4) and are not included in ERF.

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Lejeune, Q. et al., 2020: Biases in the albedo sensitivity to deforestation in CMIP5 models and their impacts on the associated historical Radiative Forcing. Earth System Dynamics, 11(4), 1209–1232, doi: 10.5194/esd-2019-94.

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Li, Y. et al., 2016: Evaluating biases in simulated land surface albedo from CMIP5 global climate models. Journal of Geophysical Research: Atmospheres, 121(11), 6178–6190, doi: 10.1002/2016jd024774.

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Thackeray, C.W., C.G. Fletcher, and C. Derksen, 2015: Quantifying the skill of CMIP5 models in simulating seasonal albedo and snow cover evolution. Journal of Geophysical Research: Atmospheres, 120(12), 5831–5849, doi: 10.1002/2015jd023325.

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Thackeray, C.W., X. Qu, and A. Hall, 2018a: Why Do Models Produce Spread in Snow Albedo Feedback?Geophysical Research Letters, 45(12), 6223–6231, doi: 10.1029/2018gl078493.

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Xiao, L., T. Che, L. Chen, H. Xie, and L. Dai, 2017: Quantifying Snow Albedo Radiative Forcing and Its Feedback during 2003–2016. Remote Sensing, 9(9), 883, doi: 10.3390/rs9090883.

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With regards to the model selection in AR5, model evaluation studies have since identified shortcomings of the CMIP5 models to match the observed distribution of sea ice thickness in the Arctic (Stroeve et al., 2014; Shu et al., 2015) and the observed evolution of albedo on seasonal scales (Koenigk et al., 2014). It was also found that many models’ deviation from observed sea ice cover climatology cannot be explained by internal variability, whereas the models’ deviation from observed sea ice cover trend (over the satellite period) can often be explained by internal variability (Olonscheck and Notz, 2017). This hinders a selection of models according to their simulated trends, which additionally has been shown to only have a weak effect on the magnitude of simulated future trends (Stroeve and Notz, 2015).

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A variety of mechanisms contribute to Arctic amplification (Section 7.4.4.1.1). While surface-albedo feedbacks associated with the loss of sea ice and snow have long been known to play important roles (Arrhenius, 1896; Manabe and Stouffer, 1980; Robock, 1983; Hall, 2004), it is now recognized that temperature (lapse-rate and Planck) feedbacks also contribute to Arctic amplification through a less efficient longwave radiative damping to space with warming at high latitudes (Winton, 2006; Pithan and Mauritsen, 2014; Goosse et al., 2018; Stuecker et al., 2018). Increases in poleward atmospheric latent heat transport and oceanic heat transport also contribute to Arctic warming (Holland and Bitz, 2003; Bitz et al., 2006; Lee et al., 2011, Lee et al., 2017; Alexeev and Jackson, 2013; Marshall et al., 2014, 2015; Woods and Caballero, 2016; Nummelin et al., 2017; Singh et al., 2017; Merlis and Henry, 2018; Oldenburg et al., 2018; Armour et al., 2019; Beer et al., 2020). Projected reduction in the strength of the AMOC over the 21st century is expected to reduce Arctic warming, but even a strong AMOC reduction would not eliminate Arctic amplification entirely (medium confidence) (Liu et al., 2017; Liu et al., 2018; Wen et al., 2018).

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Changes in GSAT variability are poorly understood. Based on model experiments it has been suggested that unforced variability of GSAT tends to decrease in a warmer world as a result of reduced albedo variability in high latitudes resulting from melting snow and sea ice (Huntingford et al., 2013; Brown et al., 2017), but confidence remains low and an observed change has not been detected. An assessment of changes in global temperature variability is inherently challenging due to the interplay of unforced internal variability and forced changes.

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When CDR is applied continuously and at scales as large as currently deemed possible, under RCP8.5 as the background scenario, the widely discussed CDR options such as afforestation, ocean iron fertilization and surface ocean alkalinisation are individually expected to be relatively ineffective, with limited (8%) warming reductions relative to the scenario with no CDR option (Keller et al., 2014). Hence, the potential role that CDR will play in lowering the temperature in high-emissions scenarios is limited (medium confidence). The challenges involved in comparing the climatic effects of various CDR options has also been recognized in recent studies (Sonntag et al., 2018; Mengis et al., 2019). For instance, due to compensating processes such as biogeophysical effects of afforestation (warming from albedo decrease when croplands are converted to forests) more carbon is expected to be removed from the atmosphere by afforestation than by ocean alkalinisation to reach the same global mean cooling.

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Simple calculations and climate modelling studies show that about 2% extra solar irradiance reflected away from Earth or a one percentage point increase in planetary albedo (0.31 to 0.32) would suffice to offset global mean warming from a doubling of the CO2 concentration (TheRoyal Society, 2009; Kravitz et al., 2013a, 2021). To offset the same amount of CO2 -induced GSAT increase, different levels of ERF are required for different methods of SRM (Schmidt et al., 2012; Chiodo and Polvani, 2016; Modak et al., 2016; Duan et al., 2018; Russotto and Ackerman, 2018; Krishnamohan et al., 2019; Zhao et al., 2021).

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Surface-based albedo modification could, in principle, achieve a negative radiative forcing of a few W m–2 by enhancing the albedo of the ocean surface (Gabriel et al., 2017; Kravitz et al., 2018). However, the technology does not exist today to increase ocean albedo at large scale. An increase in crop albedo or roof albedo in urban areas could help to reduce warming in densely populated and important agricultural regions, but the effect would be limited to local scales and ineffective at counteracting global warming (Crook et al., 2015; Zhang et al., 2016). Large changes in desert albedo could in principle result in substantial global cooling, but would severely alter the hydrological cycle (Crook et al., 2015).

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Bala, G. et al., 2011: Albedo enhancement of marine clouds to counteract global warming: impacts on the hydrological cycle. Climate Dynamics, 37(5), 915–931, doi: 10.1007/s00382-010-0868-1.

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Chen, Y.-C. et al., 2012: Occurrence of lower cloud albedo in ship tracks. Atmospheric Chemistry and Physics, 12(17), 8223–8235, doi: 10.5194/acp-12-8223-2012.

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Davin, E.L., S.I. Seneviratne, P. Ciais, A. Olioso, and T. Wang, 2014: Preferential cooling of hot extremes from cropland albedo management. Proceedings of the National Academy of Sciences, 111(27), 9757–9761, doi: 10.1073/pnas.1317323111.

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Field, L. et al., 2018: Increasing Arctic Sea Ice Albedo Using Localized Reversible Geoengineering. Earth’s Future, 6(6), 882–901, doi: 10.1029/2018ef000820.

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Gabriel, C.J., A. Robock, L. Xia, B. Zambri, and B. Kravitz, 2017: The G4Foam Experiment: global climate impacts of regional ocean albedo modification. Atmospheric Chemistry and Physics, 17(1), 595–613, doi: 10.5194/acp-17-595-2017.

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Hall, A., 2004: The Role of Surface Albedo Feedback in Climate. Journal of Climate, 17(7), 1550–1568, doi: 10.1175/1520-0442(2004)017<1550:trosaf>2.0.co;2.

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Hall, A. and X. Qu, 2006: Using the current seasonal cycle to constrain snow albedo feedback in future climate change. Geophysical Research Letters, 33(3), L03502, doi: 10.1029/2005gl025127.

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Koenigk, T., A. Devasthale, and K.G. Karlsson, 2014: Summer arctic sea ice albedo in CMIP5 models. Atmospheric Chemistry and Physics, 14(4), 1987–1998, doi: 10.5194/acp-14-1987-2014.

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Kravitz, B. et al., 2018: The climate effects of increasing ocean albedo: an idealized representation of solar geoengineering. Atmospheric Chemistry and Physics, 18(17), 13097–13113, doi: 10.5194/acp-18-13097-2018.

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Winton, M., 2006: Amplified Arctic climate change: What does surface albedo feedback have to do with it?Geophysical Research Letters, 33(3), L03701, doi: 10.1029/2005gl025244.

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Zhao, M., L. Cao, L. Duan, G. Bala, and K. Caldeira, 2021: Climate More Responsive to Marine Cloud Brightening Than Ocean Albedo Modification: A Model Study. Journal of Geophysical Research: Atmospheres, 126(3), e2020JD033256, doi: 10.1029/2020jd033256.

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The SRCCL (Jia et al., 2019) assessed that there is robust evidence and high agreement that land cover and land use or management exert significant influence on atmospheric states (e.g., temperature, rainfall, wind intensity) and phenomena (e.g., monsoons), at various spatial and temporal scales, through their biophysical influences on climate. There is robust evidence that dry soil moisture anomalies favour summer heatwaves. Part of the projected increase in heatwaves and droughts can be attributed to soil moisture feedbacks in regions where evapotranspiration is limited by moisture availability (medium confidence). Vegetation changes can also amplify or dampen extreme events through changes in albedo and evapotranspiration, which will influence future trends in extreme events (medium confidence).

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Low Earth orbit (LEO) satellites, with orbits typically at 400–700 km, provide advanced measurements of the Earth’s surface. Sun-synchronous polar orbiters can also cover the polar regions, which cannot be observed with GEO satellites. Examples of LEO observations for land surface monitoring are NASA’s Landsat (Wulder et al., 2016), ESA’s Soil Moisture Ocean Salinity Earth Explorer (SMOS) mission (Kerr et al., 2012), the Sentinel missions of the Copernicus programme, and JAXA’s ALOS-2 (Ohki et al., 2019), providing high spatial resolution land surface images. Many kinds of data are accumulated for land use and land cover studies, targeting aspects like urban footprint (Florczyk et al., 2019), land-cover data (Global Land 30; CCI-LC: ESA, 2021; Chen and Chen, 2018), land surfacetemperature data (Landsat, Parastatidis et al., 2017), and surface albedo (Chrysoulakis et al., 2019).

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Nudging experiments have been used to identify the relative roles of dynamic and thermodynamic processes in climate model biases and specific extreme events (Wehrli et al., 2018, 2019). Another related framework is used to evaluate the impact land conditions have on a climate phenomenon in a pair of experiments with one simulation serving as control run, and a perturbed simulation with prescribed land conditions (i.e., soil moisture, leaf area index, or surface albedo) characterizing a specific state of the land surface (i.e., afforestation or deforestation). The difference between the perturbed and control simulations enables a robust assessment of the possible impact of land conditions on events like droughts and heatwaves (Seneviratne et al., 2013; Stegehuis et al., 2015; Hauser et al., 2016, 2017; van den Hurk et al., 2016; Vogel et al., 2017; Rasmijn et al., 2018; Strandberg and Kjellström, 2019).

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The snow-albedo feedback contributes to enhanced warming at high elevations (Section 8.5; Pepin et al., 2015). Global models often do not simulate it realistically due to their misrepresentation of orography in complex terrain (Hall, 2014; Walton et al., 2015). The elevation dependence of historical warming, which is partly caused by the snow-albedo effect, is realistically represented across Europe by the ENSEMBLES RCMs (Kotlarski et al., 2015). Some EURO-CORDEX RCMs simulate a spring snow–albedo feedback close to that observed, whereas others considerably overestimate it (Winter et al., 2017). In a multi-physics ensemble RCM experiment, the cold bias in north-eastern Europe is amplified by the albedo feedback (García-Díez et al., 2015). For the Rocky Mountains, RCM simulations generally reproduce the observed spatial and seasonal variability in snow cover, but strongly overestimate the snow albedo (Minder et al., 2016). There is high confidence (medium evidence and high agreement) that RCMs considerably improve the representation of the snow-albedo effect in complex terrain.

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Regional land-radiation management, including modifying the albedo through, for instance, no-tillage practices, has been suggested as a measure to decrease regional maximum daily temperatures (see review in Seneviratne et al., 2018), but although modelled results and theoretical understanding are coherent, few studies have verified the results with observations. Hirsch et al. (2018) is an exception, showing that implementing minimal tillage, crop residue management and crop rotation in a global model over regions where it is practiced, improves the simulation of surface heat fluxes.

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Increasing resolution (Haarsma et al., 2016) or performing downscaling may be particularly important when it modifies the climate change signal of a lower resolution model in a physically plausible way (Hall, 2014). Improvements may result from a better representation of regional processes, upscale effects, as well as the possibility of a region-specific model tuning (Sørland et al., 2018). For instance, Gula and Peltier (2012) showed that a higher resolution allows for a more realistic simulation of lake-induced precipitation, resulting in a more credible projection of changes in the snow belts of the North American Great Lakes. Similarly, Giorgi et al. (2016) demonstrated that an ensemble of RCMs better represents high-elevation surface heating and in turn increased convective instability. As a result, the summer convective precipitation response was opposite to that simulated by the driving global models (Figure 10.9). Similarly, Walton et al. (2015) showed that a kilometre-scale RCM enables a more realistic representation of the snow-albedo feedback in mountainous terrain compared to standard resolution global models, leading to a more plausible simulation of elevation-dependent warming. Bukovsky et al. (2017) argue that strong seasonal changes in warm-season precipitation in the Southern Great Plains of the USA, projected by RCMs, are more credible than the weaker global model changes because precipitation is better simulated in the RCMs.

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Bias adjustment assumes that model biases are time invariant (or more precisely, independent of the climate state), such that the adjustment made to present climate simulations is still applicable to future climate simulations. Many findings challenge the validity of this assumption, as already assessed in AR5 (Flato et al., 2014). Further research has addressed this issue by means of perfect model experiments (Section 10.3.2.5) and process understanding. Perfect-model studies with GCMs found that circulation, energy, and water-cycle biases are roughly state-independent (Krinner and Flanner, 2018), whereas temperature biases depend linearly on temperature (Kerkhoff et al., 2014). Others show that regional temperature biases may depend on soil moisture and albedo, and may thus be state-dependent (Maraun, 2012; Bellprat et al., 2013; Maraun et al., 2017; see Cross-Chapter Box 10.2 for further limitations of bias adjustment). The fitness of weather generators for future projections depends on whether they account for all relevant changes in their parameters, either by predictors or change factors (Maraun and Widmann, 2018b).

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Bias adjustment is often used to downscale climate model results from grid box data to finer resolution or point scale. It is sometimes even directly applied to coarse-resolution global model output to avoid an intermediate dynamical downscaling step (Johnson and Sharma, 2012; Stoner et al., 2013). But bias adjustment does not add any information about the processes acting on unresolved scales and is therefore by construction not capable of bridging substantial scale gaps (Maraun, 2013a; Maraun et al., 2017). Using bias adjustment for downscaling has been shown to artificially modify long-term trends, misrepresent the spatial characteristics of extreme events, and misrepresent local weather phenomena such as temperature inversions (Maraun, 2013a; Gutmann et al., 2014; Maraun et al., 2017). Crucially, sub-grid influences on the local climate change signal are not represented. For instance, if a mountain chain is not resolved in the driving model, the snow–albedo feedback is not represented by the bias adjustment such that local temperature trends in high altitudes are under-represented (Cross-Chapter Box 10.2, Figure 1; Maraun et al., 2017). It has therefore been suggested to account for local random variability by combining bias adjustment with stochastic downscaling (Volosciuk et al., 2017; Lange, 2019), although this approach still does not account for local modifications of the climate change signal. Two approaches have been proposed to represent these local changes: dynamical downscaling with high-resolution RCMs (Maraun et al., 2017) or statistical emulators of such (Walton et al., 2015). Sections 10.3.3.4–10.3.3.6 and 10.3.3.9 discuss other examples where RCMs improve the representation of regional phenomena and regional climate change.

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Over the period 1960–2014, observed trends over land are consistent with those of most of the multi-model or SMILEs ensembles (Figure 10.20f), although large differences exist for individual models and ensemble members. The modelled ensemble-mean trends show large geographical variations. Generally, both global and regional models often underestimate the observed trend especially over parts of North Africa, Italy, the Balkans and Turkey. The cold bias in global models is related to simulated SLP trends that are anti-correlated to the observed trend, which is probably due to systematic model errors (Boé et al., 2020b). Biases in the simulation of soil-moisture and cloud-cover might also have contributed to the underestimation of the warming trend in GCMs (van Oldenborgh et al., 2009). The CORDEX results (at both 0.44° and 0.11° resolution) show consistently smaller values than those in global models and the available datasets (Figure 10.20g; Vautard et al., 2021). This is partly due to the overestimation in the temperature evolution before 1990 (Figure 10.20e), possibly because of differences in the aerosol forcing (Boé et al., 2020a), although the driving global models also have a cold bias (Vautard et al., 2021). Cold biases for recent decades are also found in Med-CORDEX simulations (Dell’Aquila et al., 2018) and by RCM simulations over the southern part of the Mediterranean, Middle East and North Africa region (Almazroui, 2016; Almazroui et al., 2016a, b; Zittis and Hadjinicolaou, 2017; Ozturk et al., 2018), although higher resolution, new bare soil albedo and modified aerosol parametrization significantly improve the results (Bucchignani et al., 2016a, b, 2018). Despite large differences in the multi-model mean trend (Figure 10.20g), in most of the land points the observed trend lies within the model range in all ensembles. For the SST bias exhibited by coupled RCMs the choice of driving global model has the largest impact (Darmaraki et al., 2019; Soto-Navarro et al., 2020).

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Brun, F. et al., 2015: Seasonal changes in surface albedo of Himalayan glaciers from MODIS data and links with the annual mass balance. The Cryosphere, 9(1), 341–355, doi: 10.5194/tc-9-341-2015.

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Chrysoulakis, N., Z. Mitraka, and N. Gorelick, 2019: Exploiting satellite observations for global surface albedo trends monitoring. Theoretical and Applied Climatology, 137(1–2), 1171–1179, doi: 10.1007/s00704-018-2663-6.

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Letcher, T.W. and J.R. Minder, 2017: The Simulated Response of Diurnal Mountain Winds to Regionally Enhanced Warming Caused by the Snow Albedo Feedback. Journal of the Atmospheric Sciences, 74(1), 49–67, doi: 10.1175/jas-d-16-0158.1.

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Minder, J.R., T.W. Letcher, and S.M.K. Skiles, 2016: An evaluation of high-resolution regional climate model simulations of snow cover and albedo over the Rocky Mountains, with implications for the simulated snow-albedo feedback. Journal of Geophysical Research: Atmospheres, 121(15), 9069–9088, doi: 10.1002/2016jd024995.

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Walton, D.B., A. Hall, N. Berg, M. Schwartz, and F. Sun, 2017: Incorporating Snow Albedo Feedback into Downscaled Temperature and Snow Cover Projections for California’s Sierra Nevada. Journal of Climate, 30(4), 1417–1438, doi: 10.1175/jcli-d-16-0168.1.

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Winter, K.J.-P.M., S. Kotlarski, S.C. Scherrer, and C. Schär, 2017: The Alpine snow-albedo feedback in regional climate models. Climate Dynamics, 48(3–4), 1109–1124, doi: 10.1007/s00382-016-3130-7.

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Thermodynamic responses and feedbacks also occur through surface processes. For instance, observations and model simulations show that temperature increases, including extreme temperatures, are amplified in areas where seasonal snow cover is reduced due to decreases in surface albedo (see Section 11.3.1). In some mid-latitude areas, temperature increases are amplified by the higher atmospheric evaporative demand (Fu and Feng, 2014; Vicente-Serrano et al., 2020a) that results in a drying of soils in some regions (Section 11.6), leading to increased sensible heat fluxes (soil-moisturetemperature feedback, see Sections 11.1.6 and 11.3.1 for more background). Other thermodynamic feedback processes include changes in the water-use efficiency of plants under enhanced atmospheric carbon dioxide (CO2) concentrations that can reduce the overall transpiration, and thus also enhance temperature in projections (Sections 8.2.3.3, 11.1.6, 11.3 and 11.6).

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In addition to regional forcings, regional feedback mechanisms can also substantially affect extremes (high confidence) (Sections 11.3, 11.4 and 11.6). In particular, soil moisture feedbacks play an important role for extremes in several mid-latitude regions, leading to a marked additional warming of hot extremes compared to mean global warming (Seneviratne et al., 2016; Bathiany et al., 2018; Miralles et al., 2019), which is superimposed on the known land–sea contrast in mean warming (Vogel et al., 2017). Soil moisture–atmosphere feedbacks also affect drought development (Section 11.6). Additionally, effects of land surface conditions on circulation patterns have also been reported (Koster et al., 2016; Sato and Nakamura, 2019). These regional feedbacks are also associated with substantial spread in models (Section 11.3), and contribute to the identified higher spread of regional projections of temperature extremes as a function of global warming, compared with the spread resulting from the differences in projected global warming (global transient climate responses) in climate models (Seneviratne and Hauser, 2020). In addition, there are also feedbacks between soil moisture content and precipitation occurrence, generally characterized by negative spatial feedbacks and positive local feedbacks (Taylor et al., 2012; Guillod et al., 2015). Climate model projections suggest that these feedbacks are relevant for projected changes in heavy precipitation (Seneviratne et al., 2013). However, there is evidence that climate models do not capture the correct sign of the soil moisture–precipitation feedbacks in several regions, in particular spatially, and/or in some cases also temporally (Taylor et al., 2012; Moon et al., 2019). In the Northern Hemisphere high latitudes, the snow- and ice-albedo feedback, along with other factors, is projected to largely amplify temperature increases (e.g., Pithan and Mauritsen, 2014), although the effect on temperature extremes is still unclear. It also remains unclear whether snow-albedo feedbacks in mountainous regions might have an effect on temperature and precipitation extremes (e.g., Gobiet et al., 2014). However, these feedbacks play an important role in projected changes in high-latitude warming (Hall and Qu, 2006), and, in particular, in changes in cold extremes in these regions (Section 11.3).

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In summary, regional forcings and feedbacks – in particular those associated with land use and aerosol forcings – and soil-moisture–temperature, soil moisture–precipitation, and snow/ice–albedo–temperature feedbacks, play an important role in modulating regional changes in extremes. These can also lead to a higher warming of extreme temperatures compared to mean temperature (high confidence), and possibly cooling in some regions (medium confidence). However, there is only medium confidence in the representation of the associated processes in state-of-the-art ESMs.

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The SREX (IPCC, 2012) and AR5 (IPCC, 2014) concluded that greenhouse gas forcing is the dominant factor for the increases in the intensity, frequency, and duration of warm extremes and the decrease in those of cold extremes. This general global-scale warming is modulated by large-scale atmospheric circulation patterns, as well as by feedbacks such as soil moisture-evapotranspiration–temperature and snow/ice-albedo–temperature feedbacks, and local forcings such as land-use change or changes in aerosol concentrations at the regional and local scales (Sections 11.1.5 and 11.1.6, and Box 11.1). Therefore, changes in temperature extremes at regional and local scales can have heterogeneous spatial distributions. Changes in the magnitudes (or intensities) of extreme temperatures are often larger than changes in global surface temperature, because of larger warming on land than on the ocean surface (Section 2.3.1.1), and because of feedbacks, though they are of similar magnitude to changes in the local mean temperature (Figure 11.2).

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Land–atmosphere feedbacks strongly modulate regional- and local-scale changes in temperature extremes (high confidence) (Section 11.1.6; Seneviratne et al., 2013; Lemordant et al., 2016; Donat et al., 2017; Sillmann et al., 2017b; Hirsch et al., 2019). This effect is particularly notable in mid-latitude regions where the drying of soil moisture amplifies high temperatures, especially through increases in sensible heat flux (Whan et al., 2015; Douville et al., 2016; Vogel et al., 2017). Land–atmosphere feedbacks amplifying temperature extremes also include boundary-layer feedbacks and effects on atmospheric circulation (Miralles et al., 2014a; Schumacher et al., 2019). Soil-moisture–temperature feedbacks affect past and present-day heatwaves in observations and model simulations, both locally (Miralles et al., 2014a; Cowan et al., 2016, 2020; Hauser et al., 2016; Meehl et al., 2016; Wehrli et al., 2019) and beyond the regions of feedback occurrence through changes in regional circulation patterns (Stéfanon et al., 2014; Koster et al., 2016; Sato and Nakamura, 2019). The uncertainty due to the representation of land–atmosphere feedbacks in ESMs is a cause of discrepancy between observations and simulations (Clark et al., 2006; Mueller and Seneviratne, 2014; Meehl et al., 2016). The decrease of plant transpiration or the increase of stomata resistance under enhanced CO2 concentrations is a direct CO2 forcing of land temperatures (warming due to reduced evaporative cooling), which contributes to higher warming on land (Lemordant et al., 2016; Vicente-Serrano et al., 2020b). The snow/ice-albedo feedback plays an important role in amplifying temperature variability in the high latitudes (Diro et al., 2018) and can be the largest contributor to the rapid warming of cold extremes in the mid- and high latitudes of the Northern Hemisphere (Gross et al., 2020).

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Projected warming is larger for TNn and exhibits strong equator-to-pole amplification, similar to the warming of boreal winter mean temperatures. The warming of TXx is more uniform over land and does not exhibit this behaviour (Figure 11.11). The warming of temperature extremes on global and regional scales tends to scale linearly with global warming (Section 11.1.4; Fischer et al., 2014; Seneviratne et al., 2016; Wartenburger et al., 2017; Li et al., 2021; see also SR1.5, Chapter 3). In the mid-latitudes, the rate of warming of hot extremes can be as large as twice the rate of global warming (Figure 11.11). In the Arctic winter, the rate of warming of the temperature of the coldest nights is about three times the rate of global warming (Appendix, Figure 11.A.1). Projected changes in temperature extremes can deviate from projected changes in annual mean warming in the same regions (Figures 11.3, 11.A.1 and 11.A.2; Di Luca et al., 2020b; Wehner, 2020) due to the additional processes that control the response of regional extremes, including, in particular, soil moisture–evapotranspiration–temperature feedbacks for hot extremes in the mid-latitudes and subtropical regions, and snow/ice–albedo–temperature feedbacks in high-latitude regions.

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Soil moisture shows an important correlation with precipitation variability (Khong et al., 2015; Seager et al., 2019), but ET also plays a substantial role in further depleting moisture from soils, in particular in humid regions during periods of precipitation deficits (Teuling et al., 2013; Padrón et al., 2020). In addition, soil moisture plays a role in drought self-intensification under dry conditions in which ET is decreased and leads to higher AED (Miralles et al., 2019), an effect that can also contribute to triggering flash droughts (Otkin et al., 2016, 2018; DeAngelis et al., 2020; Pendergrass et al., 2020). If soil moisture becomes limited, ET is reduced, which may decrease the rate of soil drying, but can also lead to further atmospheric dryness through various feedback loops (Seneviratne et al., 2010; Miralles et al., 2014a, 2019; Teuling, 2018; Vogel et al., 2018; S. Zhou et al., 2019; Liu et al., 2020). The process is complex since vegetation cover plays a role in modulating albedo and in providing access to deeper stores of water (both in the soil and groundwater). Also, changes in land cover and in plant phenology may alter ET (Sterling et al., 2013; Woodward et al., 2014; Frank et al., 2015; Döll et al., 2016; Ukkola et al., 2016; Trancoso et al., 2017; Hao et al., 2019; Lian et al., 2020). Snow depth has strong and direct impacts on soil moisture in many systems (Gergel et al., 2017; Williams et al., 2020).

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Davin, E.L., S.I. Seneviratne, P. Ciais, A. Olioso, and T. Wang, 2014: Preferential cooling of hot extremes from cropland albedo management. Proceedings of the National Academy of Sciences, 111(27), 9757–9761, doi: 10.1073/pnas.1317323111.

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Hall, A. and X. Qu, 2006: Using the current seasonal cycle to constrain snow albedo feedback in future climate change. Geophysical Research Letters, 33(3), L03502, doi: 10.1029/2005gl025127.

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Human activities influence the regional water cycle directly through modifying and exploiting stores and flows from rivers, lakes and groundwater and by altering land cover characteristics. These actions alter surface energy and water balances through changes in permeability, surface albedo, evapotranspiration, surface roughness and leaf area. Direct redistribution of water by human activities for domestic, agricultural and industrial use of about 24,000 km3yr–1 (Figure 8.1) is equivalent to half the global river discharge or double the global groundwater recharge each year (Abbott et al., 2019). Since AR5, both modelling studies and observations have demonstrated that land use change can drive local and remote responses in precipitation and river flow by altering the surface energy balance, moisture advection and recycling, land sea thermal contrast and associated wind patterns (Alter et al. , 2015; Wey et al. , 2015; De Vrese et al. , 2016; Pei et al. , 2016; Wang-Erlandsson et al. , 2018; Vicente-Serrano et al. , 2019). There is robust evidence that a warming climate combined with direct human demand for groundwater will deplete groundwater resources in already dry regions (Wada and Bierkens, 2014; D’Odorico et al., 2018; Jia et al., 2019).

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Land surface processes determine the partitioning of net surface radiation into sensible, latent and ground heat fluxes, the partitioning of precipitation into evapotranspiration and runoff, and the net terrestrial carbon flux at the Earth’s surface. They are relevant for simulating the terrestrial water cycle responses to climate change, as well as the response to land use change (FAQ 8.1). Even basic land surface properties such as albedo (Terray et al., 2018) or the ratio of transpiration to total evaporation (Chang et al., 2018) still need to be improved in state-of-the-art coupled GCMs. Runoff sensitivities are also not well constrained in these models, which display a large spread for the present-day climate, influencing simulated changes under global warming (Lehner et al., 2019). Earth System Models (ESMs) incorporate some combined biophysical and biogeochemical processes to a limited extent, and many relevant processes about how plants and soils interactively respond to climate changes are yet to be considered (e.g., Y. Liu et al., 2020). Consequently, land surface processes and their atmospheric coupling contribute to the range in water cycle projections (Jia et al., 2019).

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Non-linearities in the climate response are thought to arise from multiple factors. These include state-dependent ice-albedo feedback and its potential influence on Northern Hemisphere (NH) storm tracks (Peings and Magnusdottir, 2014; Semenov and Latif, 2015; see also Cross-Chapter Box 10.1 and Section 8.6.1.2); a state-dependent sensitivity of tropical precipitation to increased SST (Schewe and Levermann, 2017; He et al., 2018); a complex response of the Atlantic meridional overturning circulation (AMOC; Sections 9.2.4.1 and 8.6.1.1) and its model- and magnitude dependent teleconnections with regional temperature and precipitation (Kageyama et al., 2013; Jackson et al., 2015; Qasmi et al., 2017, 2020); and other atmospheric and terrestrial (Section 8.5.3.2) processes such as cloud and land surface feedbacks (Ceppi and Gregory, 2017; King, 2019). The response of convective precipitation may exhibit non-linearities because it is itself modulated by both dynamics and atmospheric water content, each responding independently to warming (Chadwick and Good, 2013; Neupane and Cook, 2013).

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CMIP5 and CMIP6 models, some of which include dynamic vegetation schemes, cannot simulate the magnitude, nor the spatial extent, of greening and precipitation change associated with the last Green Sahara under standard mid-Holocene (6,000 years ago) boundary conditions (high confidence) (Figure 3.11; Harrison et al. , 2014; Tierney et al. , 2017; Brierley et al. , 2020). This result remains unchanged since AR4 (Jansen et al., 2007). This may be due to climatological biases in the models (Harrison et al., 2015) or could imply that the strength of the feedbacks between vegetation and the water cycle in the models is too weak (Hopcroft et al., 2017). To date, climate models still only produce the amount and spatial extent of rainfall that is needed to sustain a Green Sahara if they are given prescribed changes in the land surface, such as albedo, soil moisture, vegetation cover and/or dust emissions (Pausata et al., 2016; Skinner and Poulsen, 2016; Tierney et al., 2017).

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The magnitude of hydrological disruption for both the initiation and termination of SRM depends on the method used, as well as the strength and duration of its implementation (Ekholm and Korhonen, 2016; Irvine et al., 2019). Under abrupt SRM implementation, hydrological shifts are rapid, occurring within the first decade (Crook et al., 2015). Artificial enhancement of albedo in Northern Hemisphere desert regions causes a southward shift in the Hadley Cell and ITCZ, and extreme drying in the northern tropics (Crook et al., 2015). Uniform or tropical stratospheric sulfate injection weakens the African and Asian summer monsoons and causes drying in the Amazon (Robock et al., 2008; Crook et al., 2015; Dagon and Schrag, 2016). Changes in evapotranspiration can produce large deficits or surpluses in soil moisture and runoff in different regions and seasons (Dagon and Schrag, 2016).

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Bartlett, P.A. and D.L. Verseghy, 2015: Modified treatment of intercepted snow improves the simulated forest albedo in the Canadian Land Surface Scheme. Hydrological Processes, 29(14), 3208–3226, doi: 10.1002/hyp.10431.

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Loranty, M.M., L.T. Berner, S.J. Goetz, Y. Jin, and J.T. Randerson, 2014: Vegetation controls on northern high latitude snow-albedo feedback: Observations and CMIP5 model simulations. Global Change Biology, 20(2), 594–606, doi: 10.1111/gcb.12391.

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Stephens, G.L. et al., 2015: The albedo of earth. Reviews of Geophysics, 53(1), 141–163, doi: 10.1002/2014rg000449.

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Terray, P. et al., 2018: Towards a realistic simulation of boreal summer tropical rainfall climatology in state-of-the-art coupled models: role of the background snow-free land albedo. Climate Dynamics, 50(9–10), 3413–3439, doi: 10.1007/s00382-017-3812-9.

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Thackeray, C.W. and C.G. Fletcher, 2016: Snow albedo feedback. Progress in Physical Geography: Earth and Environment, 40(3), 392–408, doi: 10.1177/0309133315620999.

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Thackeray, C.W., C.G. Fletcher, and C. Derksen, 2015: Quantifying the skill of CMIP5 models in simulating seasonal albedo and snow cover evolution. Journal of Geophysical Research: Atmospheres, 120(12), 5831–5849, doi: 10.1002/2015jd023325.

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Wang, L. et al., 2016: Investigating the spread in surface albedo for snow-covered forests in CMIP5 models. Journal of Geophysical Research: Atmospheres, 121(3), 1104–1119, doi: 10.1002/2015jd023824.

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The SRCCL also assessed how changes in land conditions affect global and regional climate. It found that changes in land cover have led to both a net release of CO2, contributing to global warming, and an increase in global land albedo, causing surface cooling. However, the report estimated that the resulting net effect on globally averaged surface temperature was small over the historical period (medium confidence).

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Each modelling group has its own strategy and, after AR5, a survey was conducted to understand the tuning approach used in 23 CMIP5 modelling centres. The results are discussed in Hourdin et al. (2017), which stresses that the behaviour of ESMs depends on the tuning strategy. An important recommendation is that the calibration steps that lead to particular model tuning should be carefully documented. In CMIP6 each modelling group now describes the three levels of tuning, both for the complete ESM and for the individual components (available at https://explore.es-doc.organd in the published model descriptions, Annex II: Models). The most important global tuning target for CMIP6 models is the net top-of-the-atmosphere (TOA) heat flux and its radiative components. Other global targets include: the decomposition of the energy fluxes at TOA into a clear sky component and a component due to the radiative effect of clouds, global mean air and ocean temperature, sea ice extent, sea ice volume, glacial mass balance, and the global root mean square error of precipitation. The TOA heat flux balance is achieved using a diversity of approaches, usually unique to each modelling group. Adjustments are made for parameters associated with uncertain or poorly constrained processes (Schmidt et al., 2017), for example the aerosol indirect effects, adjustments to ocean albedo, marine dimethyl sulfide (DMS) parameterization, or cloud properties (Mauritsen and Roeckner, 2020).

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Charlson, R.J., J.E. Lovelock, M.O. Andreae, and S.G. Warren, 1987: Oceanic phytoplankton, atmospheric sulphur, cloud albedo and climate. Nature, 326(6114), 655–661, doi: 10.1038/326655a0.

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Climate–DMS feedback: Dimethyl sulphide (DMS) is produced by marine phytoplankton and is emitted to the atmosphere where it can lead to the subsequent formation of sulphate aerosol and CCN (Section 6.2.2.5). Changes in DMS emissions from ocean could feedback on climate through their response to changes in temperature, solar radiation, ocean mixed-layer depth, sea ice extent, wind speed, nutrient recycling or shifts in marine ecosystems due to ocean acidification and climate change, or atmospheric processing of DMS into CCN (Heinze et al., 2019). Models with varying degrees of representation of the relevant biogeochemical processes and effects on DMS fluxes produce diverging estimates of changes in DMS emissions strength under climate change resulting in large uncertainties in the DMS–sulphate–cloud albedo feedback (Bopp et al., 2004; Kloster et al., 2007; Gabric et al., 2013). In AR5, the climate-DMS feedback parameter was estimated to be –0.02 W m–2°C–1based on a single model. Since AR5, new modelling studies using empirical relationships between pH and total DMS production find that global DMS emissions decrease due to combined ocean acidification and climate change, leading to a strong positive climate feedback (Six et al., 2013; Schwinger et al., 2017). However, another study argues for a much weaker positive feedback globally due to complex and compensating regional changes in marine ecosystems (S. Wang et al., 2018). The AerChemMIP multi-model analysis suggests small positive feedback (Table 6.8), consistent with these recent studies, but with large uncertainties in the magnitude of α .

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Latham, J. et al., 2008: Global temperature stabilization via controlled albedo enhancement of low-level maritime clouds. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 366(1882), 3969–3987, doi: 10.1098/rsta.2008.0137.

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Lee, Y.H. et al., 2013: Evaluation of preindustrial to present-day black carbon and its albedo forcing from Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP). Atmospheric Chemistry and Physics, 13(5), 2607–2634, doi: 10.5194/acp-13-2607-2013.

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Twomey, S., 1977: The Influence of Pollution on the Shortwave Albedo of Clouds. Journal of the Atmospheric Sciences, 34(7), 1149–1152, doi: 10.1175/1520-0469(1977)034<1149:tiopot>2.0.co;2.

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Wang, H., P.J. Rasch, and G. Feingold, 2011: Manipulating marine stratocumulus cloud amount and albedo: a process-modelling study of aerosol–cloud–precipitation interactions in response to injection of cloud condensation nuclei. Atmospheric Chemistry and Physics, 11(9), 4237–4249, doi: 10.5194/acp-11-4237-2011.

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where x represents a variable of the Earth system that has a direct effect on the energy budget at the TOA. The sum of the feedback terms (i.e., α in Equation 7.1) governs Earth’s equilibrium GSAT response to an imposed ERF. In previous assessments, α and the related ECS have been associated with a distinct set of physical processes (Planck response and changes in water vapour, lapse rate, surface albedo, and clouds; Charney et al., 1979). In this assessment, a more general definition of α and ECS is adopted such that they include additional Earth system processes that act across many time scales (e.g., changes in natural aerosol emissions or vegetation). Because, in our assessment, these additional processes sum to a near-zero value, including these additional processes does not change the assessed central value of ECS but does affect its assessed uncertainty range (Section 7.4.2). Note that there is no standardized notation or sign convention for the feedback parameter in the literature. Here the convention is used that the sum of all feedback terms (the net feedback parameter, α ) is negative for a stable climate that radiates additional energy to space with a GSAT increase, with a more negative value of α corresponding to a stronger radiative response and thus a smaller GSAT change required to balance a change in ERF (Equation 7.1).

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Using the definition of ERF from (Section 7.1, the adjustment in land-surface temperature is excluded from the definition of ERF, but changes in vegetation and snow cover (resulting from land-use change) are included (Boisier et al., 2013). Land-use change in the mid-latitudes induces a substantial amplifying adjustment in snow cover. Few climate model studies have attempted to quantify the ERF of land-use change. T. Andrews et al. (2017) calculated a very large surface albedo ERF (–0.47 W m–2) from 1860 to 2005 in the HadGEM2-ES model, although they did not separate out the surface albedo change from snow cover change. HadGEM2-ES is known to overestimate the amount of boreal trees and shrubs in the unperturbed state (Collins et al., 2011) so will tend to overestimate the ERF associated with land-use change. The increases in dust in HadGEM2-ES contributed an extra –0.25 W m–2, whereas cloud cover changes added a small positive adjustment (0.15 W m–2) consistent with a reduction in transpiration. A multi-model quantification of land-use forcing in CMIP6 models (excluding one outlier) (Smith et al., 2020b) found an IRF of –0.15 ± 0.12 W m–2(1850–2014), and an ERF (correcting for land-surface temperature change) of –0.11 ± 0.09 W m–2. This shows a small positive adjustment term (mainly from a reduction in cloud cover). CMIP5 models show an IRF of –0.11 [–0.16 to –0.04] W m–2(1850–2000) after excluding unrealistic models (Lejeune et al., 2020).

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The contribution of land-use change to albedo changes has recently been investigated using MODIS and AVHRR to attribute surface albedo to geographically specific land-cover types (Ghimire et al., 2014). When combined with a historical land-use map (Hurtt et al., 2011) this gives a SARF of –0.15 ± 0.01 W m–2 for the period 1700–2005, of which approximately –0.12 W m–2 is from 1850. This study accounted for correlations between vegetation type and snow cover, but not the adjustment in snow cover identified in T. Andrews et al. (2017).

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Because the CMIP5 and CMIP6 modelling studies are in agreement with Ghimire et al. (2014), that study is used as the assessed albedo ERF. Adding the irrigation effect to this gives an overall assessment of the ERF from land-use change of –0.20 ± 0.10 W m–2(medium confidence). Changes in ERF since 2014 are assumed to be small compared to the uncertainty, so this ERF applies to the period 1750–2019. The uncertainty range includes uncertainties in the adjustments.

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Since AR5 there has been progress in the understanding of the physical state and processes in snow that govern the albedo reduction by black carbon (BC). The SROCC (IPCC, 2019a) assessed that there is high confidence that darkening of snow by deposition of BC and other light-absorbing aerosol species increases the rate of snow melt (Section 2.2 in Hock et al., 2019; Section 3.4 in Meredith et al., 2019). C. He et al. (2018) found that taking into account both the non-spherical shape of snow grains and internal mixing of BC in snow significantly altered the effects of BC on snow albedo. The reductions of snow albedo by dust and BC have been measured and characterized in the Arctic, the Tibetan Plateau, and mid-latitude regions subject to seasonal snowfall, including North America and northern and eastern Asia (Qian et al., 2015).

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This section provides an overall assessment of individual feedback parameters, α x, by combining different lines of evidence from observations, theory, process models and ESMs. To achieve this, we review the understanding of the key processes governing the feedbacks, why the feedback estimates differ among models, studies or approaches, and the extent to which these approaches yield consistent results. The individual terms assessed are the Planck response (Section 7.4.2.1) and feedbacks associated with changes in water vapour and lapse rate (Section 7.4.2.2), surface albedo (Section 7.4.2.3), clouds (Section 7.4.2.4), biogeophysical and non-CO2 biogeochemical processes (Section 7.4.2.5), and ice sheets (Section 7.4.2.6). A synthesis is provided in (Section 7.4.2.7. Climate feedbacks in CMIP6 models are then evaluated in (Section 7.4.2.8, with an explanation of how they have been incorporated into the assessment.

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Surface albedo is determined primarily by reflectance at Earth’s surface, but also by the spectral and angular distribution of incident solar radiation. Changes in surface albedo result in changes in planetary albedo that are roughly reduced by two-thirds, owing to atmospheric absorption and scattering, with variability and uncertainty arising primarily from clouds (Bender, 2011; Donohoe and Battisti, 2011; Block and Mauritsen, 2013). Temperature change induces surface-albedo change through several direct and indirect means. In the present climate and at multi-decadal time scales, the largest contributions by far are changes in the extent of sea ice and seasonal snow cover, as these media are highly reflective and are located in regions that are close to the melting temperature (Sections 2.3.2.1 and 2.3.2.2). Reduced snow cover on sea ice may contribute as much to albedo feedback as reduced extent of sea ice (Zhang et al., 2019). Changes in the snow metamorphic rate, which generally reduces snow albedo with warmer temperature, and warming-induced consolidation of light-absorbing impurities near the surface, also contribute secondarily to the albedo feedback (Flanner and Zender, 2006; Qu and Hall, 2007; Doherty et al., 2013; Tuzet et al., 2017). Other contributors to albedo change include vegetation state (assessed separately in (Section 7.4.2.5), soil wetness and ocean roughness.

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Several studies have attempted to derive surface-albedo feedback from observations of multi-decadal changes in climate, but only over limited spatial and inconsistent temporal domains, inhibiting a purely observational synthesis of global surface-albedo feedback ( α A). Flanner et al. (2011) applied satellite observations to determine that the northern hemisphere (NH) cryosphere contribution to global α Aover the period 1979–2008 was 0.48 [likely range 0.29 to 0.78] W m–2°C–1, with roughly equal contributions from changes in land snow cover and sea ice. Since AR5, and over similar periods of observation, Crook and Forster (2014) found an estimate of 0.8 ± 0.3 W m–2°C–1(one standard deviation) for the total NH extratropical surface-albedo feedback, when averaged over global surface area. For Arctic sea ice alone, Pistone et al. (2014) and Cao et al. (2015) estimated the contribution to global α Ato be 0.31 ± 0.04 W m–2°C–1(one standard deviation) and 0.31 ± 0.08 W m–2°C–1(one standard deviation), respectively, whereas Donohoe et al. (2020) estimated it to be only 0.16 ± 0.04 W m–2°C–1(one standard deviation). Much of this discrepancy can be traced to different techniques and data used for assessing the attenuation of surface-albedo change by Arctic clouds. For the NH land snow, Chen et al. (2016) estimated that observed changes during 1982–2013 contributed (after converting from NH temperature change to global mean temperature change) by 0.1 W m–2°C–1to global α A, smaller than the estimate of 0.24 W m–2°C–1from Flanner et al. (2011). The contribution of the Southern Hemisphere (SH) to global α A is expected to be small because seasonal snow cover extent in the SH is limited, and trends in SH sea ice extent are relatively flat over much of the satellite record (Section 2.3.2).

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CMIP5 and CMIP6 models show moderate spread in global α A, determined from century time scale changes(Qu and Hall, 2014; Schneider et al., 2018; Thackeray and Hall, 2019; Zelinka et al., 2020), owing to variations in modelled sea ice loss and snow cover response in boreal forest regions. The multi-model mean global-scale α A(from all contributions) over the 21st century in CMIP5 models under the RCP8.5 scenario was derived by Schneider et al. (2018) to be 0.40 ± 0.10 W m–2°C–1(one standard deviation). Moreover, they found that modelled α Adoes not decline over the 21st century, despite large losses of snow and sea ice, though a weakened feedback is apparent after 2100. Using the idealized abrupt 4xCO2 , as for the other feedbacks, the estimate of the global-scale albedo feedback in the CMIP5 models is 0.35 ± 0.08 W m–2°C–1(one standard deviation; Vial et al., 2013; Caldwell et al., 2016). The CMIP6 multi-model mean varies from 0.3 to 0.5 W m–2°C–1depending on the kernel used (Zelinka et al., 2020). Donohoe et al. (2020) derived a multi-model mean α Aand its inter-model spread of 0.37 ± 0.19 W m–2°C–1from the CMIP5abrupt 4xCO2 ensemble, employing model-specific estimates of atmospheric attenuation and thereby avoiding bias associated with use of a single radiative kernel.

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Based on the multiple lines of evidence presented above that include observations, CMIP5 and CMIP6 models and theory, the global surface-albedo feedback is assessed to be positive with high confidence. The basic phenomena that drive this feedback are well understood and the different studies cover a large variety of hypotheses or behaviours, including how the evolution of clouds affects this feedback. The value of the global surface-albedo feedback is assessed to be α A= 0.35 W m–2°C–1, with avery likely range from 0.10 to 0.60 W m–2°C–1and a likely range from 0.25 to 0.45 W m–2°C–1 with high confidence.

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In the global energy budget at TOA, clouds affect shortwave (SW) radiation by reflecting sunlight due to their high albedo (cooling the climate system) and also longwave (LW) radiation by absorbing the energy from the surface and emitting at a lower temperature to space, that is, contributing to the greenhouse effect, warming the climate system. In general, the greenhouse effect of clouds strengthens with height whereas the SW reflection depends on the cloud optical properties. The effects of clouds on Earth’s energy budget are measured by the cloud radiative effect (CRE), which is the difference in the TOA radiation between clear and all skies (see (Section 7.2.1). In the present climate, the SW CRE tends to be compensated by the LW CRE over the equatorial warm pool, leading to the net CRE pattern showing large negative values over the eastern part of the subtropical ocean and the extratropical ocean due to the dominant influence of highly reflective marine low-clouds.

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Since AR5, several studies have confirmed that a shift from tundra to boreal forests and the associated albedo change leads to increased warming in Northern Hemisphere high latitudes (high confidence) (Willeit et al., 2014; W. Zhang et al., 2018; Armstrong et al., 2019). However, regional modelling indicates that vegetation feedbacks may act to cool climate in the Mediterranean (Alo and Anagnostou, 2017), and in the tropics and subtropics the regional response is in general not consistent across models. On a global scale, several modelling studies have either carried out a feedback analysis (Stocker et al., 2013; Willeit et al., 2014) or presented simulations that allow a feedback parameter to be estimated (O’ishi et al., 2009; Armstrong et al., 2019), in such a way that the physiological response can be accounted for as a forcing rather than a feedback. The central estimates of the biogeophysical feedback parameter from these studies range from close to zero (Willeit et al., 2014) to +0.13 W m–2°C–1(Stocker et al., 2013). An additional line of evidence comes from the mid-Pliocene warm period (MPWP, Chapter 2, Cross-Chapter Box 2.1), for which paleoclimate proxies provide evidence of vegetation distribution and CO2 concentrations. Model simulations that include various combinations of modern versus MPWP vegetation and CO2 allow an associated feedback parameter to be estimated, as long as account is also taken of the orographic forcing (Lunt et al., 2010, 2012b). This approach has the advantage over pure modelling studies in that the reconstructed vegetation is based on (paleoclimate) observations, and is in equilibrium with the CO2 forcing. However, there are uncertainties in the vegetation reconstruction in regions with little or no proxy data, and it is uncertain how much of the vegetation change is associated with the physiological response to CO2. This paleoclimate approach gives an estimate for the biogeophysical feedback parameter of +0.3 W m–2°C–1.

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Earth’s ice sheets (Greenland and Antarctica) are sensitive to climate change (Section 9.4; Pattyn et al., 2018). Their time evolution is determined by both their surface mass balance and ice dynamic processes, with the latter being particularly important for the West Antarctic Ice Sheet. Surface mass balance depends on the net energy and hydrological fluxes at their surface, and there are mechanisms of ice-sheet instability that depend on ocean temperatures and basal melt rates (Section 9.4.1.1). The presence of ice sheets affects Earth’s radiative budget, hydrology, and atmospheric circulation due to their characteristic high albedo, low roughness length, and high altitude, and they influence ocean circulation through freshwater input from calving and melt (e.g., Fyke et al., 2018). Ice-sheet changes also modify surface albedo through the attendant change in sea level and therefore land area (Abe-Ouchi et al., 2015). The time scale for ice sheets to reach equilibrium is of the order of thousands of years (Clark et al., 2016). Due to the long time scales involved, it is a major challenge to run coupled climate–ice sheet models to equilibrium, and as a result, long-term simulations are often carried out with lower complexity models, and/or are asynchronously coupled.

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There are several modelling studies since AR5 in which ESMs of varying complexity have been used to explore temperature dependence of feedbacks, either under modern (Hansen et al., 2013; Jonko et al., 2013; Meraner et al., 2013; Good et al., 2015; Duan et al., 2019; Mauritsen et al., 2019; Rohrschneider et al., 2019; Stolpe et al., 2019; Bloch-Johnson et al., 2020; Rugenstein et al., 2020) or paleo (Caballero and Huber, 2013; Zhu et al., 2019a) climate conditions, typically by carrying out multiple simulations across successive CO2 doublings. A non-linear temperature response to these successive doublings may be partly due to forcing that increases more (or less) than expected from a purely logarithmic dependence (Section 7.3.2; Etminan et al., 2016), and partly due to state-dependence in feedbacks; however, not all modelling studies have partitioned the non-linearities in temperature response between these two effects. Nonetheless, there is general agreement among ESMs that the net feedback parameter, α , increases (i.e., becomes less negative) as temperature increases from pre-industrial levels (i.e., sensitivity to forcing increases as temperature increases; e.g., Meraner et al., 2013; see Figure 7.11). The associated increase in sensitivity to forcing is, in most models, due to the water vapour (Section 7.4.2.2) and cloud (Section 7.4.2.4) feedback parameters increasing with warming (Caballero and Huber, 2013; Meraner et al., 2013; Zhu et al., 2019a; Rugenstein et al., 2020; Sherwood et al., 2020). These changes are offset partially by the surface-albedo feedback parameter decreasing (Jonko et al., 2013; Meraner et al., 2013; Rugenstein et al., 2020), as a consequence of a reduced amount of snow and sea ice cover in a much warmer climate. At the same time, there is little change in the Planck response (Section 7.4.2.1), which has been shown in one model to be due to competing effects from increasing Planck emission at warmer temperatures and decreasing planetary emissivity due to increased CO2 and water vapour (Mauritsen et al., 2019). Analysis of the spatial patterns of the non-linearities in temperature response (Good et al., 2015) suggests that these patterns are linked to a reduced weakening of the AMOC, and changes to evapotranspiration. The temperature dependence of α is also found in model simulations of high-CO2 paleoclimates (Caballero and Huber, 2013; Zhu et al., 2019a). The temperature dependence is not only evident at very high CO2 concentrations in excess of 4×CO2, but also apparent in the difference in temperature response to a 2×CO2 forcing compared with to a 4×CO2 forcing (Mauritsen et al., 2019; Rugenstein et al., 2020), and as such is relevant for interpreting century-scale climate projections.

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Several processes contribute to polar amplification under greenhouse gas forcing, including the loss of sea ice and snow (an amplifying surface-albedo feedback), the confinement of warming to near the surface in the polar atmosphere (an amplifying lapse-rate feedback), and increases in poleward atmospheric and oceanic heat transport (Pithan and Mauritsen, 2014; Goosse et al., 2018; Dai et al., 2019; Feldl et al., 2020). Modelling and process studies since AR5 have led to an improved understanding of the combined effect of these different processes in driving polar amplification and how they differ between the hemispheres.

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Idealized modelling studies suggest that polar amplification would occur even in the absence of any amplifying polar surface-albedo or lapse-rate feedbacks owing to changes in poleward atmospheric heat transport under global warming (Hall, 2004; Alexeev et al., 2005; Graversen and Wang, 2009; Alexeev and Jackson, 2013; Graversen et al., 2014; Roe et al., 2015; Merlis and Henry, 2018; Armour et al., 2019). Poleward heat transport changes reflect compensating changes in the transport of latent energy (moisture) and dry-static energy (sum of sensible and potential energy) by atmospheric circulations (Alexeev et al., 2005; Held and Soden, 2006; Hwang and Frierson, 2010; Hwang et al., 2011; Kay et al., 2012; Huang and Zhang, 2014; Feldl et al., 2017a; Donohoe et al., 2020). ESMs project that within the mid-latitudes, where eddies dominate the heat transport, an increase in poleward latent energy transport arises from an increase in the equator-to-pole gradient in atmospheric moisture with global warming, with moisture in the tropics increasing more than at the poles as described by the Clausius–Clapeyron relation (Section 8.2). This change is partially compensated by a decrease in dry-static energy transport arising from a weakening of the equator-to-pole temperature gradient as the polar regions warm more than the tropics.

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Regional energy budget analyses are commonly used to diagnose the relative contributions of radiative feedbacks and energy fluxes to polar amplification as projected by ESMs under increased CO2 concentrations (Figure 7.12; Feldl and Roe, 2013; Pithan and Mauritsen, 2014; Goosse et al., 2018; Stuecker et al., 2018). These analyses suggest that a primary cause of amplified Arctic warming in ESMs is the latitudinal structure of radiative feedbacks, which warm the Arctic more than the tropics (Figure 7.12b), and enhanced latent energy transport into the Arctic. That net atmospheric heat transport into the Arctic does not change substantially within ESMs, on average, under CO2 forcing (Figure 7.12b) reflects a compensating decrease in poleward dry-static energy transport as a response to polar amplified warming (Hwang et al., 2011; Armour et al., 2019; Donohoe et al., 2020). The latitudinal structure of radiative feedbacks primarily reflects that of the surface-albedo and lapse-rate feedbacks, which preferentially warm the Arctic (Graversen et al., 2014; Pithan and Mauritsen, 2014; Goosse et al., 2018). Latitudinal structure in the lapse-rate feedback reflects weak radiative damping to space with surface warming in polar regions, where atmospheric warming is constrained to the lower troposphere owing to stably stratified conditions, and strong radiative damping in the tropics, where warming is enhanced in the upper troposphere owing to moist convective processes. This is only partially compensated by latitudinal structure in the water-vapour feedback (Taylor et al., 2013), which favours tropical warming (Pithan and Mauritsen, 2014). While cloud feedbacks have been found to play little role in Arctic amplification in CMIP5 models (Pithan and Mauritsen, 2014; Goosse et al., 2018; Figure 7.12b), less-negative cloud feedbacks at high latitude, as seen within some CMIP6 models (Zelinka et al., 2020), tend to favour stronger polar amplification (Dong et al., 2020). A weaker Planck response at high latitudes, owing to less efficient radiative damping where surface and atmospheric temperatures are lower, also contributes to polar amplification (Pithan and Mauritsen, 2014). The effective radiative forcing of CO2 is larger in the tropics than at high latitudes, suggesting that warming would be tropically amplified if not for radiative feedbacks and poleward latent heat transport changes (Figure 7.12b–d; Stuecker et al., 2018).

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The energy budget analyses (Figure 7.12) suggest that greater surface warming in the Arctic than the Antarctic under greenhouse gas forcing arises from two main processes. The first is large surface heat uptake in the Southern Ocean (Figure 7.12c) driven by the upwelling of deep waters that have not yet felt the effects of the radiative forcing; the heat taken up is predominantly transported away from Antarctica by northward-flowing surface waters (Section 9.2.1; Marshall et al., 2015; Armour et al., 2016). Strong surface heat uptake also occurs in the subpolar North Atlantic Ocean under global warming (Section 9.2.1). However, this heat is partially transported northward into the Arctic, which leads to increased heat fluxes into the Arctic atmosphere (Figure 7.12b; Rugenstein et al., 2013; Jungclaus et al., 2014; Koenigk and Brodeau, 2014; Marshall et al., 2015; Nummelin et al., 2017; Singh et al., 2017; Oldenburg et al., 2018). The second main process contributing to differences in Arctic and Antarctic warming is the asymmetry in radiative feedbacks between the poles (Yoshimori et al., 2017; Goosse et al., 2018). This primarily reflects the weaker lapse-rate and surface-albedo feedbacks and more-negative cloud feedbacks in the SH high latitudes (Figure 7.12). However, note the SH cloud feedbacks are uncertain due to possible biases in the treatment of mixed phase clouds (Hyder et al., 2018). Idealized modelling suggests that the asymmetry in the polar lapse-rate feedback arises from the height of the Antarctic Ice Sheet precluding the formation of deep atmospheric inversions that are necessary to produce the stronger positive lapse-rate feedbacks seen in the Arctic (Salzmann, 2017; Hahn et al., 2020). ESM projections of the equilibrium response to CO2 forcing show polar amplification in both hemispheres, but generally with less warming in the Antarctic than the Arctic (C. Li et al., 2013; Yoshimori et al., 2017).

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Because multiple processes contribute to polar amplification, it is a robust feature of the projected long-term response to greenhouse gas forcing in both hemispheres. At the same time, contributions from multiple processes make projections of the magnitude of polar warming inherently more uncertain than global mean warming (Holland and Bitz, 2003; Roe et al., 2015; Bonan et al., 2018; Stuecker et al., 2018). The magnitude of Arctic amplification ranges from a factor of two to four in ESM projections of 21st-century warming (Section 4.5.1). While uncertainty in both global and tropical warming under greenhouse gas forcing is dominated by cloud feedbacks (Section 7.5.7; Vial et al., 2013), uncertainty in polar warming arises from polar surface-albedo, lapse-rate, and cloud feedbacks, changes in atmospheric and oceanic poleward heat transport, and ocean heat uptake (Hwang et al., 2011; Mahlstein and Knutti, 2011; Pithan and Mauritsen, 2014; Bonan et al., 2018).

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To avoid the influence of local biases, changes in the longitudinal temperature difference within Pliocene model simulations are typically evaluated using domain-averaged SSTs within chosen east and west Pacific regions and as such there is sensitivity to methodology. Unlike the reconstructed estimates, longitudinal gradient changes simulated by the Pliocene Model Intercomparison Project Phase 1 (PlioMIP1) models do not agree on the change in sign and are reported as spanning approximately –0.5°C to +0.5°C by Brierley et al. (2015) and approximately –1°C to +1°C by Tierney et al. (2019). Initial PlioMIP Phase 2 (PlioMIP2) analysis suggests responses similar to PlioMIP1 (Feng et al., 2019; Haywood et al., 2020). Models that include hypothetical modifications to cloud albedo or ocean mixing are required to simulate the substantially weaker longitudinal differences seen in reconstructions of the Early Pliocene (Fedorov et al., 2013; Burls and Fedorov, 2014a).

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The primary consideration that led to excluding ECS and TCR directly derived from ESMs is that information from these models is incorporated in the lines of evidence used in the assessment: ESMs are partly used to estimate historical and paleoclimate ERFs (Sections 7.5.2 and 7.5.3); to convert from local to global mean paleo temperatures (Section 7.5.3); to estimate how feedbacks change with SST patterns (Section 7.4.4.3); and to establish emergent constraints on ECs (Section 7.5.4). They are also used as important evidence in the process understanding estimates of the temperature, water vapour, albedo, biogeophysical, and non-CO2 biogeochemical feedbacks, whereas other evidence is primarily used for cloud feedbacks where the climate model evidence is weak (Section 7.4.2). One perspective on this is that the process understanding line of evidence builds on and replaces ESM estimates.

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While the magnitude of global warming by the end of the 21st century is dominated by future GHG emissions, the uncertainty in warming for a given ERF change is dominated by the uncertainty in ECS and TCr (Section 4.3.4). The proportion of variation explained by ECS and TCR varies with scenario and the time period considered, but within CMIP5 models around 60–90% of the globally averaged projected surface warming range in 2100 can be explained by the model range of these metrics (Grose et al., 2018). Uncertainty in the long-term global surface temperature change can further be understood in terms of the processes affecting the global TOA energy budget, namely the ERF, the radiative feedbacks which govern the efficiency of radiative energy loss to space with surface warming, and the increase in the global energy inventory (dominated by ocean heat uptake) which reduces the transient surface warming. A variety of studies evaluate the effect of each of these processes on surface changes within coupled ESM simulations by diagnosing so-called ‘warming contributions’ (Dufresne and Bony, 2008; Crook et al., 2011; Feldl and Roe, 2013; Vial et al., 2013; Pithan and Mauritsen, 2014; Goosse et al., 2018). By construction, the individual warming contributions sum to the total global surface warming (Figure 7.20b). For long-term warming in response to CO2 forcing in CMIP5 models, the energy added to the climate system by radiative feedbacks is larger than the ERF of CO2 (Figure 7.20a), implying that feedbacks more than double the magnitude of global warming (Figure 7.20b). Radiative kernel methods (see (Section 7.4.1) can be used to decompose the net energy input from radiative feedbacks into its components. The water-vapour, cloud and surface-albedo feedbacks enhance global warming, while the lapse-rate feedback reduces global warming. Ocean heat uptake reduces the rate of global surface warming by sequestering heat at depth away from the ocean surface. Section 7.4.4.1 shows the warming contributions from these factors at the regional scale.

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Qu, X. and A. Hall, 2014: On the persistent spread in snow-albedo feedback. Climate Dynamics, 42(1–2), 69–81, doi: 10.1007/s00382-013-1774-0.

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Schneider, A., M. Flanner, and J. Perket, 2018: Multidecadal Variability in Surface Albedo Feedback Across CMIP5 Models. Geophysical Research Letters, 45(4), 1972–1980, doi: 10.1002/2017gl076293.

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Stephens, G.L. et al., 2015: The albedo of Earth. Reviews of Geophysics, 53(1), 141–163, doi: 10.1002/2014rg000449.

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Thackeray, C.W. and A. Hall, 2019: An emergent constraint on future Arctic sea-ice albedo feedback. Nature Climate Change, 9(12), 972–978, doi: 10.1038/s41558-019-0619-1.

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Zhang, R., H. Wang, Q. Fu, P.J. Rasch, and X. Wang, 2019: Unraveling driving forces explaining significant reduction in satellite-inferred Arctic surface albedo since the 1980s. Proceedings of the National Academy of Sciences, 116(48), 23947–23953, doi: 10.1073/pnas.1915258116.

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The SR1.5 assessed with high confidence that there is no hysteresis in the loss of Arctic summer sea ice. In addition, there is no tipping point or critical threshold in global mean temperature beyond which the loss of summer sea ice becomes self-accelerating and irreversible (high confidence). This is because stabilizing feedbacks during winter related to increased heat loss through thin ice and thin snow, and increased emission of longwave radiation from open water, dominate over the amplifying ice albedo feedback (see Section 7.4.2 for details on the individual feedbacks; e.g., Eisenman, 2012; Wagner and Eisenman, 2015; Notz and Stroeve, 2018). Observed and modelled Arctic summer sea ice and global mean temperature are linked with little temporal delay, and the summer sea ice loss is reversible on decadal time scales (Armour et al., 2011; Ridley et al., 2012; Li et al., 2013; Jahn, 2018). The loss of winter sea ice is reversible as well, but the loss of winter sea ice area per degree of warming in CMIP5 and CMIP6 projections increases as the ice retreats from the continental shore lines, because these limit the possible areal fluctuations (high confidence) (Section 4.3.2.1; Bathiany et al., 2016, 2020; Meccia et al., 2020).

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The SROCC did not assess the role of cloud changes in detail. Studies since AR5 have shown that higher incident shortwave radiation in conjunction with reduced cloud cover leads to increased melt rates, particularly over the low-albedo ablation zone in the southern part of the Greenland Ice Sheet (Hofer et al., 2017; Niwano et al., 2019; Ruan et al., 2019). Conversely, an increase in cloud cover over the high-albedo central parts of the ice sheet, leading to higher downwelling longwave radiation, was shown to lead either to increased melt (Bennartz et al., 2013) or reduced refreezing of meltwater (van Tricht et al., 2016). The elevation dependence of the cloud radiative effect and its control on surface meltwater generation and refreezing (W. Wang et al., 2019; Hahn et al., 2020) can induce a spatially consistent response of the integrated Greenland Ice Sheet melt to dominant patterns of cloud and atmospheric variability. The shortwave and longwave radiation effects on surface melt by clouds have been shown to compensate for each other during strong atmospheric river events, and the increase in melt is caused by increased sensible heat fluxes during such events (Mattingly et al., 2020). In summary, there is medium confidence that cloud cover changes are an important driver of the increasing melt rates in the southern and western part of the Greenland Ice Sheet.

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The SROCC stated with high confidence that positive albedo feedbacks contributed substantially to the post-1990s Greenland Ice Sheet melt increase. Several (mostly positive) feedbacks involving surface albedo operate on ice sheets (e.g., Fyke et al., 2018). Melt amplification by the observed increase of bare ice exposure through snowline migration to higher parts of the ice sheet since 2000 (Shimada et al., 2016; Ryan et al., 2019) was five times stronger than the effect of hydrological and biological processes that lead to reduced bare ice albedo (Ryan et al., 2019). Impurities, in part biologically active (Ryan et al., 2018), have been observed to lead to albedo reduction (Stibal et al., 2017) and are estimated to have increased runoff from bare ice in the southwestern sector of the Greenland Ice Sheet by about 10% (Cook et al., 2020). In summary, new studies confirm that there is high confidence that the Greenland Ice Sheet melt increase since about 2000 has been amplified by positive albedo feedbacks, with the expansion of bare ice extent being the dominant factor, and albedo in the bare ice zone being primarily controlled by distributed biologically active impurities (see also Section 7.3.4.3).

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The SROCC stated with high confidence that, besides temperature, other factors, such as precipitation changes or internal glacier dynamics, have modified the temperature-induced glacier response in some regions. Deposition of a thin layer (<2 cm) of light-absorbing particles (e.g., black carbon, brown carbon, algae, mineral dust or volcanic ash) can exert an important control on glacier mass balance, by decreasing surface albedo and thus increasing absorbed shortwave radiation and melt (see also Section 7.3.4.3). The SROCC found limited evidence and low agreement that this process has had a significant effect on observed long-term glacier changes. Several studies have shown melt increases due to the deposition of light-absorbing particles (Schmale et al., 2017; Wittmann et al., 2017; Sigl et al., 2018; Di Mauro et al., 2019, 2020; Magalhães et al., 2019; Constantin et al., 2020). Conversely, increasingly thick debris cover (>2–5 cm) on retreating glaciers can slow down glacier melt (Pratap et al., 2015; Brun et al., 2016). Although debris covers only about 4–7% of the total glacier area globally (Scherler et al., 2018; Herreid and Pellicciotti, 2020), many glaciers are heavily debris-covered in their lower reaches, especially in High Mountain Asia, the Caucasus, the European Alps, Southern Andes and Alaska, resulting in different responses to warming than similar clean-ice glaciers. A shift in regional meteorological conditions, driven by the location and strength of the upper level zonal wind, has been found to have forced recent high mass loss rates in Western North America (Menounos et al., 2019). High geothermal heat flux areas underneath glaciers and high energy dissipation in the flow of water and ice causes additional mass loss of the glaciers in Iceland (Jóhannesson et al., 2020), accounting for 20% of the mass loss since 1994 (Aðalgeirsdóttir et al. 2020). Glacier lake volume in front of retreating glaciers, has increased globally by around 48% between 1990 and 2018 (Shugar et al., 2020), which can increase both subaqueous melt and calving. In summary, there is high confidence that non-climatic drivers have and will continue to modulate the first-order temperature response of glaciers in some regions.

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Secondary processes such as debris-cover thickening (e.g., Herreid and Pellicciotti, 2020), albedo changes due to light-absorbing particles (e.g., Magalhães et al., 2019; Williamson et al., 2019), trends of refreezing and water storage in firn (e.g., Ochwat et al., 2021), dynamic instabilities such as surges (e.g., Thøgersen et al., 2019) or glacier collapse (e.g., Kääb et al., 2018), are not represented in global glacier models, resulting in both underestimated and overestimated sensitivity to warming that is currently not possible to quantify. Furthermore, challenges for future projections are caused by the low-resolution and high-spatial variability at sub-grid scale of the precipitation amount provided by general circulation models (GCMs), which requires downscaling to the spatial scale of a glacier (Maussion et al., 2019; Zekollari et al., 2019; Marzeion et al., 2020). In summary, in agreement with SROCC, progress in global scale glacier modelling efforts allows medium confidence in the capability of current-generation glacier models to simulate the magnitude and timing of glacier mass changes as a response to climatic forcing.

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Observed large-scale snow cover changes, their attribution to human activity, and their effects on the hydrological cycle are also discussed in Chapter 2 (Section 2.3.2.2), Chapter 3 (Section 3.4.2) and Chapter 8 (Section 8.2.3.1) of this Report. The role of snow in the global surface albedo feedback is assessed in Section 7.4.2.3. The effect of aerosol deposition on snow albedo and associated climate forcing is assessed in Section 7.3.4.3.

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Building on AR5 (Flato et al., 2013) and subsequent published work, SROCC (Meredith et al., 2019) stated that CMIP5 models tended to underestimate the observed decrease of Northern Hemisphere spring SCE due to inappropriate parametrization of snow processes, misrepresentation of the snow-albedo feedback, underestimated temperature sensitivity, and biased climatological spring snow cover. Since AR5, progress in the observation, description and understanding of snow microstructure (Kinar and Pomeroy, 2015; Calonne et al., 2017) and its links to physical (thermal and radiative) properties (Löwe et al., 2013; Calonne et al., 2014) has prompted efforts to represent physical properties as a function of the evolving snow microstructure in models (Carmagnola et al., 2014; Calonne et al., 2015). However, even state-of-the-art snow models intended for meteorological and climate applications still struggle to correctly represent the time evolution of the snow thermal properties, particularly of cold and dry tundra snow (Domine et al., 2016). Moreover, most, if not all, CMIP6 climate models do not explicitly represent the darkening of snow by deposition of black carbon and other light-absorbing aerosol species known to influence snow melt rates (Section 7.3.4.3). Regardless of these shortcomings, snow modules of climate models continue to be improved. Recent progress includes the incorporation of multiple energy balances within the canopy and between sub-grid tiles with different snow heights (Aas et al., 2017; Boone et al., 2017) and inclusion of advanced specific snow models in coupled climate models (Niwano et al., 2018; Voldoire et al., 2019), opening the prospect of future progress in quantifying snow-related feedbacks in a changing climate. Recently developed multi-physics snow models (Essery, 2015; Lafaysse et al., 2017), which are able to emulate the behaviour of a large number of models in a broad range of climates, allow model shortcomings and key parameter uncertainties, for example, concerning snow masking by vegetation or snow thermal conductivity, to be identified. Guidance for future model improvement can be provided by improved diagnostics, such as a concise metric of snow insulation (A.G. Slater et al., 2017), which builds on an observed relation between effective seasonal mean SD and the dampening of winter season temperature decrease within the soil, and allows an efficient quantification of inaccuracies in the simulated snow insulation effect.

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There is high confidence that large inter-model variations in the snow-cover sensitivity to temperature can largely be explained by inaccuracies in the simulated snow-albedo feedback (Qu and Hall, 2014); a multi-model sub-ensemble of CMIP5 models that simulate a correct magnitude of this feedback presents a 40% reduced spread in the projected 21st century Northern Hemisphere land warming trend (Thackeray and Fletcher, 2016). Errors of the simulated feedback strength were linked to: (i) systematic positive albedo biases over the boreal forest belt, mostly due to unrealistic treatment of vegetation masking (Thackeray and Fletcher, 2016); (ii) inaccurate prescribed tree cover fraction and inappropriate parametrization of leaf area index in some models (Loranty et al., 2014; L. Wang et al., 2016); and (iii) low spatial resolution leading to inaccuracies in the strength of the simulated snow albedo feedback in mountainous regions (Letcher and Minder, 2015). Although the representation of snow-albedo feedback improved in many CMIP5 models over CMIP3, some models deteriorated (Thackeray et al., 2018).

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Since AR5, one study (Brown et al., 2017), applying a method developed by de Elía et al. (2013) to a CMIP5 sub-ensemble, suggested that over most of the Northern Hemisphere, the projected decrease of SCD will exceed natural variability before this will be the case for annual maximum SWE. The same study reports that, over large parts of Eastern and Western North America and Europe, forced SCD changes are projected to exceed natural variability in the 2020s in spring and autumn, while the signals tend to emerge later in the Arctic regions and particularly late, after 2060, in Eastern Siberia under the RCP8.5 scenario. Thackeray and Fletcher (2016) have shown that inter-model spread in projected spring SCE trends could be reduced through improved simulation of spring season warming because of the tight coupling between temperature and SCE linked to the snow-albedo feedback (Qu and Hall, 2014; Thackeray and Fletcher, 2016).

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In Greenland, stronger mass loss than currently projected might also occur (Aschwanden et al., 2019; Khan et al., 2020; T. Slater et al., 2020). For example, warming-induced dynamical changes in atmospheric circulation could enhance summer blocking and produce more frequent extreme melt events over Greenland similar to the record mass loss of more than 500 Gt in summer 2019 (Section 9.4.1.1; Delhasse et al., 2018; Sasgen et al., 2020). Cloud processes in polar areas that are not well represented in models could further enhance surface melt (Hofer et al., 2019), as could feedbacks between surface melt and the increasing albedo from meltwater, detritus and pigmented algae (Section 9.4.1.1; Cook et al., 2020). The same ice dynamical processes associated with basal melt and MISI discussed for Antarctica could also occur in Greenland, as long as the ice sheet is in contact with the ocean.

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Anttila, K., T. Manninen, E. Jääskeläinen, A. Riihelä, and P. Lahtinen, 2018: The role of climate and land use in the changes in surface albedo prior to snow melt and the timing of melt season of seasonal snow in northern land areas of 40°N–80°N during 1982–2015. Remote Sensing, 10(10), doi: 10.3390/rs10101619.

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Applegate, P.J. and K. Keller, 2015: How effective is albedo modification (solar radiation management geoengineering) in preventing sea-level rise from the Greenland Ice Sheet?Environmental Research Letters, 10(8), 84018, doi: 10.1088/1748-9326/10/8/084018.

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Constantin, J.G. et al., 2020: Measurements and modeling of snow albedo at Alerce Glacier, Argentina: Effects of volcanic ash, snow grain size, and cloudiness. Cryosphere, 14(12), 4581–4601, doi: 10.5194/tc-14-4581-2020.

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Di Mauro, B. et al., 2020: Glacier algae foster ice-albedo feedback in the European Alps. Scientific Reports, 10(1), 4739, doi: 10.1038/s41598-020-61762-0.

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Lenaerts, J.T.M. et al., 2017: Meltwater produced by wind-albedo interaction stored in an East Antarctic ice shelf. Nature Climate Change, 7(1), 58–62, doi: 10.1038/nclimate3180.

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Letcher, T.W. and J.R. Minder, 2015: Characterization of the simulated regional snow albedo feedback using a regional climate model over complex terrain. Journal of Climate, 28(19), 7576–7595, doi: 10.1175/jcli-d-15-0166.1.

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Loranty, M.M., L.T. Berner, S.J. Goetz, Y. Jin, and J.T. Randerson, 2014: Vegetation controls on northern high latitude snow-albedo feedback: Observations and CMIP5 model simulations. Global Change Biology, 20(2), 594–606, doi: 10.1111/gcb.12391.

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Qu, X. and A. Hall, 2014: On the persistent spread in snow-albedo feedback. Climate Dynamics, 42(1–2), 69–81, doi: 10.1007/s00382-013-1774-0.

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Thackeray, C.W. and C.G. Fletcher, 2016: Snow albedo feedback: Current knowledge, importance, outstanding issues and future directions. Progress in Physical Geography, 40(3), 392–408, doi: 10.1177/0309133315620999.

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Thackeray, C.W., C.G. Fletcher, and C. Derksen, 2015: Quantifying the skill of CMIP5 models in simulating seasonal albedo and snow cover evolution. Journal of Geophysical Research: Atmospheres, 120(12), 5831–5849, doi: 10.1002/2015jd023325.

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Thackeray, C.W., X. Qu, and A. Hall, 2018: Why Do Models Produce Spread in Snow Albedo Feedback?Geophysical Research Letters, 45(12), 6223–6231, doi: 10.1029/2018gl078493.

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Wang, L. et al., 2016: Investigating the spread in surface albedo for snow-covered forests in CMIP5 models. Journal of Geophysical Research: Atmospheres, 121(3), 1104–1119, doi: 10.1002/2015jd023824.

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Wittmann, M. et al., 2017: Impact of dust deposition on the albedo of Vatnajökull ice cap, Iceland. The Cryosphere, 11, 741–754, doi: 10.5194/tc-11-741-2017.

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There is a growing body of literature indicating elevation-dependent warming (EDW; different rates of warming by altitude although not necessarily increasing with altitude) in several mountain regions but not globally (Hock et al., 2019; Pepin et al., 2019; Ahmed et al., 2020; B. Li et al., 2020; Williamson et al., 2020; You et al., 2020; Micu et al., 2021). Statistically significant elevational enhancement to long-term trends in maximum near-surface air temperatures and diurnal temperature range were observed in southern central Himalaya and in the Swiss Alps (Rottler et al., 2019; Thakuri et al., 2019). Aguilar-Lome et al. (2019) reported that winter daytime land surface temperatures in the Andean region between 7°S and 20°S show the strongest trends at higher elevations: +1.7°C per decade above 5000 m above sea level. Palazzi et al. (2019) identified changes in albedo and downward thermal radiation as key drivers of EDW according to the simulation outputs of a high-spatial-resolution model in three important mountainous areas: the Colorado Rocky Mountains, the Greater Alpine Region and the Himalayas–Tibetan Plateau, but mechanisms for EDW remain complex (Hock et al., 2019). Warming is also affecting mountain lake surface temperatures, increasing probabilities of ice-free winters and the frequency and duration of ‘lake heatwaves’ (high confidence) (O’Reilly et al., 2015; Woolway et al., 2020, 2021) with a high variability from lake to lake.

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Gaffin, S.R. et al., 2012: Bright is the new black – multi-year performance of high-albedo roofs in an urban climate. Environmental Research Letters, 7(1), 014029, doi: 10.1088/1748-9326/7/1/014029.

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The possibility of feedbacks and interactions between climate drivers and biological systems or ecological processes was identified as a significant emerging issue in AR5, and has since also been highlighted in the SRCCL and the SR1.5. It is virtually certain that land cover changes affect regional and global climate through changes to albedo, evapotranspiration and roughness (very high confidence) (Perugini et al., 2017). There is growing evidence that biosphere-related climate processes are being affected by climate change in combination with disturbance and LULCC (high confidence) (Jia et al., 2019). It is virtually certain that land surface change caused by disturbances such as forest fires, hurricanes, phenological changes, insect outbreaks and deforestation affect carbon, water and energy exchanges, thereby influencing weather and climate (very high confidence) (Table 2.4; Figure 2.10) (Bright et al., 2013; Brovkin et al., 2013; Naudts et al., 2016; Prăvălie, 2018).

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Changes in regional biodiversity are integral parts of ecosystem–climate feedback loops, including and beyond carbon cycle processes (Figure 2.10; Table 2.4). For instance, the impacts of climate-induced altered animal composition and trophic cascades on ecosystem carbon turnover (see Sections 2.4.4.4, 2.5.3.4) could be a substantive contribution to carbon–climate feedbacks (low confidence). Additional surface–atmosphere feedbacks that arise from changes in vegetation cover and subsequently altered albedo, evapotranspiration or roughness (often summarised as biophysical feedbacks) can be regionally relevant and could amplify or dampen vegetation cover changes (Jia et al., 2019).

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Chen, D. et al., 2018a: Strong cooling induced by stand-replacing fires through albedo in Siberian larch forests. Scientific Reports, 8, doi:10.1038/s41598-018-23253-1.

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Landry, J. S. et al., 2016: Modelling long-term impacts of mountain pine beetle outbreaks on merchantable biomass, ecosystem carbon, albedo, and radiative forcing. Biogeosciences, 13 (18), 5277–5295, doi:10.5194/bg-13-5277-2016.

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Lang, J. et al., 2018: An Investigation of Ice Surface Albedo and Its Influence on the High-Altitude Lakes of the Tibetan Plateau. Remote Sensing, 10 (2), 218.

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Loranty, M. M. et al., 2014: Vegetation controls on northern high latitude snow-albedo feedback: observations and CMIP5 model simulations. Global Change Biology, 20 (2), 594–606, doi:10.1111/gcb.12391.

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Both natural and managed ecosystems, ecosystem services and livelihoods in Asia will potentially be substantially impacted by changing climate (Wu et al., 2018d). There will be increased risk for biodiversity, particularly many endemic and threatened species of fauna and flora already under environmental pressure from land-use change and other regional and global processes (Zomer et al., 2014; Rashid et al., 2015; Choi et al., 2019). Biomes shift not only serves as a signal of climate change but also provides important information for resources management and ecotone ecosystem conservation. A widespread upwards encroachment of subalpine forests would displace regionally unique alpine tundra habitats and possibly cause the loss of alpine species (Schickhoff et al., 2015). In North Asia, emissions from fires reduce forests’ ability to regulate climate. A warmer and longer growing season will increase vulnerability to fires, although fires can be attributed both to climate warming and to other human and natural influences. Recent field-based observations revealed that the forests in South Siberia are losing their ability to regenerate after fire and other landscape disturbances under a warming climate (Brazhnik et al., 2017). Data support the hypothesis of a climate-driven increase in fire frequency in boreal forests with the possible turning of boreal forests from a carbon sink to a carbon source (Ponomarev et al., 2016; Schaphoff et al., 2016; Brazhnik et al., 2017; Ponomarev et al., 2018); however, warming resulting from forest fire is partly offset by cooling in response to increased surface albedo of burned areas in a snow-on period (Chen and Loboda, 2018; Chen et al., 2018a; Jia et al., 2019; Lasslop et al., 2019).

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Chen, D., et al., 2018a: Strong cooling induced by stand-replacing fires through albedo in Siberian larch forests. Sci. Rep. , 8, doi:10.1038/s41598-018-23253-1.

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Davin, E.L., et al., 2014: Preferential cooling of hot extremes from cropland albedo management. Proc. Natl. Acad. Sci. , 111 (27), 9757–9761.

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Field, L., et al., 2018: Increasing Arctic sea ice albedo using localized reversible geoengineering. Earth’s Future, 6 (6), 882–901.

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Similarly, technology must always be grounded in an appreciation of the cultural context. Research in the European Arctic with the Indigenous Sami Peoples found that use of GPS technology on reindeer, together with supplementary feeding, offered useful adaptations for some herders. However, there are fears such technologies may, over time, reduce the skills, cultural knowledge and Indigenous adaptations of the Sami (Andersson and Keskitalo, 2017), as, for example, reindeer become tamer through supplementary feeding, affecting their range selection. Overall, technology and other adaptations should seek not to erode Sami culture’s adaptive capacity (Vuojala-Magga et al., 2011; Risvoll and Hovelsrud, 2016), particularly because reindeer grazing as a land management practice can play a useful climate change mitigation role too. Reindeer grazing protects tundra from tree line and bush encroachment, while summer grazing increases surface albedo by delaying snowmelt (Jaakkola et al., 2018).

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Interventions in the morphology and built form of cities can contribute to the reduction of the urban heat island effect and reduce the consequences of urban heatwaves. These can include installing air conditioning, establishing public cooling centres (i.e., for use during heatwaves), pavement watering (Parison et al., 2020a) and increasing surface albedo through ‘cool roofs’ (i.e., with high-reflectance materials) and walls. Air conditioning can significantly increase the local urban heat island (Salamanca et al., 2014; Wang et al., 2019a) and the choice of refrigerant has a significant impact on global warming potential (McLinden et al., 2017). The relative efficiency of cool roofs compared with green roofs is variable, because while white roofs have similar potential to reduce the urban heat island (Li, Bou-Zeid and Oppenheimer, 2014), they can quickly turn grey due to dust and air pollution, losing their effectiveness (Gunawardena, Wells and Kershaw, 2017), although these effects are now well studied and newer performance standards should account for ageing and soiling effects on reflectivity (Paolini et al., 2014). Ageing of ‘cool pavements’ is more complex, which makes their long-term performance less reliable to predict (Lontorfos, Efthymiou and Santamouris, 2018). The cooling performance of green roofs is highly variable and depends on the actual water content of the green roof substrate, with dry vegetation performing poorly in terms of cooling (Parison et al., 2020b). This holds true for regular vegetation and NBS in general (Daniel, Lemonsu and Viguie, 2018). For all built environment adaptations, changes are locked-in for a long time, and are likely to be expensive so that care is needed to avoid potential negative impacts on social equity (Cabrera and Najarian, 2015; Romero-Lankao et al., 2018; Fried et al., 2020; Rode et al., 2017) and carbon-intensive construction (Bai et al., 2018; Seto et al., 2016).

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Architectural and urban design regulations at the single-building scale (building codes and guidelines) facilitate climate responsive buildings that adapt to a changing climate and have the potential to collectively change user behaviour during extreme weather events (Osman and Sevinc, 2019). They include buildings that are adaptive to ensure user comfort during extremes of hot and cold as well as to floods (e.g., building on stilts and amphibian architecture). Changes to design standards can scale quickly and widely, but retrofit of existing buildings is expensive, so care must be taken to avoid potential negative impacts on social equity (Schünemann et al., 2020; Matopoulos, Kovács and Hayes, 2014; Ajibade and McBean, 2014; Bastidas-Arteaga and Stewart, 2019). Buildings can be adapted to the negative consequences of climate change by altering their characteristics, for example increasing the insulation values (e.g., van Hooff et al., 2014; Makantasi and Mavrogianni, 2016; Fisk, 2015; Fosas et al., 2018; Barbosa, Vicente and Santos, 2015; Invidiata and Ghisi, 2016; Pérez-Andreu et al., 2018; Taylor et al., 2018; Triana, Lamberts and Sassi, 2018), adding solar shading (e.g., van Hooff et al., 2014; Makantasi and Mavrogianni, 2016; Barbosa, Vicente and Santos, 2015; Invidiata and Ghisi, 2016; Pérez-Andreu et al., 2018; Taylor et al., 2018; Triana, Lamberts and Sassi, 2018; Dodoo and Gustavsson, 2016; Osman and Sevinc, 2019), increasing natural ventilation, preferably during the night (e.g., van Hooff et al., 2014; Makantasi and Mavrogianni, 2016; Pérez-Andreu et al., 2018; Triana, Lamberts and Sassi, 2018; Dodoo and Gustavsson, 2016; Osman and Sevinc, 2019; Mulville and Stravoravdis, 2016; Cellura et al., 2017; Fosas et al., 2018; Dino and Meral Akgül, 2019), solar orientation of bedroom windows (Schuster et al., 2017), applying high-albedo materials for the building envelope (van Hooff et al., 2014; Invidiata and Ghisi, 2016; Baniassadi et al., 2018; Triana, Lamberts and Sassi, 2018), altering the thermal mass (van Hooff et al., 2014; Mulville and Stravoravdis, 2016; Din and Brotas, 2017), adding green roofs/facades to poorly insulated buildings (Geneletti and Zardo, 2016; Skelhorn, Lindley and Levermore, 2014; van Hooff et al., 2014; de Munck et al., 2018; Feitosa and Wilkinson, 2018) and for water harvesting (Sepehri et al., 2018).

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Baniassadi, A., D.J. Sailor, P.J. Crank and G.A. Ban-Weiss, 2018: Direct and indirect effects of high-albedo roofs on energy consumption and thermal comfort of residential buildings. Energy Build. , 178, 71–83.

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Schubert, S. and S. Grossman-Clarke, 2013: The influence of green areas and roof albedos on air temperatures during extreme heat events in Berlin, Germany. Meteorol. Z. , 22 (2), 131–143.

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A multi-sectoral approach, including the engagement of a range of stakeholders will likely benefit the response to longer-term heat risks through the implementation of measures such as climate-sensitive urban design and planning that mitigates UHI effects (high confidence) (Ebi, 2019; Jay et al., 2021; Alexander et al., 2016; Levy, 2016; Masson et al., 2018; McEvoy, 2019; Pisello et al., 2018). In the shorter-term, potentially localised solutions can include awnings, louvers, directional reflective materials, altering roof albedo, mist sprays, evaporative materials, green roofs and building facades and cooling centres (Jay et al., 2021; Macintyre and Heaviside, 2019; Spentzou et al., 2021; Takebayashi, 2018). NbS to reduce heat that offer co-benefits for ecological systems include green and blue infrastructure (e.g., urban greening/forestry and the creation of water bodies) (Koc et al., 2018; Lai et al., 2019; Shooshtarian et al., 2018; Ulpiani, 2019; Zuvela-Aloise et al., 2016; Hobbie and Grimm, 2020). The implementation of climate-sensitive design and planning can be constrained by governance issues (Jim et al., 2018) and the benefits are not always evenly distributed among residents. Implementation of climate-sensitive design and NbS does, however, need to be carried out within the context of wider public health planning because water bodies and moist vegetated surfaces provide suitable habitats for a range of disease vectors (Nasir et al., 2017; Tian et al., 2016; Trewin et al., 2020). Solutions recommended for managing exposure to heat in outdoor workers include improved basic protection (including shade and planned rest breaks), heat-appropriate personal protective equipment, work scheduling for cooler times of the day, heat acclimation, improved aerobic fitness, access to sufficient cold drinking water and on-site cooling facilities and mechanisation of work (Morabito et al., 2021; Morris et al., 2020; Varghese et al., 2020; Williams et al., 2020).

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Bright, R. M., K. Zhao, R. B. Jackson and F. Cherubini, 2015: Quantifying surface albedo and other direct biogeophysical climate forcings of forestry activities. Global Change Biology, 21 (9), 3246–3266, doi: https://doi.org/10.1111/gcb.12951.

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A large expansion in cropland area (soybean, corn and sugarcane) was observed in the past two decades in SAM, in response to increased local and global demand for biofuels and agricultural commodities (high confidence) (Lapola et al., 2014; Cohn et al., 2016). Feedbacks to the climate system resulting from such land use changes are intricate. The clear-cutting of Amazon forest and Cerrado savannah in the region led to a local warming due to an increase in the energy balance and evapotranspiration (Malhado et al., 2010); in contrast, the replacement of pasture by agriculture have led to a local cooling effect, due to changes in the surface albedo (medium confidence: medium evidence, medium agreement ). Deforestation of the Amazon for pastures and soybean have decreased evapotranspiration during drought months and caused a localised lengthening of the dry season in northwestern SAM by 6.5 (± 2.5) d since 1979 (medium confidence: medium evidence, medium agreement ) (Fu et al., 2013).

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Malmros, J.K., et al., 2018: Snow cover and snow albedo changes in the central Andes of Chile and Argentina from daily MODIS observations (2000–2016). Remote Sens. Environ. , 209, 240–252, doi:10.1016/j.rse.2018.02.072.

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Under specific conditions, biofuels may represent an important climate mitigation strategy for the transport sector (Daioglou et al. 2020; Muratori et al. 2020). Both the IPCC Special Report on Global Warming of 1.5°C and the IPCC Special Report on Climate Change and Land highlighted that biofuels could be associated with climate mitigation co-benefits and adverse side effects to many SDGs. These side effects depend on context-specific conditions, including deployment scale, associated land-use changes and agricultural management practices (Section 7.4.4 and Box 7.10). There is broad agreement in the literature that the most important factors in determining the climate footprint of biofuels are the land use and land-use change characteristics associated with biofuel deployment scenarios (Elshout et al. 2015; Daioglou et al. 2020). This issue is covered in more detail in Box 7.1. While the mitigation literature primarily focuses on the GHG-related climate forcings, note that land is an integral part of the climate system through multiple geophysical and geochemical mechanisms (albedo, evaporation, etc.). For example, Sections 2.2.7 and 7.3.4 in the AR6 WGI report indicate that geophysical aspects of historical land-use change outweigh the geochemical effects, leading to a net cooling effect. The land-related carbon footprints of biofuels presented in Sections 10.4–10.6 are adopted from Chapter 7 (Section 7.4.4, Box 7, and Figure 7.1). The results show how the land-related footprint increases due to an increased outtake of biomass, as estimated with different models that rely on global supply scenarios of biomass for energy and fuel of 100 exajoules (EJ). The integrated assessment models and scenarios used include the EMF 33 scenarios (IAM-EMF33), from partial models with constant land cover (PM-CLC), and from partial models with natural regrowth (PM-NGR). These results are combined with both biomass cultivation emission ranges for advanced biofuels aligned with Koeble et al. (2017), El Akkari et al. (2018), Jeswani et al. (2020), and Puricelli et al. (2021) and conversion efficiencies and conversion phase emissions as described in Table 10.5. The modelled footprints resulting from land-use changes related to delivering 100 EJ of biomass at global level are in the range of 3–77 gCO2-eq per MJ of advanced biofuel (median 38 gCO2-eq MJ–1) at an aggregate level for Integrated Assessment Models (IAMs) and partial models with constant land cover (Daioglou et al. 2020; Rose et al. 2020). The results for partial models with natural regrowth are much higher (91–246 CO2-eq MJ–1 advanced biofuel). The latter ranges may appear in contrast with the results from the scenario literature in Section 10.7, where biofuels play a role in many scenarios compatible with low warming levels. This contrast is a result of different underlying modelling practices. The general modelling approach used for the scenarios in the AR6 database accounts for the land-use change and all other GHG emissions along a given transformation trajectory, enabling assessments of the warming level incurred. The results labelled ‘EMF33’ and ‘partial models with constant land cover’ are obtained with this modelling approach. The results in the category ‘partial models with natural regrowth’ attribute additional CO2 emissions to the bioenergy system, corresponding to estimated uptake of CO2 in a counterfactual scenario where land is not used for bioenergy, but instead subject to natural vegetation regrowth. While the partial analysis provides insights into the implications of alternative land-use strategies, such analysis does not identify the actual emissions of bioenergy production. As a result, the partial analysis is not compatible with the identification of warming levels incurred by an individual transformation trajectory, and therefore not aligned with the general approach applied for the scenarios in the AR6 database.

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Shipping in the Arctic is a topic of increasing interest. The reduction of Arctic summer sea ice increases the access to the northern sea routes (Smith and Stephenson 2013; Melia et al. 2016; Aksenov et al. 2017; Fox-Kemper et al. 2021). Literature and public discourse has sometimes portrayed this trend as positive (Zhang et al. 2016b), as it allows for shorter shipping routes, for example between Asia and Europe, with estimated travel time savings of 25–40% (Aksenov et al. 2017). However, the acceleration of Arctic cryosphere melt and reduced sea ice that enable Arctic shipping reduce surface albedo and amplify climate warming (Eyring et al. 2021). Furthermore, local air pollutants can play different roles in the Arctic. For example, black carbon emissions reduce albedo and absorb heat in air, on snow and ice (Browse et al. 2013; Kang et al. 2020; Messner 2020; Eyring et al. 2021). Finally, changing routing from Suez to the northern sea routes may reduce total emissions for a voyage, but also shifts emissions from low to high latitudes. Changing the location of the emissions adds complexity to the assessment of the climatic impacts of Arctic shipping, as the local conditions are different and the SLCF may have a different impact on clouds, precipitation, albedo and local environment (Dalsøren et al. 2013; Fuglestvedt et al. 2014; Marelle et al. 2016). Observations have shown that 5–25% of air pollution in the Arctic stems from shipping activity within the Arctic itself (Aliabadi et al. 2015). Emissions outside the Arctic can affect Arctic climate, and changes within the Arctic may have global climate impacts. Both modelling and observations have shown that aerosol emissions from shipping can have a significant effect on air pollution and shortwave radiative forcing (Ødemark et al. 2012; Peters et al. 2012; Dalsøren et al. 2013; Roiger et al. 2014; Righi et al. 2015; Marelle et al. 2016).

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The most important ways to minimise PV’s impact on the environment lie in recycling materials at end of life and making smart land-use decisions (medium confidence). A comprehensive assessment of PV’s environmental impacts requires lifecycle analysis (LCA) of resource depletion, land-use, ecotoxicity, eutrophication, acidification, ozone, and particulates, among other things (Mahmud et al. 2018). LCA studies show that solar PVs produce far less CO2 per unit of electricity than fossil generation, but PV CO2 emissions vary due to the carbon intensity of manufacturing energy and offset electricity (Grant and Hicks 2020). Concerns about systemic impacts, such as reducing the Earth’s albedo by covering surfaces with dark panels, have shown to be trivial compared to the mitigation benefits (Nemet 2009) (Box 6.7). Even though GHG LCA estimates span a considerable range of 9–250 gCO2 kWh –1 (de Wild-Scholten 2013; Kommalapati et al. 2017), recent studies that reflect higher efficiencies and manufacturing improvements find lower lifecycle emissions, including a range of 18–60 gCO2 kWh –1 (Wetzel and Borchers 2015) and central estimates of 80 gCO2 kWh –1 (Hou et al. 2016), 50 gCO2 kWh –1 (Nugent and Sovacool 2014), and 20 gCO2 kWh –1 (Louwen et al. 2016). These recent values are an order of magnitude lower than coal, and natural gas and further decarbonisation of the energy system will make them lower still. Thin films and organics produce half the lifecycle emissions of silicon wafer PV, mainly because they use less material (Lizin et al. 2013; Hou et al. 2016). Novel materials promise even lower environmental impacts, especially with improvements to their performance ratios and reliability (Gong et al. 2015; Muteri et al. 2020). Higher efficiencies, longer lifetimes, sunny locations, less carbon-intensive manufacturing inputs, and shifting to thin films could reduce future lifecycle impacts.

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Wohlfahrt, G., E. Tomelleri, and A. Hammerle, 2021: The albedo–climate penalty of hydropower reservoirs. Nat. Energy, 6(4) , 372–377, doi:10.1038/s41560-021-00784-y.

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AFOLU CO2 emissions fluxes are mainly driven by land use change (CO2LULUCF), and account for about half of total net AFOLU emissions. The rate of deforestation has generally declined, while global tree cover and global forest growing stock levels are likely increasing (medium confidence). There are substantial regional differences, with losses of carbon generally observed in tropical regions and gains in temperate and boreal regions. Agricultural methane (CH4) and nitrous oxide (N2O) emissions are estimated to average 157 ± 47.1 MtCH4 yr –1 and 6.6 ± 4.0 MtN2O yr –1 or 4.2 ± 1.3 and 1.8 ± 1.1 GtCO2-eq yr –1 (using IPCC AR6 GWP100 values for CH4 and N2O) respectively between 2010 and 2019. AFOLU CH4 emissions continue to increase ( high confidence), the main source of which is enteric fermentation from ruminant animals ( high confidence). Similarly, AFOLU N2O emissions are increasing, dominated by agriculture, notably from manure application, nitrogen deposition, and nitrogen fertiliser use ( high confidence). In addition to being a source and sink for GHG emissions, land plays an important role in climate through albedo effects, evapotranspiration and volatile organic compounds (VOCs) and their mix, although the combined role in total climate forcing is unclear and varies strongly with bioclimatic region and management type. {2.4.2.5, 7.2, 7.2.1, 7.2.3, 7.3}

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The deployment of all land-based mitigation measures can provide multiple co-benefits, but there are also risks and trade-offs from misguided or inappropriate land management (high confidence). Such risks can best be managed if AFOLU mitigation is pursued in response to the needs and perspectives of multiple stakeholders to achieve outcomes that maximise synergies while limiting trade-offs (medium confidence). The results of implementing AFOLU measures are often variable and highly context specific. Depending on local conditions (e.g., ecosystem, climate, food system, land ownership) and management strategies (e.g., scale, method), mitigation measures have the potential to positively or negatively impact biodiversity, ecosystem functioning, air quality, water availability and quality, soil productivity, rights infringements, food security, and human well-being. Mitigation measures addressing GHGs may also affect other climate forcers such as albedo and evapotranspiration. Integrated responses that contribute to mitigation, adaptation, and other land challenges will have greater likelihood of being successful ( high confidence); measures which provide additional benefits to biodiversity and human well-being are sometimes described as ‘Nature-Based Solutions’. {7.1, 7.4, 7.6}

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Studies of biophysical effects have increased since AR5 reaching high agreement for the effects of changes in land condition on surface albedo (Leonardi et al. 2015). Low confidence remains in proposing specific changes in land conditions to achieve desired impacts on local, regional and global climates due to: a poor relationship between changes in surface albedo and changes in surface temperature (Davin and de Noblet-Ducoudré 2010), compensation and feedbacks among biophysical processes (Bonan 2016; Kalliokoski et al. 2020), climate and seasonal dependency of the biophysical effects (Bonan 2016), omittance of short-lived chemical forcers (Unger 2014; Kalliokoski et al. 2020), and study domains often being too small to document possible conflicts between local and non-local effects (Swann et al. 2012; Hirsch et al. 2018).

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Activities, co-benefits, risks and implementation opportunities and barriers. Afforestation and reforestation (A/R) are activities that convert land to forest, where reforestation is on land that has previously contained forests, while afforestation is on land that historically has not been forested (Box 7.2). Forest restoration refers to a form of reforestation that gives more priority to ecological integrity as well, even though it can still be a managed forest. Depending on the location, scale, and choice and management of tree species, A/R activities have a wide variety of co-benefits and trade-offs. Well-planned, sustainable reforestation and forest restoration can enhance climate resilience and biodiversity, and provide a variety of ecosystem services including water regulation, microclimatic regulation, soil erosion protection, as well as renewable resources, income and livelihoods (Locatelli et al. 2015; Stanturf et al. 2015; Ellison et al. 2017; Verkerk et al. 2020). Afforestation, when well planned, can help address land degradation and desertification by reducing runoff and erosion and lead to cloud formation however, when not well planned, there are localised trade-offs such as reduced water yield or biodiversity (Teuling et al. 2017; Ellison et al. 2017). The use of non-native species and monocultures may have adverse impacts on ecosystem structure and function, and water availability, particularly in dry regions (Ellison et al. 2017). A/R activities may change the surface albedo and evapotranspiration regimes, producing net cooling in the tropical and subtropical latitudes for local and global climate and net warming at high latitudes (Section 7.4.2). Very large-scale implementation of A/R may negatively affect food security since an increase in global forest area can increase food prices through land competition (Kreidenweis et al. 2016).

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Conclusions from AR5 and IPCC Special Reports (SR1.5, SROCC and SRCCL); mitigation potential, costs, and pathways. In the SRCCL (Chapters 2 and 6), it was estimated that avoided peat impacts could deliver 0.45–1.22 GtCO2-eq yr –1 technical potential by 2030–2050 (medium confidence) (Hooijer et al. 2010; Griscom et al. 2017; Hawken 2017). The mitigation potential estimates cover tropical peatlands and include CO2, N2O and CH4 emissions. The mitigation potential is derived from quantification of losses of carbon stocks due to land conversion, shifts in GHG fluxes, alterations in net ecosystem productivity, input factors such as fertilisation needs, and biophysical climate impacts (e.g., shifts in albedo, water cycles, etc.). Tropical peatlands account for only about 10% of peatland area and about 20% of peatland carbon stock but about 80% of peatland carbon emissions, primarily from peatland conversion in Indonesia (about 60%) and Malaysia (about 10%) (Hooijer et al. 2010; Page et al. 2011; Leifeld and Menichetti 2018). While the total mitigation potential of peatland conservation is considered moderate, the per hectare mitigation potential is the highest among land-based mitigation measures (Roe et al. 2019).

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Activities, co-benefits, risks and implementation opportunities and barriers. Biochar is produced by heating organic matter in oxygen-limited environments (pyrolysis and gasification) (Lehmann and Joseph 2012). Feedstocks include forestry and sawmill residues, straw, manure and biosolids. When applied to soils, biochar is estimated to persist from decades to thousands of years, depending on feedstock and production conditions (J. Wang et al. 2016; Singh et al. 2015). Biochar systems producing biochar for soil application plus bioenergy, generally give greater mitigation than bioenergy alone and other uses of biochar, and are recognised as a CDR strategy. Biochar persistence is increased through interaction with clay minerals and soil organic matter (Fang et al. 2015). Additional CDR benefits arise through ‘negative priming’ whereby biochar stabilises soil carbon and rhizodeposits (Weng et al. 2015; J. Wang et al. 2016; Archanjo et al. 2017; Hagemann et al. 2017; Han Weng et al. 2017; Weng et al. 2018). Besides CDR, additional mitigation can arise from displacing fossil fuels with pyrolysis gases, lower soil N2O emissions (Cayuela et al. 2014, 2015; Song et al. 2016; He et al. 2017; Verhoeven et al. 2017; Borchard et al. 2019), reduced nitrogen fertiliser requirements due to reduced nitrogen leaching and volatilisation from soils (Liu et al. 2019; Borchard et al. 2019), and reduced GHG emissions from compost when biochar is added (Agyarko-Mintah et al. 2017; Wu et al. 2017). Biochar application to paddy rice has resulted in substantial reductions (20–40% on average) in N2O (Song et al. 2016; Awad et al. 2018; Liu et al. 2018) (Section 7.4.3.5) and smaller reduction in CH4 emissions (Song et al. 2016; Kammann et al. 2017; Kim et al. 2017a; He et al. 2017; Awad et al. 2018). Potential co-benefits include yield increases particularly in sandy and acidic soils with low cation exchange capacity (Woolf et al. 2016; Jeffery et al. 2017); increased soil water-holding capacity (Omondi et al. 2016), nitrogen use efficiency (Liu et al. 2019; Borchard et al. 2019), biological nitrogen fixation (Van Zwieten et al. 2015); adsorption of organic pollutants and heavy metals (e.g., Silvani et al. 2019); odour reduction from manure handling (e.g., Hwang et al. 2018) and managing forest fuel loads (Puettmann et al. 2020). Due to its dark colour, biochar could decrease soil albedo (Meyer et al. 2012), though this is insignificant under recommended rates and application methods. Biochar could reduce enteric CH4 emissions when fed to ruminants (Section 7.4.3.4). Barriers to upscaling include insufficient investment, limited large-scale production facilities, high production costs at small scale, lack of agreed approach to monitoring, reporting and verification, and limited knowledge, standardisation and quality control, restricting user confidence (Gwenzi et al. 2015).

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Kreidenweis, U. et al., 2016: Afforestation to mitigate climate change: impacts on food prices under consideration of albedo effects. Environ. Res. Lett. , 11(8) , 085001, doi:10.1088/1748-9326/11/8/085001.

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Leonardi, S., F. Magnani, A. Nolè, T. Van Noije, and M. Borghetti, 2015: A global assessment of forest surface albedo and its relationships with climate and atmospheric nitrogen deposition. Glob. Change Biol. , 21(1) , 287–298, doi:10.1111/gcb.12681.

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Meyer, S., R.M. Bright, D. Fischer, H. Schulz, and B. Glaser, 2012: Albedo Impact on the Suitability of Biochar Systems To Mitigate Global Warming. Environ. Sci. Technol. , 46(22) , 12726–12734, doi:10.1021/es302302g.

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Solar radiation modification, in the literature also referred to as ‘solar geoengineering’, refers to the intentional modification of the Earth’s shortwave radiative budget, such as by increasing the reflection of sunlight back to space, with the aim of reducing warming. Several SRM options have been proposed, including stratospheric aerosol injection (SAI), marine cloud brightening (MCB), ground-based albedo modifications (GBAM), and ocean albedo change (OAC). SRM has been discussed as a potential response option within a broader climate risk management strategy, as a supplement to emissions reduction, carbon dioxide removal and adaptation (Crutzen 2006; Shepherd 2009; Caldeira and Bala 2017; Buck et al. 2020), for example as a temporary measure to slow the rate of warming (Keith and MacMartin 2015) or address temperature overshoot (MacMartin et al. 2018; Tilmes et al. 2020). SRM assessments of potential benefits and risks still primarily rely on modelling efforts and their underlying scenario assumptions (Sugiyama et al. 2018a), for example in the context of the Geoengineering Model Intercomparison Project GeoMIP6 (Kravitz et al. 2015). Recently, small-scale MCB and OAC experiments started to take place on the Great Barrier Reef (McDonald et al. 2019).

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Crutzen, P.J., 2006: Albedo Enhancement by Stratospheric Sulfur Injections: A Contribution to Resolve a Policy Dilemma?Clim. Change, 77(3–4) , 211–220, doi:10.1007/s10584-006-9101-y.

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Davin, E.L., S.I. Seneviratne, P. Ciais, A. Olioso, and T. Wang, 2014: Preferential cooling of hot extremes from cropland albedo management. Proc. Natl. Acad. Sci. , 111(27) , 9757–9761, doi:10.1073/pnas.1317323111.

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Field, L. et al., 2018: Increasing Arctic Sea Ice Albedo Using Localized Reversible Geoengineering. Earth’s Future, 6(6) , 882–901, doi:10.1029/2018ef000820.

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Possible climate risks relate to direct and/or indirect land carbon losses (A/R, BECCS, biochar), increased N2O emissions (BECCS, SCS), saturation and non-permanence of carbon storage (A/R, SCS) (Jia et al. 2019; Smith et al. 2019b) (Chapter 7), and potential CO2 leakage from deep geological reservoirs (BECCS) (Chapter 6). Land cover change associated with A/R and biomass supply for BECCS and biochar may cause albedo changes that reduce mitigation effectiveness (Fuss et al. 2018; Jia et al. 2019). Potentially unfavourable albedo change resulting from biochar use can be minimised by incorporating biochar into the soil (Fuss et al. 2018) (Chapter 7).

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Impacts of biomass production and A/R on the hydrological cycle and water availability and quality depend on scale, location, previous land use/cover and type of biomass production system. For example, extraction of logging residues in forests managed for timber production has little effect on hydrological flows, while land-use change to establish dedicated biomass production can have a significant effect (Teter et al. 2018; Drews et al. 2020). Deployment of A/R can affect temperature, albedo and precipitation locally and regionally, and can mitigate or enhance the effects of climate change in the affected areas (Stenzel et al. 2021b) (Section 7.2.4). A/R activities can increase evapotranspiration, impacting groundwater and downstream water availability, but can also result in increased infiltration to groundwater and improved water quality (Farley et al. 2005; Zhang et al. 2016; Zhang et al. 2017; Lu et al. 2018) and can be beneficial where historical clearing has caused soil salinisation and stream salinity (Farrington and Salama 1996; Marcar 2016). There is limited evidence that very large-scale land-use or vegetation cover changes can alter regional climate and precipitation patterns, for example downwind precipitation depends on upwind evapotranspiration from forests and other vegetation (Keys et al. 2016; Ellison et al. 2017; van der Ent and Tuinenburg 2017).

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Deserts can be well suited for solar PV and CSP farms, especially at low latitudes where global horizontal irradiance is high, as there is lower competition for land and land carbon loss is minimal, although remote locations may pose challenges for power distribution (Xu et al. 2016). Solar arrays can reduce the albedo, particularly in desert landscapes, which can lead to local temperature increases and regional impacts on wind patterns (Millstein and Menon 2011). Modelling studies suggest that large-scale wind and solar farms, for example in the Sahara (Li et al. 2018), could increase rainfall through reduced albedo and increased surface roughness, stimulating vegetation growth and further increasing regional rainfall (Li et al. 2018) (limited evidence). Besides impacts at the site of deployment, wind and solar power affect land through mining of critical minerals required by these technologies (Viebahn et al. 2015; McLellan et al. 2016; Carrara et al. 2020).

albedoresources/ipcc/cleaned_content/wg3/Chapter12/html_with_ids.html#references_p1177

Wohlfahrt, G., E. Tomelleri, and A. Hammerle, 2021: The albedo–climate penalty of hydropower reservoirs. Nat. Energy 2021 64, 6(4) , 372–377, doi:10.1038/s41560-021-00784-y.

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The areas with relatively rapid oxygen decrease include OMZs in the tropical oceans, where oxygen content has been decreasing at a rate of 0.9–3.4 µmol kg–1 per decade in the thermocline for the past five decades (Stramma et al., 2008). Low oxygen, low pH and shallow aragonite saturation horizons in the OMZs of the eastern boundary upwelling regions co-occur, affecting ecosystem structure (Chavez et al., 2008) and function in the water column, including the presently unbalanced nitrogen cycle (Paulmier and Ruiz-Pino, 2009). The coupling between upwelling, productivity, and oxygen depletion feeds back to biological productivity and the role of these regions as sinks or sources of climate active gases. When OMZ waters upwell and impinge on the euphotic zone, they release significant quantities of GHGs, including N2O (0.81–1.35 TgN yr–1), CH4 (0.27–0.38 TgCH4yr–1), and CO2 (yet to be quantified) to the atmosphere, exacerbating global warming (Paulmier et al., 2008; Naqvi et al., 2010; Kock et al., 2012; Arévalo-Martínez et al., 2015; Babbin et al., 2015; Farías et al., 2015). Modelling projectionssuggest a global decrease of 4–12% in oceanic N2O emissions (from 3.71–4.03 TgN yr–1 to 3.54–3.56 TgN yr–1) from 2005 to 2100 under RCP8.5, despite a tendency to increased N2O production in the OMZs, associated primarily with denitrification (Martinez-Rey et al., 2015). It is difficult to single out the contribution of nitrification and denitrification, which can occur simultaneously. A rigorous separation of these two processes would require more mechanistic parametrization, which has been hindered by the still large conceptual and parametric uncertainties (Babbin et al., 2015; Trimmer et al., 2016; Landolfi et al., 2017). Furthermore, the correlation between N2O and oxygen varies with microorganisms present, nutrient concentrations, and other environmental variables (Voss et al., 2013).

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Temperate, non-upwelling coastal areas along the north-west Atlantic display a trend of decreasing seawater pH, mainly attributed to the combined effects of eutrophication and decreasing seawater buffering capacity (high agreement, robust evidence). Observations show an increasing north to south gradient of aragonite saturation state (Sutton et al., 2016; Fennel et al., 2019; Cai et al., 2020). Low alkalinity and total inorganic carbon concentration, combined with an ocean signal of acidification, diminishes the buffering capacity along the decreasing salinity gradient from the ocean to the coast. Models suggest that, in this area, the aragonite saturation is seasonally controlled by nutrient availability and primary production, supporting the finding that eutrophication is the main driver for exacerbating acidification (Cai et al., 2017, 2020). The coastal Gulf of Mexico is facing a parallel increase in bottom water acidification and deoxygenation off the Mississippi Delta driven by eutrophication (Cai et al., 2011; Laurent et al., 2017; Fennel et al., 2019).

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While TCs cause extreme local rainfall and flooding, they can be also an important contributor to annual precipitation and regional fresh water resources (Hristova-Veleva et al., 2020). Transport of moisture by TCs is an important contributor for precipitation over the coastal areas of East Asia mostly from July through October, with the TC rainfall accounting for nearly 10% to 30% of the total rainfall in the region (L. Guo et al., 2017). Local TC rainfall totals depend on rain-rate and translation speed (the speed of TC movement along the storm track) with slow TCs such as Hurricane Harvey (2017), providing a clear example of the effect of slow translation speed on local rainfall accumulation, with urbanization exacerbating the storm total rainfall and flooding (Section 11.7.1; W. Zhang et al., 2018).

exacerbatingresources/ipcc/cleaned_content/wg1/Chapter09/html_with_ids.html#references_p1261

Storlazzi, C.D. et al., 2018: Most atolls will be uninhabitable by the mid-21st century because of sea-level rise exacerbating wave-driven flooding. Science Advances, 4(4), eaap9741, doi: 10.1126/sciadv.aap9741.

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Projections of global changes in tropical cyclones indicate more frequent Category 4–5 storms (high confidence) and increased rain rates (high confidence) (Knutson et al., 2020), with relative sea level rise exacerbating storm surge potential, but with large regional differences (see Section 11.7.1.5). By the late 21st century, tropical cyclones are projected to be less frequent in the basins of the western and eastern North Pacific, Bay of Bengal, Caribbean Sea and in the Southern Hemisphere, but will be more frequent in the subtropical central Pacific (Murakami et al., 2014; Yoshida et al., 2017; Bell et al., 2019; Knutson et al., 2020). Over CAR, tropical cyclone intensity is expected to increase by the end of the century under RCP8.5 due to higher sea surface temperatures but can be inhibited by increases in vertical wind shear in the region (medium confidence) (Kossin, 2017; Ting et al., 2019). The poleward movement of the area in which tropical cyclones reach peak intensity in the western North Pacific is likely to continue, which affects the tropical cyclone frequency over the small islands in the area (Section 11.7.1.5; Kossin et al., 2016). Projections also indicate an increase (decrease) in the tropical cyclone frequency during El Niño (La Niña) events in the Pacific at the end of the 21st century (Chand et al., 2017). RCP8.5 2080–2099 projections indicate a 2% increase in the number of tropical cyclones in the north-central Pacific relative to 1980–1999, with tracks shifting northward towards Hawaii (N. Li et al., 2018). Given projected reductions to the overall number of tropical cyclones but increases in storm intensity, total rainfall and storm surge potential, we assess medium confidence of overall changes to tropical cyclones affecting the Caribbean and Pacific small islands.

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Storlazzi, C.D. et al., 2018: Most atolls will be uninhabitable by the mid-21st century because of sea-level rise exacerbating wave-driven flooding. Science Advances, 4(4), eaap9741, doi: 10.1126/sciadv.aap9741.

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Since AR5, rising public awareness and an increasing diversity of actors, have overall helped accelerate political commitment and global efforts to address climate change (medium confidence). Mass social movements have emerged as catalysing agents in some regions, often building on prior movements including Indigenous Peoples-led movements, youth movements, human rights movements, gender activism, and climate litigation, which is raising awareness and, in some cases, has influenced the outcome and ambition of climate governance. (medium confidence). Engaging Indigenous Peoples and local communities using just-transition and rights-based decision-making approaches, implemented through collective and participatory decision-making processes has enabled deeper ambition and accelerated action in different ways, and at all scales, depending on national circumstances (medium confidence). The media helps shape the public discourse about climate change. This can usefully build public support to accelerate climate action (medium evidence, high agreement ). In some instances, public discourses of media and organised counter movements have impeded climate action, exacerbating helplessness and disinformation and fuelling polarisation, with negative implications for climate action (medium confidence). {WGII SPM C.5.1, WGII SPM D.2, WGII TS.D.9, WGII TS.D.9.7, WGII TS.E.2.1, WGII 18.4; WGIII SPM D.3.3, WGIII SPM E.3.3, WGIII TS.6.1, WGIII 6.7, WGIII 13 ES, WGIII Box.13.7}

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Maladaptive responses to climate change can create lock-ins of vulnerability, exposure and risks that are difficult and expensive to change and exacerbate existing inequalities. Actions that focus on sectors and risks in isolation and on short-term gains often lead to maladaptation. Adaptation options can become maladaptive due to their environmental impacts that constrain ecosystem services and decrease biodiversity and ecosystem resilience to climate change or by causing adverse outcomes for different groups, exacerbating inequity. Maladaptation can be avoided by flexible, multi-sectoral, inclusive and long-term planning and implementation of adaptation actions with benefits to many sectors and systems. (high confidence). {WGII SPM C.4, WGII SPM.C.4.1, WGII SPM C.4.2, WGII SPM C.4.3}

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The estimated yield loss does not account for interactions with other climatic factors. Temperatures enhance not only ozone production but also ozone uptake by plants, exacerbating yield and quality damage. Burney (2014) estimated current yield losses due to the combined effects of ozone and heat in India at 36% for wheat and 20% for rice. Schauberger et al. (2019a) found global yield losses, ranging from 2% to 10% for soybean and 0% to 39% for wheat with a model that accounts for temperature, water and CO2 concentration on ozone uptake.

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Regional-scale assessment also highlights the importance of adaptive capacity. For instance, rice and maize production in Viet Nam Mekong Delta has high exposure to multiple climate hazards such as flooding, sea level rise, salinity intrusion and drought (Parker et al., 2019). Risks can be moderated by a relatively high adaptive capacity because of infrastructure, resources and high education levels (Parker et al., 2019). Another regional study demonstrated that erratic rains and high temperatures in southern and southeastern Africa increased the vulnerability of agricultural soils, thereby exacerbating impacts of prolonged and frequent droughts (Sonwa et al., 2017a; See also Box 5.4).

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Changes to the availability, abundance, access and storage of wild foods associated with changing climate are exacerbating high rates of food insecurity (high confidence) (Ford, 2009; Beaumier and Ford, 2010; Herman-Mercer et al., 2019). Wild foods are central to the food systems of communities throughout the Arctic and sub-Arctic (Kuhnlein et al., 1996; Ballew et al., 2006; Kuhnlein and Receveur, 2007; Johnson et al., 2009) and play an essential role in people’s physical and emotional health (Section CCP6.2.5; 2.8) (high confidence) (Loring and Gerlach, 2009; Cunsolo Willox et al., 2012). Wild foods consumed in the Arctic and northern regions include animals and a wide variety of plant foods (Wein et al., 1996; Ballew et al., 2006; Kuhnlein and Receveur, 2007). Wild foods contribute most of important nutrients in the diets of northern and Arctic people (Johnson et al., 2009; Wesche and Chan, 2010; Kenny et al., 2018). However, the use of traditional wild foods is declining across the region, lowering diet quality (Rosol et al., 2016). Indigenous communities in the Arctic perceive climate change related impacts on traditional wild foods, and availability and access to wild foods are forecast to continue to decline (Brinkman et al., 2016). Some communities hold positive views of the new opportunities a warmer climate will bring, seeing them as a favourable trade-off relative to the loss of some forms of subsistence hunting (Nuttall, 2009). Climate change is causing ecological changes that impact Arctic wild food availability and abundance in many different ways, including changes to breeding success, migration patterns and food webs (Table 5.10, Markon et al., 2018).

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Glacier mass and runoff in the Tropics are projected to diminish by >70% and >10%, respectively, by 2100, under mean of RCP2.6, 4.5 and 8.5 (Huss and Hock, 2018; Hock et al., 2019). In Peru, montane ice-field meltwater provides 80% of the water resources for the arid coast where half the population lives (Thompson et al., 2021). Increasing variability of precipitation has compromised rain-fed agriculture and power generation, particularly in the dry season, exacerbating pressures for new sources of water (Bradley et al., 2006; Bury et al., 2013; Buytaert et al., 2017). There is therefore a risk of increasing conflicts between adaptation to climate change to benefit human and natural communities in the high Andes and maintaining water provisioning for lowland agricultural and urban areas.

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Reforestation of previously forested areas can bring multiple benefits, but planting trees in places where they do not naturally grow can have serious environmental impacts, including potentially exacerbating the effects of climate change. Savannas are amongst the ecosystems at risk from afforestation programmes. Savannas are grass-dominated, high-diversity ecosystems with endemic species adapted to high-light environments, herbivory and fire (Staver et al., 2011; Murphy et al., 2016). Interactions between climate change, elevated CO2 and the disruption of natural disturbance regimes have led to the widespread encroachment of woody plants (Stevens et al., 2016), causing a fundamental shift in ecosystem structure and function with loss of grass and reduced fire frequency (Archibald et al., 2009) and stream flow (Honda and Durigan, 2016) (Sections 2.4.3.5, 2.5.2.5, Box 2.1, 2.5.4, TAble 2.5, Figure 2.11). Afforestation exacerbates this degradation (Bremer and Farley, 2010; Veldman et al., 2015; Abreu et al., 2017). Global-scale analyses aimed at identifying degraded forest areas suitable for reforestation (Veldman et al., 2019) cannot reliably separate naturally grassy ecosystems with sparse tree cover from degraded forests, so local information is essential to ensure tree planting is targeted where it can benefit most and avoid harm. Figure Box 2.2.1 indicates where these issues are most likely to arise.

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Acidification of estuarine water is a growing hazard (medium confidence) (Doney et al., 2020; Scanes et al., 2020a; Cai et al., 2021), and resident organisms display sensitivity to altered pH in laboratory settings (medium confidence) (Young et al., 2019a; Morrell and Gobler, 2020; Pardo and Costa, 2021). However, attribution of the biological effects of acidification is difficult because many biogeochemical processes affect estuarine carbon chemistry (Sections 3.2.3.1, 3.3.2). Warming can exacerbate the impacts of both acidification and hypoxia on estuarine organisms (Baumann and Smith, 2018; Collins et al., 2019b; Ni et al., 2020). These effects are further complicated by eutrophication, with high nitrogen loads associated with lower pH (Rheuban et al., 2019). Warming (including MHWs) and eutrophication interact to decrease estuarine oxygen content and pH, increasing the vulnerability of animals to MHWs (Brauko et al., 2020) and exacerbating the incidence and impact of dead zones (medium confidence) (Altieri and Gedan, 2015). The impacts of storms on estuaries are variable and are described in SM3.3.1.

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The extent of past degradation due to multiple drivers is important, as climate change is expected to interact synergistically and cumulatively with these (Finlayson et al., 2006), exacerbate existing problems for wetland managers and potentially increase emissions from carbon-rich wetland soils (Finlayson et al., 2017; Moomaw et al., 2018). Freshwater ecosystems are also under extreme pressure from changes in land use and water pollution, with climate change exacerbating these, such as the further decline of snow cover (DeBeer et al., 2016) and increased consumptive use of fresh water, and leading to the decline, and possibly extinction, of many freshwater-dependent populations (high confidence). Thus, differentiating between the impacts of multiple drivers is needed, especially given the synergistic and cumulative nature of such impacts, which remains a knowledge gap.

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Overall drought-driven yield loss is estimated to increase by 9–12% (wheat), 5.6–6.3% (maize), 18.1–19.4% (rice) and 15.1–16.1% (soybean) by 2071–2100, relative to 1961–2016 (RCP8.5) (Leng and Hall, 2019). In addition, temperature-driven increases in water vapour deficit could have additional negative effects, further exacerbating drought-induced plant mortality and thus impacting yields (Grossiord et al., 2020) (see also Cross-Chapter Box 1 in Chapter 5 of WGI report). Currently, global agricultural models do not fully differentiate crop responses to elevated CO2 under temperature and hydrological extremes (Deryng et al., 2016) and largely underestimate the effects of climate extremes (Schewe et al., 2019).

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While COVID-19 is an airborne disease (see Cross-Chapter Box COVID in Chapter 7), public health responses to the COVID-19 pandemic and the associated socioeconomic and environmental impacts of these measures intersect with WaSH (Armitage and Nellums, 2020a). Notably, COVID-19 and climate change act as compound risks in the context of water-induced disasters, exacerbating existing threats to sustainable development (Neal, 2020; Pelling et al. 2021).

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In AR5, there was medium evidence and high agreement that some adaptation and mitigation measures can lead to maladaptive outcomes, such as a rise in GHG emissions, while further exacerbating water scarcity leading to increased vulnerability to climate change, now or in the future (Noble et al., 2014). In addition, SR1.5 (Hoegh-Guldberg et al., 2018; IPCC, 2018a) and SRCCL (IPCC, 2019b) reiterated the challenge of trade-offs that may undermine sustainable development. Conversely, adaptation, when framed and implemented appropriately, can synergistically reduce emissions and enhance sustainable development.

exacerbatingresources/ipcc/cleaned_content/wg2/Chapter04/html_with_ids.html#references_p1418

Storlazzi, C.D., et al., 2018: Most atolls will be uninhabitable by the mid-21st century because of sea-level rise exacerbating wave-driven flooding. Sci. Adv. , 4 (4), eaap9741, doi:10.1126/sciadv.aap9741.

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Maladaptation refers to current or potential negative consequences of adaptation-related responses that lead to an increase in the climate vulnerability of a system, sector or group (Barnett and O’Neill, 2010) by exacerbating or shifting vulnerability or exposure now or in the future (Antwi-Agyei et al., 2014; Noble et al., 2014; Juhola et al., 2016; Magnan et al., 2020) and eroding sustainable development (Juhola et al., 2016). Conceptually, maladaptation differs from ‘failed’ or ‘unsuccessful’ adaptation (Schipper, 2020), which ‘describes a failed adaptation initiative not producing any significant detrimental effect’ (Magnan et al., 2016: 648). Several frameworks have been proposed to explain and better assess maladaptation (Hallegatte, 2009; Barnett and O’Neill, 2010; Magnan, 2014; Magnan et al., 2016; Gajjar et al., 2019b). To limit the risk of maladaptation, a common focus of these frameworks is on intentionally avoiding negative consequences of adaptation interventions, anticipating detrimental lock-ins and path dependence, and minimising spatio-temporal trade-offs/ dis-benefits.

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Some criteria score highly across a number of options (Figure 17.11, central panel columns), showing that many adaptations do not pay attention to different trade-offs. For example, particular attention should be paid to prioritising benefits to low-income groups and leveraging the transformational potential of adaptation (having the largest number of large circles), that is, many evaluated options become maladaptive by exacerbating the vulnerability of low-income groups and by fortifying the status quo (medium confidence). On the contrary, most evaluated adaptation options are widely applicable across populations (benefits to humans) and deliver ecosystem services, while some also respect gender equity (largest number of small bubbles across options). Through these criteria, a number of adaptation options contribute to a higher potential for successful adaptation (high confidence).

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Conceptually, the opposite of successful adaptation is maladaptation, that is, when adaptation responses produce unintended negative side effects such as exacerbating or shifting vulnerability, increasing risk for certain people or ecosystems, or increasing greenhouse gas emissions. Among the adaptation options assessed in this report (Figure FAQ17.5.1), physical infrastructure along coasts (e.g., sea walls) has the highest risk for maladaptation over time through negative side effects on ecosystem functioning and coastal livelihood opportunities. However, such adaptations may appear valuable in the short and even longer term for already densely populated urban coasts, demonstrating that an adaptation can be differently judged based on the context it is implemented in (Figure FAQ17.5.1). Many other adaptation options have a larger potential to contribute to successful adaptation (Figure FAQ17.5.1), such as nature restoration, providing social safety nets and changing diets/minimising food waste.

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Increasing urban drought risk will also have cascading impacts on regions from where water is imported, exacerbating drought exposure beyond urban settlements and limiting water availability in certain regions (Chuah et al., 2018; Garrick et al., 2019; Zhang et al., 2020c; Zhao et al., 2020). There is medium evidence (high agreement ) that urban water insecurity is experienced differentially based on income, risk exposure, and assets, and that urban drought and water scarcity is causing material and non-material losses and damage (Singh et al., 2021a). Importantly, in several Asian cities, flood and drought risk is expected to occur concurrently, especially in South Asia which is projected to see the largest increase in urban land exposed to both floods and droughts (25–32% increase in flood and drought risk between 2000 and 2030).

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There is medium evidence with high agreement that climatic risks are exacerbating internal and international migration across Asia (see Box 10.2; IDMC, 2019; Maharjan et al., 2020). In coastal cities, formal ‘retreat’ measures, such as forced displacement and planned relocation (Oppenheimer et al., 2019), are commonly considered ‘last resort’ adaptation strategies once other infrastructural and ecosystem-based protect-and-accommodate strategies are exhausted (CCP2.3) (Haasnoot et al., 2019). In contrast, migration (which can take various forms from seasonal, temporary mobility to circular or permanent movement) is a regular feature across Asian urban settlements (Box 10.2, CCB MIGRATE, Maharjan et al., 2020).

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Climatologically, the summer surface air temperature in South, Southeast and Southwest Asia is high, and its coastal area is very humid. In these regions, heat stress is already a medium risk for humans. Large cities are warmer by more than 2°C compared with the surroundings due to heat island effects, exacerbating heat stress conditions. Future warming will cause more frequent temperature extremes and heatwaves especially in densely populated South Asian cities, where working conditions will be exacerbated and daytime outdoor work will become dangerous. For example, incidence of excess heat-related mortality in 51 cities in China is estimated to reach 37,800 deaths per year over a 20-year period in the mid-21st century (2041–2060) under the RCP8.5 scenario.

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However, evidence also suggests limitations of adaptation actions, with the objectives and actions often being too narrow to address social justice and enable CRD. As such, adaptation actions can sometimes undermine SDG achievement through exacerbating social vulnerability, inequity and uneven power relations (Antwi-Agyei et al., 2018; Atteridge and Remling, 2018; Paprocki, 2018; Mikulewicz, 2019; Satyal et al., 2020; Scoville-Simonds et al., 2020). This is due to adaptation practices often not accounting for the differentiated ways in which minority groups are especially vulnerable. For example, designs of emergency shelters should consider the fear of social stigma or abuse faced by women and girls (Pelling and Garschagen, 2019).

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An effective and innovative technological regime is one that is integrated with local social entities across different modes of life, local governance processes (Pereira, 2018; Nightingale et al., 2020) and local knowledge(s), which increasingly support adaptation to socio-environmental drivers of vulnerability (Schipper et al., 2014; Nalau et al., 2018; IPCC, 2019 f). These actors and their knowledge are often ignored in favour of knowledge held by experts and policymakers, exacerbating uneven power relations (Naess, 2013; Nightingale et al., 2020). For example, achieving sustainability and shifting towards a low carbon energy system (e.g., hydropower dams, wind farms) remains a contested space with divergent interests, values and future prospects (Bradley and Hedrén, 2014; Avila, 2018; Mikulewicz, 2019), and potential impacts on human rights as embodied by the Paris Agreement (UNFCCC, 2015). A number of studies have emphasised the limits of relying upon technology innovation and deployment (e.g., expansion of renewable energy systems and/or carbon capture) as a solution to challenges of climate change and sustainable development (Section 18.3.1.2). This is because such solutions may fail to consider the local historical contexts and barriers to participation of vulnerable communities, restricting their access to land, food, energy and resources for their livelihoods.

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Regional climate change has continued since AR5 was released in 2014, with trends exacerbating many extreme events (very high confidence). The following changes are quantified with references in Tables 11.2a and 11.2b. The region has continued to warm (Figure 11.1), with more extremely high temperatures and fewer extremely low temperatures. Snow depths and glacier volumes have declined. Sea level rise and ocean acidification have continued. Northern Australia has become wetter, while April–October rainfall has decreased in south-western and south-eastern Australia. In New Zealand, most of the south has become wetter, while most of the north has become drier (Figure 11.2). The frequency, severity and duration of extreme fire weather conditions have increased in southern and eastern Australia and eastern New Zealand. Changes in extreme rainfall are mixed. There has been a decline in tropical cyclone frequency near Australia.

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Altered water regimes resulting from the combined effects of climatic conditions and water policies carry uneven and far-reaching implications for communities (high confidence). Acting on Indigenous Peoples’ claims to cultural flows (to maintain their connections with their country) is increasingly recognised as an important water management and social justice issue (Taylor et al., 2017; Hartwig et al., 2018; Jackson, 2018; Jackson and Moggridge, 2019; Moggridge et al., 2019). Compounding stressors, such as coal and coal seam gas developments, can also severely impact local communities, water catchments and water-dependent ecosystems and assets, exacerbating their vulnerability to climate change (Navi et al., 2015; Tan et al., 2015; Chiew et al., 2018).

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Climatic extremes are exacerbating existing vulnerabilities (high confidence). Long supply chains, poorly maintained infrastructure, social disadvantage and poor health and lack of skilled workers (Eldridge and Beecham, 2018; Mathew et al., 2018; Rolfe et al., 2020) are contributing to serious stress and disruption (Smith and Lawrence, 2014; Kiem et al., 2016). In many rural settlements, population ageing and reliance on an overstretched volunteer base for recovery from extreme events are increasing vulnerability to climate change (Astill and Miller, 2018; Davies et al., 2018). Recovery from long, intense, more frequent and compounding climatic events in rural areas has been disrupted by the erosion of natural, financial, built, human and social capital (De et al., 2016; Sheng and Xu, 2019). Delayed recovery from extreme climatic events has been compounded by long-term displacement, which in turn prolongs the impacts (Matthews et al., 2019). Severe droughts have contributed to poor health outcomes for rural communities, including extreme stress and suicide (Beautrais, 2018; Perceval et al., 2019). In Australia, competition among water users has left some rural communities experiencing extreme water shortage and insecurity with associated health impacts (Wheeler et al., 2018; Judd, 2019) (Box 11.3).

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Unless there is more effective control of nutrient runoff, bacterial contamination of drinking water supplies is projected to increase due to more intense rainfall events, exacerbating risks to human health (Gilpin et al., 2020; Lai et al., 2020), and higher temperatures will increase freshwater toxic blooms (Hamilton et al., 2016).

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Due to ongoing impacts of colonisation, Aboriginal and Torres Strait Islander Peoples have, on average, lower income, poorer nutrition, lower school outcomes and employment opportunities, higher incarceration and higher levels of removal of children than non-Indigenous Australians, represented in high comorbidities of chronic diseases and mental health impacts (Marmot, 2011; Green and Minchin, 2014; AIHW, 2015). This relative poverty can reduce climate-adaptive capacities while exacerbating climate change vulnerabilities (Nursey-Bray and Palmer, 2018). In remote country, this can combine with lack of security for food and water, non-resilient housing and extreme weather events, contributing to migration off traditional country and into towns and cities—with flow-on social impacts such as homelessness, dislocation from community and family and disconnection from country and spirituality (Mosby, 2012; Brand et al., 2016).

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Climate impacts are cascading, compounding and aggregating across sectors and systems due to complex interactions (high confidence) (Pescaroli and Alexander, 2016; Challinor et al., 2018; Zscheischler et al., 2018; Steffen et al., 2019; AghaKouchak et al., 2020; CoA, 2020e; Lawrence et al., 2020b; Simpson et al., 2021) (Boxes 11.1, 11.3, 11.4, 11.5 and 11.6). Cascading impacts propagate via interconnections and systemic factors, including supply chains, shared reliance on connected biophysical systems (e.g., water catchments and ecosystems), infrastructure, essential goods and services and the exercise of governance, leadership, regulation, resources and standard practices (e.g., in planning and building codes), including lock-in of past decisions and experience (CSIRO, 2018; Lawrence et al., 2020b). The capacity of critical systems such as information, communication and technology, water infrastructure, health care, electricity and transport networks, is being stretched, with impacts cascading to other systems and places, exacerbating existing hazards and generating new risks (Cradock-Henry, 2017) (11.3.6; 11.3.10; Box 11.1). Temporal or spatial overlap of hazards (e.g., drought, extreme heat and fire; drought followed by extreme rainfall) are compounding impacts (Zscheischler et al., 2018) and affecting multiple sectors.

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Current global emissions reduction policies are projected to lead to a global warming of 2.1°C–3.9°C by 2100 (Liu and Raftery, 2021), leaving many of the region’s human and natural systems at very high risk and beyond adaptation limits (high confidence). With higher levels of warming, adaptation costs increase, loss and damages grow, and governance and institutional responses reduce adaptive capacity. Underlying social and economic vulnerabilities and injustices further reduce adaptive capacity, exacerbating disadvantage in particular groups in society. Sustainable development across and beyond the region will help reduce shared adaptation challenges (11.5.1.2). Effective adaptation avoids lock-in and path dependency, reduces vulnerabilities, increases flexibility to change, builds adaptive capacity and advances SDGs, thereby improving intra- and intergenerational justice (11.5, 11.6, 11.7). Reducing greenhouse gas emissions and structural inequalities is key to achieving the SDGs and contributing to climate resilient development.

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Many climate responses interact with all of these global risk drivers. Some raise additional equity concerns about marginalising those most vulnerable and exacerbating social conflicts (Oppenheimer et al., 2019), leading to wider questions about the governance of climate risks (and impacts) across scales. Hence, our assessment of impacts, responses and risks is complemented by the assessment of governance and the enabling environment for risk management in Chapter 17, and of climate resilient development in Chapter 18.

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Climate change also could increase income inequality between countries (high confidence) as well as within them (medium evidence, high agreement ) resulting from and exacerbating impacts on aggregate economic activity, poverty and livelihoods. Increasing inequality implies larger impacts on the least well-off, threatens their ability to respond to climate hazards, compromises basic principles of fairness and established global development goals, and potentially threatens the functioning of society and long-term progress (Roe and Siegel, 2011; Cingano, 2014; van der Weide and Milanovic, 2018). There is evidence that warming has slowed down the convergence in between-country income in recent decades (Diffenbaugh and Burke, 2019). Future impacts may halt or even reverse this trend during this century owing to high sensitivity of developing economies (Burke et al., 2015; Pretis et al., 2018; Baarsch et al., 2020), although projections depend as much or more on future socioeconomic development pathways and mitigation policies as on warming levels (Takakura et al., 2019; Harding et al., 2020; Taconet et al., 2020). Within countries, studies that find adverse impacts on low-income groups imply an increase in inequality (Hallegatte and Rozenberg, 2017; Hsiang et al., 2017), although evidence for long-term climate impacts on within-country inequality at global scale remains limited.

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Storlazzi, C.D., et al., 2018: Most atolls will be uninhabitable by the mid-21st century because of sea-level rise exacerbating wave-driven flooding. Sci. Adv. , 4 (4), eaap9741, doi:10.1126/sciadv.aap9741.

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Many of the observed outcomes of climate change, for example, migration, are also outcomes of multidimensional poverty in low-income countries (Burrows and Kinney, 2016). Future impacts may be better understood if the vulnerability and the capacity for adaptation is understood to be rooted in a sustainable development context (see Box 8.2). The UN Sustainable Development Goals (SDGs), which aim to reduce poverty and inequality, and identify options for achieving development progress, also provide insight on reducing climate vulnerability (United Nations, 2015). First, climate change impacts may undermine progress toward various SDGs (medium confidence), primarily poverty reduction (SDG1), zero hunger (SDG2), gender equality (SDG5) and reducing inequality (SDG10), among others (medium evidence, high agreement ). In both developing and high-income countries, climate change hazards in connection with other non-climatic drivers already accelerate trends of wealth inequality (SDG 1) (Leal Filho et al., 2020b). Climate impacts on SDGs illustrate the complex interrelations in development. For example, in regions encountering obstacles to SDGs, characterised by high levels of inequality and poverty, such as in Africa, Central Asia and Central America, climate change is exacerbating water insecurity (SDG 6), which may then also drive food insecurity (SDG 2), impacting the poor directly (e.g., via crop failure), or indirectly (e.g., via rising food prices) (Conway et al., 2015; Hertel, 2015; Cheeseman, 2016; Rasul and Sharma, 2016). There is a pressing need to address poverty issues, since these may negatively influence the implementation of all SDGs (Leal Filho et al., 2021a).

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UN-DESA, 2020a: Climate change: exacerbating poverty and inequality. In: World Social Report 2020. Inequality in a Rapidly Changing World. United Nation, New York, pp. 81–106. ISBN 9789210043670.

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Cities and urbanising areas are currently home to over half the world’s population. What happens in cities is crucial to successful adaptation (Grafakos et al., 2019). By 2050, over two thirds of the world’s population is expected to be urban, many living in unplanned and informal settlements and in smaller urban centres in Africa and Asia (high confidence) (UNDESA, 2018). Between 2015 and 2020, urban populations globally have grown by about 397 million people, with more than 90% of this growth taking place in less developed countries (UNDESA, 2018). Projections of the number of people expected to live in urban areas highly exposed to climate change impacts have also increased, exacerbating future risks under a range of climate scenarios. Rates of population growth are most pronounced in smaller and medium-sized settlements of up to 1 million people (UNDESA, 2018).

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Decreased regional precipitation and associated changes in runoff and storage from droughts is exacerbating urban scarcity by impairing the quality of water available for its resource management in cities (high confidence). For example, less runoff to freshwater rivers can increase salinity and concentrate pathogens and pollutants that increases risks of urban water scarcity (Hellwig, Stahl and Lange, 2017; Jones and van Vliet, 2018; Leddin and Macrae, 2020; Lorenzo and Kinzig, 2020; Ma et al., 2020; Mosley, 2015; Zhang et al., 2019; van Vliet, Flörke and Wada, 2017; see also Box 6.2). Drought also changes the dynamics of groundwater pollution, leading to increased environmental health risks when those sources are used for urban water supplies (Kubicz et al., 2021; Moreira et al., 2020; Pincetl et al., 2019). Changes in the nature of droughts, for example, hotter droughts (Herrera and Ault, 2017), snow droughts (Cooper, Nolin and Safeeq, 2016; Mote et al., 2016) or ‘flash’ droughts (Otkin et al., 2016; Otkin et al., 2018; Pendergrass et al., 2020) can exacerbate urban water scarcity, exposing the limitations of engineered water infrastructure designed to accommodate historical patterns of supply and demand (Gober et al., 2016; Ulibarri and Scott, 2019; Zhao et al., 2018a).

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Climate change can be a threat multiplier in cities and urban regions, exacerbating existing human security tension (limited evidence, medium agreement ) (Froese and Schilling, 2019; Flörke, Schneider and McDonald, 2018; Rajsekhar and Gorelick, 2017). Where conflict or administrative tensions extend beyond cities, adapting regional infrastructure systems that underpin urban life is challenging, for example where elements of networked infrastructure are under the control of conflicting political interests. This has been noted for the water sector (Tänzler, Maas and Carius, 2010). Coordinating political processes is a major challenge even for industrialised countries with adequate administrative capacity. In post-conflict societies, the difficulties of coordination for urban planning are disproportionately greater (Sovacool, Tan-Mullins and Abrahamse, 2018).

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Adaptation to prevent climate change from exacerbating conflict risk involves meeting development objectives encapsulated in the SDGs. Conflict-sensitive adaptation and climate-sensitive peacebuilding offer promising avenues to addressing conflict risk, but their efficacy is yet to be demonstrated through effective monitoring and evaluation (Gilmore et al., 2018). Associations between environmental factors and conflict are weak in comparison to socioeconomic and political drivers. Therefore, meeting the SDGs, including Goal 16 on peace, justice and strong institutions represent unambiguous pathways to reducing conflict risk under climate change (Singh and Chudasama, 2021). Actively pursuing peace rather than taking conflict for granted (Barnett, 2019), improving focus on gender within peacebuilding (Dunn and Matthew, 2015; UNEP, 2021) and understanding how natural resources and their governance interact with peacebuilding (Krampe et al., 2021) present key elements of CRDPs for sustainable peace.

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In Africa, placing cross-sectoral approaches at the core of CRD provides significant opportunities to deliver large benefits and/or avoided damages across multiple sectors including water, health, ecosystems and economies (very high confidence) (Boxes 9.5; 9.6; 9.7). They can also prevent adaptation or mitigation action in one sector exacerbating risks in other sectors and resulting in maladaptation, for example, from large-scale dam construction or large-scale afforestation (e.g., water–energy–food nexus and large-scale tree planting efforts) (Boxes 9.3; 9.5).

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Changes in the amplitude, timing and frequency of extreme events such as droughts and floods will continue to affect lake levels, rates of river discharge and runoff and groundwater recharge (high confidence) (Gownaris et al., 2016; Darko et al., 2019), but with disparate effects at regional, basin and sub-basin scales, and at seasonal, annual and longer timescales. The increased frequency of extreme rainfall events under climate change (Myhre et al., 2019) is projected to amplify groundwater recharge in drylands (Jasechko and Taylor, 2015; Cuthbert et al., 2019). However, declining trends in rainfall and snowfall in some areas of north Africa (Donat et al., 2014b) are projected to continue in a warming world (Seif-Ennasr et al., 2016), restricting groundwater recharge from meltwater flows, exacerbating the salinisation and depletion of groundwater (Hamed et al., 2018) and increasing the risk of reduced soil moisture (Petrova et al., 2018) in this region where groundwater abstraction is greatest (Wada et al., 2014).

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While natural variability in the hydrological cycle has always been considered by water resources planners and engineers (Müller Schmied et al., 2016; Muller, 2018), many countries will have to take into consideration the range of historically unprecedented extremes expected in the future. Increasingly, the provision of urban water security is dependent on the functioning of complex bulk water infrastructure systems consisting of dams, inter-basin transfers, pipelines, pump stations, water treatment plants and distribution networks (McDonald et al., 2014). Risk-based studies on the potential climate change risks for water security show that there are benefits when risks are reduced at the tails of the distribution—floods and droughts—even if there is little benefit in terms of changes in the mean (Arndt et al., 2019). When risk is taken into account in an integrated (national) bulk water infrastructure supply system, the overall impact of climate change on the average availability of water to meet current and future demands is significantly reduced (Cullis et al., 2015). Further, stemming leakages and enhancing efficiency through technology and management improvements is important in building climate-resilient water conveyance systems (UN Environment, 2019). African cities could leap-frog through the development phases to achieve a water sensitive city ideal, reaping benefits such as improved liveability, reduced flooding impacts, safe water and overall lower net energy requirements and avoid making the mistakes developed countries’ cities have made (Fisher-Jeffes et al., 2017) (Brodnik et al., 2018). However, the challenge of large proportions of the population lacking access to even basic water supply and sanitation infrastructure (Armitage et al., 2014) must be simultaneously and effectively addressed, particularly in light of other major exacerbating factors, like the COVID-19 pandemic (Section 9.11.5).

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Climate-related displacement is widespread in Africa, with increased migration to urban areas in sub-Saharan Africa linked to decreased rainfall in rural areas, increasing urbanisation and affecting household vulnerability (see Box 9.9). Much of this growth can occur in informal settlements which are growing due to both climatic and non-climatic drivers, and which often house temporary migrants, including internally displaced people. Such informal settlements are located in areas exposed to climate change and variability and are exposed to floods, landslides, sea level rise and storm surges in low-lying coastal areas, or alongside rivers that frequently overflow, thereby exacerbating existing vulnerabilities (Satterthwaite et al., 2020).

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The COVID-19 pandemic recovery effort includes significant opportunities for African countries to reduce future vulnerability to compound climate, economic and health risks. Fiscal recovery packages could set economies on a pathway towards net-zero emissions, reducing future climate risk or entrench fossil-fuel intensive systems, exacerbating risk (Hepburn et al., 2020; Dibley et al., 2021; IEA, 2021). Investments in renewable energy, building efficiency retrofits, education and training, natural capital (i.e., ecosystem restoration and EbA), R&D, connectivity infrastructure and sustainable agriculture can help meet both economic recovery and climate goals (Hepburn et al., 2020; Dibley et al., 2021).

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Risks to marine and coastal European ecosystems are very likely to intensify (Figure 13.11) in response to projected further warming. Since the capacity of natural systems for autonomous adaptation is limited (medium confidence) (Thomsen et al., 2017; Miller et al., 2018; Bindoff et al., 2019), pronounced changes in community composition and biodiversity patterns are projected by 2100 for TEUS and the eastern Mediterranean Sea (SEUS) for >3°C GWL (García Molinos et al., 2016), challenging conservation efforts (Corrales et al., 2018; Cramer et al., 2018; Kim et al., 2019). At 1.5°C GWL, particularly in winter, Mediterranean coastal fish communities are projected to lose ~10% of species, increasing to ~60% at 4°C GWL (Dahlke et al., 2020), exacerbating regime shifts linked to overexploitation (medium confidence) (Clark et al., 2020). Warming at this level will threaten many species currently living in marine protected areas (MPAs) in TEUS and NEUS (Bruno et al., 2018). Increasing marine heatwaves (MWHs), particularly in SEUS at 4°C GWL (Darmaraki et al., 2019a), elevate risks for species (Galli et al., 2017), coastal biodiversity, and ecosystem functions, goods and services (Smale et al., 2019); however, MWH-related risk levels differ among biotas (Pansch et al., 2018) and across European seas (Smale et al., 2015).

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The main drivers of allergies are predominantly non-climatic (e.g., increased urbanisation, adoption of westernised lifestyles, social and genetic factors), but climate change strongly contributes to the spread of some allergenic plants, thus exacerbating existing allergies and causing new ones in people across Europe (high confidence) (D’Amato et al., 2016; EASAC, 2019). The prevalence of hay fever (allergic rhinitis), for example, is between 4 and 30% among European adults (Pawankar et al., 2013). The invasive common ragweed (Ambrosia asteraceae) is a key species already causing major allergy in late summers (including hay fever and asthma), particularly in Hungary, Romania and parts of Russia (Ambelas Skjøth et al., 2019). Across Europe, sensitisation to ragweed is expected to increase from 33 million people in 1986–2005 to 77 million people at 2°C GWL (Lake et al., 2017).

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Climate change is projected to reduce groundwater recharge in major southwest US aquifers (e.g., Southern High Plains, San Pedro and Wasatch Front), exacerbating their ongoing depletion due to unsustainable pumping. Other aquifers, especially those farther north, face uncertain or possibly increasing recharge (medium confidence) (Meixner et al., 2016).

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Climate change will affect ecosystems (Section 16.5.2.3), living standards (Section 16.5.2.3.4), health (Section 16.5.2.3.5) and food security (Section 16.5.2.3.6) globally, and these changes may exacerbate violence and political instability (medium confidence) with implications for national security in North America (medium confidence). Climate variability, hazards and trends, to date, have played a role in exacerbating conflict, but the influence of climate appears to be minor and more uncertain than the roles of low socioeconomic development, low state capability and high intergroup inequality (Mach et al., 2019). More profound impacts from climate change on weather and seasons, as well as changing socioeconomic conditions, could lead to patterns of violence that cannot be predicted by projecting relationships between current climate and violence into the future (Section 14.6.3; Mach et al., 2019). If global levels of violence increase, there will be increased demand for international efforts, including disaster aid and humanitarian efforts (Eyzaguirre et al., 2021). Climate change and geopolitical goals interact in the Arctic (Smith et al., 2018). New transportation corridors and the potential access to natural resources could lead to competition for access to and control over the region (Section CCP6.2.6; see Box CCP6.1; FAQ CCP6.2; Estrada, 2021). Governance structures exist to manage geopolitical manoeuvring and to protect the human security of Arctic populations (Sections 14.5.10.3, 7.2.7.1).

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Population growth plays a major role in projected future water stress (Schewe et al., 2014). Combining projected aridity change (fractional change compared to historical climatology) with population projections derived from SSP2 shows that the SIDS with high projected population growth rates are expected to experience the most severe freshwater stress by 2030 under a 2°C warming threshold scenario (Karnauskas et al., 2018). For several SIDS (e.g., Belize and Jamaica), increasing aridity change is a prominent exacerbating factor, but for others (e.g., the Solomon Islands and Comoros) population growth is the main factor. An increase in temperature of 1°C (from 1.7°C to 2.7°C) could result in a 60% increase in the number of people projected to experience severe water resources stress in the period 2043–2071 (Schewe et al., 2014; Karnauskas et al., 2018). Research on Jamaica concluded that the ability of rainwater harvesting to meet potable water needs between the 2030s and 2050s will be reduced based on predicted shorter intense showers and frequent dry spells (Aladenola et al., 2016).

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Storlazzi, C., et al., 2018: Most atolls will be uninhabitable by the mid-21st century because of seal-level rise exacerbating wave-driven flooding. Sci. Adv. , 4, eaap9741, doi:10.1126/sciadv.aap9741.

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CSA shows increasing trends of climatic change and variability and extreme events severely impacting the region, exacerbating problems of rampant and persistent poverty, precarious health systems and water and sanitation services, malnutrition and pollution. Inadequate governance and lack of participation escalates the vulnerability and risk to climate variability and change in the region (high confidence) (WGII AR5 Chapter 27) (Magrin et al., 2014).

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Climate change is exacerbating socioeconomic vulnerability in CA, a region with high levels of socioeconomic, ethnic and gender inequality, high rates of child and maternal mortality and morbidity, high levels of malnutrition and inadequate access to food and drinking water (ECLAC et al., 2015). Disasters from adverse natural events exacerbate CA’s economic vulnerability, accounting for substantial human and economic losses (UNISDR and CEPREDENAC, 2014). Vulnerability in most sectors is considered high or very high (high confidence) (Figure 12.7).

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Heat stress is another health concern in this already warm and humid part of the world (high confidence) (Table 12.2); it is an increasing occupational health hazard with potential impacts on kidney disease (Sheffield et al., 2013; Dally et al., 2018; Johnson et al., 2019). SLR exacerbating wave-driven flooding is expected to impact infrastructure and freshwater availability in small islands and atolls off the coast of Belize (Storlazzi et al., 2018). Observed and expected impacts in the coastal and ocean ecosystems of the sub-region are described in Figure 12.9.

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Indigenous Peoples and resource-dependent rural communities in the Amazon have been impacted over the last decade by extreme drought and flood events in various dimensions of their livelihoods (Pinho et al., 2015). Food security has been strongly impacted since it is based on fishing and small-scale agriculture, two sectors highly vulnerable to climate change. During extreme events, fishing decreases due to limited access to fishing grounds (medium confidence: low evidence, high agreement ) (Figure 12.9) (Pinho et al., 2015; Camacho Guerreiro et al., 2016. Overfishing, deforestation and dam construction are a threat to fishing in the sub-region (Lopes et al., 2019) and therefore contribute to exacerbating the impacts of climate change. Small-scale agriculture practices (e.g., floodplain agriculture and slash and burn) are highly coupled with natural hydrological cycles and therefore severely affected by extreme events (Figure 12.9) (Cochran et al., 2016). Livelihoods are also impacted by disruptions in land and river transport, restrictions in drinking water access, increased incidence of forest fires and disease outbreaks (medium confidence: medium evidence, high agreement ) (Figure 12.9) (Marengo et al., 2013; Pinho et al., 2015; Marengo and Espinoza, 2016; Marengo et al., 2018). In addition, flood events have caused losses of homes and disruption of public and commercial services (Figure 12.9) (Parry et al., 2018).

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Climate-change impacts are increasing and exacerbating poverty and social inequalities, affecting those already vulnerable and disfavoured, generating new and interlinked risk and challenging climate resilient development pathways (high confidence) (Section 8.2.1.4; Shi et al., 2016; Otto et al., 2017; Johnson et al., 2021). Poverty, high levels of inequality and pre-existing vulnerabilities can also be worsened by climate-change policies (Antwi-Agyei et al., 2018; IPCC, 2018; Roy et al., 2018; Eriksen et al., 2021). Those already suffering are losing their livelihoods and reducing their development options; poor populations and countries are more vulnerable and have lower adaptive capacity to climate change compared to rich ones (very high confidence) (Section 8.5.2.1; Rao et al., 2017).

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Impacts are likely to occur simultaneously, exacerbating the challenges faced by the poorer segments of society, but also creating new groups at risk (Miranda Sara et al., 2016; Rosenzweig et al., 2018; Dodman et al., 2019). The material basis for poor and vulnerable urban and rural populations’ adaptations is in a critical state across the CSA region, magnifying extreme events’ impacts, making CSA less resilient. Consequences in terms of social vulnerability and livelihood will be widely felt, inasmuch as the security and protection of critical assets (housing, infrastructure and water, land and ecosystem services) continue to lag behind. Small businesses are usually located within homes, and if the home is affected, so is the business (Stein and Moser, 2015), adding another layer of vulnerability for this population.

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Migration to cities can mean opportunities for migrants and for urban areas, but it can also worsen existing problems, as urban poor people can become even more exposed and vulnerable, and the pressure on urban capacities may not be well absorbed (high confidence) (Chisari and Miller, 2016; Gemenne et al., 2020). Internal migration to cities is likely to exacerbate pre-existing vulnerabilities related to inequality, poverty, indigence and informal activities and housing (Warn and Adamo, 2014). Immigration can make cities/residents more vulnerable to climate-change risks (Sections 12.5.5 and 12.5.7). Groups such as children, Indigenous Peoples and the poor are usually among the most vulnerable in migrations and displacements, which poses challenges to national policies and international aid (Sedeh, 2014; Gamez, 2016; Ulla, 2016; Priotto and Salvador Aruj, 2017; Ramos and de Salles Cavedon-Capdeville, 2017; Amar-Amar et al., 2019; Gemenne et al., 2020). In migration or displacement driven by climate effects, women are prone to lose their leadership, autonomy and voice, especially in new organisational structures imposed by authorities. This is especially the case in temporary accommodation camps created after disasters, exacerbating existing differentiated vulnerabilities (Aldunce Ide et al., 2020). International migration has become more dangerous and difficult as border controls have become stricter, but programmes such as one to help temporary agricultural workers from Guatemala to Canada have proven successful (Gabriel and Macdonald, 2018). At the same time, emigration may lead to the loss of IKLK for adaptation (Moreno et al., 2020b).

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Poor populations have little or no access to good-quality education, information, health systems and financial services. They have fewer chances to access resources, such as land and water, good-quality housing, risk-reducing infrastructure, and services, such as running water, sanitation and drainage. Their lack of political clout and endowments limits their access to assets for withstanding and recovering from shocks and stresses. Poverty, inequality and high vulnerability to the impacts of climate change are interrelated processes. Poor populations are highly vulnerable to the impacts of climate change and are usually located in areas of high exposure to extreme events. The constant loss of assets and livelihoods in both urban and rural areas drives communities into chronic poverty traps, exacerbating local poverty cycles and creating new ones.

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Storlazzi, C.D., et al., 2018: Most atolls will be uninhabitable by the mid-21st century because of sea-level rise exacerbating wave-driven flooding. Sci. Adv. , 4 (4), eaap9741, doi:10.1126/sciadv.aap9741.

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Policies that support the avoidance of higher-emission lifestyles and improve well-being are facilitated by the introduction of smart technologies, infrastructures and practices (Amini et al. 2019). They include regulations and measures for investment in high-quality ICT infrastructure and regulations to restrict number plates, as well as company policy around flexible working conditions (Lachapelle et al. 2018; Shabanpour et al. 2018). Working-from-home arrangements may advantage certain segments of society such as male, older, higher-educated and highly-paid employees, potentially exacerbating existing inequalities in the labour market (Lambert et al. 2020; Bonacini et al. 2021). In the absence of distributive or other equity-based measures, the potential gains in terms of emissions reduction may therefore be counteracted by the cost of increasing inequality. This potential growth in inequality is likely to be more severe in poorer countries that will additionally suffer from a lack of international funding for achieving the SDGs ( high evidence, medium agreement ) (Barbier and Burgess 2020; UN 2020).

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There is strong agreement that the climate policy institutional framework as well as technological progress have a profound impact on the attainability of low-carbon pathways. Delaying international cooperation reduces the available carbon budget and locks into carbon-intensive infrastructure exacerbating implementation challenges (Keppo and Rao 2007; Bosetti et al. 2009; Boucher et al. 2009; Clarke et al. 2009; Krey and Riahi 2009; van Vliet et al. 2009; Knopf et al. 2011; Jakob et al. 2012; Luderer et al. 2013; Rogelj et al. 2013a; Aboumahboub et al. 2014; Kriegler et al. 2014a; Popp et al. 2014; Riahi et al. 2015; Gambhir et al. 2017; Bertram et al. 2021). Similarly, technological availability influences the feasibility of climate stabilisation, though differently for different technologies (Kriegler et al. 2014a; Iyer et al. 2015a; Riahi et al. 2015).

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Scaling up transformative place-based action for both adaptation and mitigation requires enabling conditions, including land-based financing, intermediaries, and local partnerships (medium evidence, high agreement ) (Chu et al. 2019; Chaudhuri, 2020) supported by a new generation of big data approaches. Governance structures that combine actors working at different levels with a different mix of tools are effective in addressing challenges related to implementation of integrated action while cross-sectoral coordination is necessary (Singh et al. 2020). Joint institutionalisation of mitigation and adaptation in local governance structures can also enable integrated action (Göpfert et al. 2020; Hurlimann et al. 2021). However, the proportion of international finance that reaches local recipients remains low, despite the repeated focus of climate policy on place-based adaptation and mitigation (Manuamorn et al. 2020). Green financing instruments that enable local climate action without exacerbating current forms of inequality can jointly address mitigation, adaptation, and sustainable development. Climate finance that also reaches beyond larger non-state enterprises (e.g., small and medium-sized enterprises, local communities, or non-governmental organisations (NGOs)), and is inclusive in responding to the needs of all urban inhabitants (e.g., disabled individuals, or citizens of different races or ethnicities) is essential for inclusive and resilient urban development (Colenbrander et al. 2019; Gabaldón-Estevan et al. 2019; Frenova 2021). Developing networks that can exert climate action at scale is another priority for climate finance.

exacerbatingresources/ipcc/cleaned_content/wg3/Chapter01/html_with_ids.html#1.7.2.2_p2

If attention is not paid to equity, efforts designed to tackle climate change may end up exacerbating inequities among communities and between countries (Heffron and McCauley 2018). The implication is that to be sustainable in the long run, mitigation involves a central place for consideration of justice, both within and between countries (Chapters 4 and 14). Arguments that the injustices following from climate change are symptomatic of a more fundamental structural injustice in social relations, are taken to imply a need to address the deeper inequities within societies (Routledge et al. 2018).

exacerbatingresources/ipcc/cleaned_content/wg3/Chapter09/html_with_ids.html#9.7.1_p1

A large body of literature on climate impacts on buildings focuses on the impacts of climate change on heating and cooling needs (de Wilde and Coley 2012; Wan et al. 2012; Andrić et al. 2019). The associated impacts on energy consumption are expected to be higher in hot summer and warm winter climates, where cooling needs are more relevant (Li et al. 2012; Wan et al. 2012; Andrić et al. 2019). If not met, this higher demand for thermal comfort can impact health, sleep quality and work productivity, having disproportionate effects on vulnerable populations and exacerbating energy poverty (Biardeau et al. 2020; Sun et al. 2020; Falchetta and Mistry 2021) (Section 9.8).

exacerbatingresources/ipcc/cleaned_content/wg3/Chapter15/html_with_ids.html#FAQ 15.3 | What defines a financing gap, and where are the critically identified gaps?_p2

Gaps are in particular concerning for many developing countries, with COVID-19 exacerbating the macroeconomic outlook and fiscal space for governments. Also, limited institutional capacity represents a key barrier for many developing countries, burdening risk perceptions and access to appropriately priced financing as well as limiting their ability to actively manage the transformation. Existing fundamental inequities in access to finance, as well as its terms and conditions, and countries’ exposure to physical impacts of climate change, overall result in a worsening outlook for a global just transition.

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Scenarios that limit warming to 2°C (>67%) or lower by 2100 commonly involve extensive mitigation in the agriculture, forestry and other land use (AFOLU) sector that at the same time provides biomass for mitigation in other sectors. Bioenergy is the most land intensive renewable energy option, but the total land occupation of other renewable energy options can become significant in high deployment scenarios (robust evidence, high agreement). Growing demands for food, feed, biomaterials, and non-fossil fuels increase the competition for land and biomass while climate change creates additional stresses on land, exacerbating existing risks to livelihoods, biodiversity, human and ecosystem health, infrastructure, and food systems. Appropriate integration of bioenergy and other bio-based systems, and of other mitigation options, with existing land and biomass uses can improve resource use efficiency, mitigate pressures on natural ecosystems and support adaptation through measures to combat land degradation, enhance food security, and improve resilience through maintenance of the productivity of the land resource base (medium evidence, high agreement ). {3.2.5, 3.4.6, 12.5}

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Since AR5, rising public awareness and an increasing diversity of actors, have overall helped accelerate political commitment and global efforts to address climate change (medium confidence). Mass social movements have emerged as catalysing agents in some regions, often building on prior movements including Indigenous Peoples-led movements, youth movements, human rights movements, gender activism, and climate litigation, which is raising awareness and, in some cases, has influenced the outcome and ambition of climate governance. (medium confidence). Engaging Indigenous Peoples and local communities using just-transition and rights-based decision-making approaches, implemented through collective and participatory decision-making processes has enabled deeper ambition and accelerated action in different ways, and at all scales, depending on national circumstances (medium confidence). The media helps shape the public discourse about climate change. This can usefully build public support to accelerate climate action (medium evidence, high agreement ). In some instances, public discourses of media and organised counter movements have impeded climate action, exacerbating helplessness and disinformation and fuelling polarisation, with negative implications for climate action (medium confidence). {WGII SPM C.5.1, WGII SPM D.2, WGII TS.D.9, WGII TS.D.9.7, WGII TS.E.2.1, WGII 18.4; WGIII SPM D.3.3, WGIII SPM E.3.3, WGIII TS.6.1, WGIII 6.7, WGIII 13 ES, WGIII Box.13.7}

exacerbatingresources/ipcc/syr/longer-report/html_with_ids.html#3.2_p5

Maladaptive responses to climate change can create lock-ins of vulnerability, exposure and risks that are difficult and expensive to change and exacerbate existing inequalities. Actions that focus on sectors and risks in isolation and on short-term gains often lead to maladaptation. Adaptation options can become maladaptive due to their environmental impacts that constrain ecosystem services and decrease biodiversity and ecosystem resilience to climate change or by causing adverse outcomes for different groups, exacerbating inequity. Maladaptation can be avoided by flexible, multi-sectoral, inclusive and long-term planning and implementation of adaptation actions with benefits to many sectors and systems. (high confidence). {WGII SPM C.4, WGII SPM.C.4.1, WGII SPM C.4.2, WGII SPM C.4.3}

exacerbatingresources/ipcc/wg3/Chapter03/html_with_ids.html#3.8.4_p1

There is strong agreement that the climate policy institutional framework as well as technological progress have a profound impact on the attainability of low-carbon pathways. Delaying international cooperation reduces the available carbon budget and locks into carbon-intensive infrastructure exacerbating implementation challenges (Keppo and Rao 2007; Bosetti et al. 2009; Boucher et al. 2009; Clarke et al. 2009; Krey and Riahi 2009; van Vliet et al. 2009; Knopf et al. 2011; Jakob et al. 2012; Luderer et al. 2013; Rogelj et al. 2013a; Aboumahboub et al. 2014; Kriegler et al. 2014a; Popp et al. 2014; Riahi et al. 2015; Gambhir et al. 2017; Bertram et al. 2021). Similarly, technological availability influences the feasibility of climate stabilisation, though differently for different technologies (Kriegler et al. 2014a; Iyer et al. 2015a; Riahi et al. 2015).

meridional overturning circulationresources/ipcc/cleaned_content/wg1/Chapter05/html_with_ids.html#5.1_p7

(Section 5.3 builds on the Special Report on the Ocean and Cryosphere (SROCC) covering the change in ocean acidification due to oceanic CO2 uptake across the paleo, historical periods and future projections using Coupled Model Intercomparison Project Phase 6 (CMIP6), with consequences for marine life (assessed in the Sixth Assessment Report Working Group II, AR6 WGII) and biogeochemical cycles. The section also assesses changes in deoxygenation of the oceans due to warming, increased stratification of the surface ocean, and slowing of the meridional overturning circulation.

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Vegetation regrowth and increased precipitation in wetland regions associated with the mid-deglacial Northern Hemisphere warming (referred to as the Bølling/Allerød (B/A) warm interval, 14.7–12.7 ka), in particular in the (sub)tropics, accounts for large increases in both CH4 and N2O emissions to the atmosphere (Baumgartner et al., 2014; Schilt et al., 2014; Bock et al., 2017; H. Fischer et al., 2019). Specifically, changes in CH4 sources were steered by variations in vegetation productivity, source size area, temperatures and precipitation as modulated by insolation, local sea level changes and monsoon intensity (Bock et al., 2017; Kleinen et al., 2020). Changes in the CH4 atmospheric sink term probably only played a secondary role in modulating atmospheric CH4 inventories across the LDT (Hopcroft et al., 2017; Kleinen et al., 2020) Geological emissions, related to the destabilization of fossil (radiocarbon-dead) CH4 sources buried in continental margins as a result of sudden warming, appear small (Bock et al., 2017; Petrenko et al., 2017; Dyonisius et al., 2020). Stable isotope analysis on N2O extracted from Antarctic and Greenland ice reveal that marine and terrestrial emissions increased by 0.7 ± 0.3 and 1.7 ± 0.3 TgN, respectively, across the LDT (Fischer et al., 2019). During abrupt Northern Hemisphere warmings, terrestrial emissions responded rapidly to the northward displacement of the Intertropical Convergence Zone (ITCZ) associated with the resumption of the Atlantic meridional overturning circulation (AMOC; H. Fischer et al., 2019). About 90% of these step increases occurred rapidly, possibly in less than 200 years (Fischer et al., 2019). In contrast, marine emissions increased more gradually, modulated by global ocean circulation reorganization.

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During the LDT, deep ocean ventilation increased as Antarctic Bottom Water (AABW) (Skinner et al., 2010; Gottschalk et al., 2016; Jaccard et al., 2016) and subsequently the Atlantic meridional overturning circulation (McManus et al., 2004; Lippold et al., 2016) resumed, transferring previously sequestered remineralized carbon from the ocean interior to the upper ocean, and eventually the atmosphere (Skinner et al., 2010; Galbraith and Jaccard, 2015; Gottschalk et al., 2016; Ronge et al., 2016, 2020; Sikes et al., 2016; Rae et al., 2018), contributing to the deglacial CO2 rise. Intermediate depths lost oxygen as a result of sluggish ventilation and increasing temperatures (decreasing saturation). As the world emerged from the last Glacial period, OMZs underwent a large volumetric increase at the beginning of the Bølling-Allerød (B/A), a northern-hemisphere wide warming event, 14.7 ka (Jaccard and Galbraith, 2012; Praetorius et al., 2015) with deleterious consequences for benthic ecosystems (e.g., Moffitt et al., 2015). These observations indicate with high confidence that the rate of warming, affecting the solubility of oxygen and upper water column stratification, coupled with changes in subsurface ocean ventilation, impose a direct control on the degree of ocean deoxygenation, implying a high sensitivity of ocean oxygen loss to warming. The expansion of OMZs contributed to a widespread increase in water column (de)nitrification (Galbraith and Kienast, 2013), which contributed substantially to enhanced marine N2O emissions. Nitrogen stable isotope measurements on N2O extracted from ice cores suggest that approximately one-third (of the order of 0.7 ± 0.3TgN yr–1) of thedeglacial increase in N2O emissions relates to oceanic sources (Schilt et al., 2014; H. Fischer et al., 2019).

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Large-scale regional and global ocean circulation shape the spatial pattern of the uptake and storage of both CO2 and heat (see Figure 5.8 for carbon; Figure 9.6 for heat observations; Section 9.2; Frölicher et al., 2015; Bronselaer and Zanna, 2020). This coherence of spatial patterns driven by the large-scale ocean circulation has three aspects. First, notwithstanding interannual-decadal variability in heat and CO2 uptake, there is a spatial coherence of the temporally integrated uptake at the air–sea boundary, particularly in the Southern Ocean (Cross-Chapter Box 5.3, Figure 1; Talley et al., 2016; Keppler and Landschützer, 2019; Auger et al., 2021). Second, the importance of the meridional overturning circulation in the subsequent storage of both heat and CO2 in mode, intermediate and deep waters of the ocean interior (Section 9.2). Third, of particular note, the roles of the North Atlantic Ocean (Section 9.2.3.1) and the Southern Ocean (Section 9.2.3.2) in linking the spatial pattern of air–sea fluxes, the storage of heat and carbon, and ultimately in understanding and predicting the sensitivity of the carbon-heat nexus to climate change (Frölicher et al., 2015; Thomas et al., 2018; Wu et al., 2019).

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Burls, N.J. et al., 2017: Active Pacific meridional overturning circulation (PMOC) during the warm Pliocene. Science Advances, 3(9), e1700156, doi: 10.1126/sciadv.1700156.

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Lippold, J. et al., 2016: Deep water provenance and dynamics of the (de)glacial Atlantic meridional overturning circulation. Earth and Planetary Science Letters, 445, 68–78, doi: 10.1016/j.epsl.2016.04.013.

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McManus, J.F., R. Francois, J.-M. Gherardi, L.D. Keigwin, and S. Brown-Leger, 2004: Collapse and rapid resumption of Atlantic meridional circulation linked to deglacial climate changes. Nature, 428(6985), 834–837, doi: 10.1038/nature02494.

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Southern Ocean circulation changes are assessed in SROCC (Meredith et al., 2019), and are confirmed and synthesized in Section 9.2.3.2 which shows that there is no indication of ACC transport change, and that it is unlikely that the mean meridional position of the ACC has moved southward in recent decades.

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The AR5 reported no changes in the Atlantic Meridional Mode (AMM) during the 20th century or shorter periods thereof. For the Atlantic Zonal Mode (AZM), also referred as the Atlantic Niño, the AR5 reported increases during the 1950–2012 period but neither assessed trends nor provided a confidence statement. The AR5 did not assess paleo evidence for the AZM and AMM.

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Baringer, M.O. et al., 2018: Meridional overturning and oceanic heat transport circulation observations in the North Atlantic Ocean [in “State of the Climate in 2017”]. Bulletin of the American Meteorological Society, 99(8), S91–S94, doi: 10.1175/2018bamsstateoftheclimate.1.

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Caesar, L., G.D. McCarthy, D.J.R. Thornalley, N. Cahill, and S. Rahmstorf, 2021: Current Atlantic Meridional Overturning Circulation weakest in last millennium. Nature Geoscience, 14(3), 118–120, doi: 10.1038/s41561-021-00699-z.

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Foltz, G.R., M.J. McPhaden, and R. Lumpkin, 2012: A Strong Atlantic Meridional Mode Event in 2009: The Role of Mixed Layer Dynamics. Journal of Climate, 25(1), 363–380, doi: 10.1175/jcli-d-11-00150.1.

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Fu, Y., F. Li, J. Karstensen, and C. Wang, 2020: A stable Atlantic Meridional Overturning Circulation in a changing North Atlantic Ocean since the 1990s. Science Advances, 6(48), eabc7836, doi: 10.1126/sciadv.abc7836.

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Hassanzadeh, P., Z. Kuang, and B.F. Farrell, 2014: Responses of midlatitude blocks and wave amplitude to changes in the meridional temperature gradient in an idealized dry GCM. Geophysical Research Letters, 41(14), 5223–5232, doi: 10.1002/2014gl060764.

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Lippold, J. et al., 2019: Constraining the Variability of the Atlantic Meridional Overturning Circulation During the Holocene. Geophysical Research Letters, 46(20), 11338–11346, doi: 10.1029/2019gl084988.

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Little, C.M. et al., 2019: The Relationship Between U.S. East Coast Sea Level and the Atlantic Meridional Overturning Circulation: A Review. Journal of Geophysical Research: Oceans, 124(9), 6435–6458, doi: 10.1029/2019jc015152.

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Lynch-Stieglitz, J., 2017: The Atlantic Meridional Overturning Circulation and Abrupt Climate Change. Annual Review of Marine Science, 9, 83–104, doi: 10.1146/annurev-marine-010816-060415.

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Lynch-Stieglitz, J. et al., 2007: Atlantic Meridional Overturning Circulation During the Last Glacial Maximum. Science, 316(5821), 66, doi: 10.1126/science.1137127.

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McManus, J.F., R. Francois, J.-M. Gherardi, L.D. Keigwin, and S. Brown-Leger, 2004: Collapse and rapid resumption of Atlantic meridional circulation linked to deglacial climate changes. Nature, 428(6985), 834–837, doi: 10.1038/nature02494.

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Meinen, C.S. et al., 2018: Meridional Overturning Circulation Transport Variability at 34.5°S During 2009–2017: Baroclinic and Barotropic Flows and the Dueling Influence of the Boundaries. Geophysical Research Letters, 45(9), 4180–4188, doi: 10.1029/2018gl077408.

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Mercier, H. et al., 2015: Variability of the meridional overturning circulation at the Greenland–Portugal OVIDE section from 1993 to 2010. Progress in Oceanography, 132, 250–261, doi: 10.1016/j.pocean.2013.11.001.

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Moat, B.I. et al., 2020: Pending recovery in the strength of the meridional overturning circulation at 26°N. Ocean Science, 16(4), 863–874, doi: 10.5194/os-16-863-2020.

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Perez, F.F. et al., 2018: Meridional overturning circulation conveys fast acidification to the deep Atlantic Ocean. Nature, 554(7693), 515–518, doi: 10.1038/nature25493.

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The simulation of annual mean rainfall patterns in the CMIP6 models reveals minor improvements compared to those in CMIP5 models (Figure 3.13). The persistent biases include the double ITCZ in the tropical Pacific (seen as bands of excessive rainfall on both sides of the equatorial Pacific in Figure 3.13b,d) and the southward-shifted ITCZ in the equatorial Atlantic, which have been linked to the meridional pattern of SST bias (S. Zhou et al., 2020) and the reduced sensitivity of precipitation to local SST (Good et al., 2021). Tian and Dong (2020) also found that all three generations of CMIP models share similar systematic annual mean precipitation errors in the tropics, but that the double ITCZ bias is slightly reduced in CMIP6 models in comparison to CMIP3 and CMIP5 models. They also found some improvement in the overly intense Indian ocean ITCZ and the too dry South American continent except over the Andes. Fiedler et al. (2020) identified improvements in the tropical mean spatial correlations and root mean square error of the climatology as well as in the day-to-day variability, but found little change across CMIP phases in the double ITCZ bias and diurnal cycle. The CMIP6 models reproduce better the domain and intensity of the global monsoon (see Section 3.3.3.2). Moreover, CMIP6 models better represent the storm tracks (Priestley et al., 2020; also (Section 3.3.3.3), thereby reducing the precipitation biases in the North Atlantic and mid-latitudes of the Southern Hemisphere (Figure 3.13b,d). As a result, pattern correlations between simulated and observed annual mean precipitation range between 0.80 and 0.92 for CMIP6 models, compared to a range of 0.79 to 0.88 for CMIP5 (Bock et al., 2020). This relative improvement may be related to increased model resolution, as found when comparing biases in the mean of the HighResMIP models with the mean of the corresponding lower-resolution versions of the same models (see Figure 3.13e,f), particularly in the tropics and extratropical storm tracks. Consistent with this, a recent study using several coupled models showed that increasing the atmospheric resolution leads to a strong decrease in the precipitation bias in the tropical Atlantic ITCZ (see further discussion in Section 3.8.2.2; Vannière et al., 2019). Based on these results we assess that despite some improvements, CMIP6 models still have deficiencies in simulating precipitation patterns, particularly over the tropical ocean (high confidence).

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Paleoclimate proxy evidence shows that the global monsoon has varied with orbital forcing and greenhouse gases (Section 2.3.1.4.2; Mohtadi et al., 2016; Seth et al., 2019). These large-magnitude intensifications and weakenings in the global monsoon involved in some cases orders-of-magnitude changes in precipitation locally (Harrison et al., 2014; Tierney et al., 2017). Paleoclimate modelling and limited data from past climate states with high CO2 suggest that precipitation intensifies in the monsoon domain under elevated greenhouse gases, providing context for present and future trends (Passey et al., 2009; Haywood et al., 2013; Zhang et al., 2013b). In model simulations of the mid-Pliocene, when globally averaged temperature was higher than present day, precipitation was larger in West African, South Asian and East Asian monsoons than under pre-industrial conditions, consistent with proxy evidence (Zhang et al., 2015; Sun et al., 2016, 2018; Corvec and Fletcher, 2017; X. Li et al., 2018). Prescott et al. (2019) and R. Zhang et al. (2019) find an important role for orbital forcing and CO2 in the mid-Pliocene monsoon expansion and intensification. Models are also able to capture interhemispherically contrasting monsoon changes in the Last Interglacial in response to orbital forcing and greenhouse gases, with wetter West African and Asian monsoons and a drier South American monsoon as seen in proxies (Govin et al., 2014; Gierz et al., 2017; Pedersen et al., 2017). In overall agreement with proxy evidence, a model with transient forcing simulates wetting and drying respectively of the Southern and Northern Hemisphere monsoons during the last deglaciation, with an important contribution from Atlantic Meridional Overturning Circulation (AMOC) slowdown (Otto-Bliesner et al., 2014; Mohtadi et al., 2016).

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The Atlantic Meridional Overturning Circulation (AMOC) represents a large-scale flow of warm salty water northward at the surface and a return flow of colder water southward at depth. As such, its mean state plays an important role in transporting heat in the climate system, while its variability can act to redistribute heat (see Sections 2.3.3.4.1 and 9.2.3.1 for more details). Paleo-climatic and model evidence suggest that changes in AMOC strength have played a prominent role in past transitions between warm and cool climatic phases (e.g., Dansgaard et al., 1993; Ritz et al., 2013).

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The assessment of observations presented in Sections 2.3.3.4.2 and 9.2.3.2 reports that there is no evidence of an ACC transport change, and it is unlikely that the mean meridional position of the ACC has moved southward in recent decades (Sections 2.3.3.4.2 and 9.2.3.2). This is despite observations of surface wind displaying an intensification and southward shift (Section 2.4.1.2). There is low confidence in an observed intensification of upper ocean overturning in the Southern Ocean and there is medium confidence for a slowdown of the Antarctic Bottom Water circulation and commensurate Antarctic Bottom Water volume decrease since the 1990s (Section 9.2.3.2). Section 9.2.3.2 presents new evidence, since SROCC, which assessed with medium confidence that the lower cell can episodically increase as a response to climatic anomalies, temporally counteracting the forced tendency for reduced bottom water formation.

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The Southern Annular Mode (SAM) consists of a meridional redistribution of atmospheric mass around Antarctica (Figure 3.33c,f), associated with a meridional shift of the jet and surface westerlies over the Southern Ocean. SAM indices are variously defined as the difference in zonal-mean sea level pressure or geopotential height between middle and high latitudes or via a principal-component analysis (Annex IV.2.2). Observational aspects of the SAM are assessed in Section 2.4.1.2.

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Most CMIP5 and CMIP6 models are found to represent the general structure of observed SST anomalies during ENSO events well (Kim and Yu, 2012; Taschetto et al., 2014; Brown et al., 2020; Grose et al., 2020). However, the majority of CMIP5 models display SST anomalies that: i) extend too far to the west (Taschetto et al., 2014; Capotondi et al., 2015); and ii) have meridional widths that are too narrow (Zhang and Jin, 2012) compared to the observations. CMIP6 models display a statistically significant improvement in the longitudinal representation of ENSO SST anomalies relative to CMIP5 models (Planton et al., 2021), however, systematic biases in the zonal extent and meridional width remain in CMIP6 models (Fasullo et al., 2020; Planton et al., 2021). The ENSO phase asymmetry, where observed strong El Niño events are larger and have a shorter duration than strong La Niña events (Ohba and Ueda, 2009; Frauen and Dommenget, 2010), is also under-represented in both CMIP5 and CMIP6 models (Zhang and Sun, 2014; Planton et al., 2021). In this instance, both CMIP5 and CMIP6 models typically display El Niño events that have a longer duration than those observed, La Niña events that have a similar duration to those observed, and there is very little asymmetry in the duration of El Niño and La Niña phases (Figure 3.36). Roberts et al. (2018) find an improvement in amplitude asymmetry in a HighResMIP model, but the under-representation remains.

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The Atlantic Zonal Mode (AZM), often referred to as the Atlantic Equatorial Mode or Atlantic Niño, and the Atlantic Meridional Mode (AMM) are the two leading basin-wide patterns of interannual to decadal variability in the tropical Atlantic. Akin to ENSO in the Pacific, the term Atlantic Niño is broadly used to refer to years when the SSTs in the tropical eastern Atlantic basin along the cold tongue are significantly warmer than the climatological average. The AMM is characterized by anomalous cross-equatorial gradients in SST. Both modes are associated with altered strength of the Inter-tropical Convergence Zone (ITCZ) and/or latitudinal shifts in the ITCZ, which locally affect African and American monsoon systems and remotely affect tropical Pacific and Indian Ocean variability through inter-basins teleconnections. A detailed description of both AZM and AMM, as well as their associated teleconnection over land, is given in Annex IV.2.5

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Amaya, D.J., M.J. DeFlorio, A.J. Miller, and S.-P. Xie, 2017: WES feedback and the Atlantic Meridional Mode: observations and CMIP5 comparisons. Climate Dynamics, 49(5), 1665–1679, doi: 10.1007/s00382-016-3411-1.

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Buckley, M.W. and J. Marshall, 2016: Observations, inferences, and mechanisms of the Atlantic Meridional Overturning Circulation: A review. Reviews of Geophysics, 54(1), 5–63, doi: 10.1002/2015rg000493.

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Cheng, W., J.C.H. Chiang, and D. Zhang, 2013: Atlantic meridional overturning circulation (AMOC) in CMIP5 Models: RCP and historical simulations. Journal of Climate, 26(18), 7187–7197, doi: 10.1175/jcli-d-12-00496.1.

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Gent, P.R., 2016: Effects of Southern Hemisphere Wind Changes on the Meridional Overturning Circulation in Ocean Models. Annual Review of Marine Science, 8(1), 79–94, doi: 10.1146/annurev-marine-122414-033929.

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Liu, W., S.-P. Xie, Z. Liu, and J. Zhu, 2017: Overlooked possibility of a collapsed Atlantic Meridional Overturning Circulation in warming climate. Science Advances, 3(1), e1601666, doi: 10.1126/sciadv.1601666.

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Menary, M.B. and A.A. Scaife, 2014: Naturally forced multidecadal variability of the Atlantic meridional overturning circulation. Climate Dynamics, 42(5), 1347–1362, doi: 10.1007/s00382-013-2028-x.

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Perez, F.F. et al., 2018: Meridional overturning circulation conveys fast acidification to the deep Atlantic Ocean. Nature, 554(7693), 515–518, doi: 10.1038/nature25493.

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Reintges, A., T. Martin, M. Latif, and N.S. Keenlyside, 2017: Uncertainty in twenty-first century projections of the Atlantic Meridional Overturning Circulation in CMIP3 and CMIP5 models. Climate Dynamics, 49(5), 1495–1511, doi: 10.1007/s00382-016-3180-x.

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Roberts, C.D., L. Jackson, and D. McNeall, 2014: Is the 2004–2012 reduction of the Atlantic meridional overturning circulation significant?Geophysical Research Letters, 41(9), 3204–3210, doi: 10.1002/2014gl059473.

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Roberts, M.J. et al., 2020c: Sensitivity of the Atlantic Meridional Overturning Circulation to Model Resolution in CMIP6 HighResMIP Simulations and Implications for Future Changes. Journal of Advances in Modeling Earth Systems, 12(8), e2019MS002014, doi: 10.1029/2019ms002014.

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Schmith, T., S. Yang, E. Gleeson, and T. Semmler, 2014: How Much Have Variations in the Meridional Overturning Circulation Contributed to Sea Surface Temperature Trends since 1850? A Study with the EC-Earth Global Climate Model. Journal of Climate, 27(16), 6343–6357, doi: 10.1175/jcli-d-13-00651.1.

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Sherriff-Tadano, S., A. Abe-Ouchi, M. Yoshimori, A. Oka, and W.-L. Chan, 2018: Influence of glacial ice sheets on the Atlantic meridional overturning circulation through surface wind change. Climate Dynamics, 50(7), 2881–2903, doi: 10.1007/s00382-017-3780-0.

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Smeed, D.A. et al., 2014: Observed decline of the Atlantic meridional overturning circulation 2004–2012. Ocean Science, 10(1), 29–38, doi: 10.5194/os-10-29-2014.

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Trouet, V., J.D. Scourse, and C.C. Raible, 2012: North Atlantic storminess and Atlantic Meridional Overturning Circulation during the last Millennium: Reconciling contradictory proxy records of NAO variability. Global and Planetary Change, 84–85, 48–55, doi: 10.1016/j.gloplacha.2011.10.003.

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Weijer, W., W. Cheng, O.A. Garuba, A. Hu, and B.T. Nadiga, 2020: CMIP6 Models Predict Significant 21st Century Decline of the Atlantic Meridional Overturning Circulation. Geophysical Research Letters, 47(12), e2019GL086075, doi: 10.1029/2019gl086075.

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Wouters, B., S. Drijfhout, and W. Hazeleger, 2012: Interdecadal North-Atlantic meridional overturning circulation variability in EC-EARTH. Climate Dynamics, 39(11), 2695–2712, doi: 10.1007/s00382-012-1366-4.

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Zhang, L. and C. Wang, 2013: Multidecadal North Atlantic sea surface temperature and Atlantic meridional overturning circulation variability in CMIP5 historical simulations. Journal of Geophysical Research: Oceans, 118(10), 5772–5791, doi: 10.1002/jgrc.20390.

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Zhang, W. and F.F. Jin, 2012: Improvements in the CMIP5 simulations of ENSO-SSTA meridional width. Geophysical Research Letters, 39(23), 1–5, doi: 10.1029/2012gl053588.

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Zhou, S., G. Huang, and P. Huang, 2020: Excessive ITCZ but Negative SST Biases in the Tropical Pacific Simulated by CMIP5/6 Models: The Role of the Meridional Pattern of SST Bias. Journal of Climate, 33(12), 5305–5316, doi: 10.1175/jcli-d-19-0922.1.

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The chapter is organized as follows (Figure 4.1). After (Section 4.2 on the methodologies used in the assessment, Section 4.3 assesses projected changes in key global climate indicators throughout the 21st century, relative to the period 1995–2014, which comprises the last 20 years of the historical simulations of CMIP6 (Eyring et al., 2016) and hence the most recent past simulated with the observed atmospheric composition. The global climate indicators assessed include GSAT, global land precipitation, Arctic sea ice area (SIA), global mean sea level (GMSL), the Atlantic Meridional Overturning Circulation (AMOC), global mean ocean surface pH, carbon uptake by land and ocean, the global monsoon, the Northern and Southern Annular Modes (NAM and SAM), and the El Niño–Southern Oscillation (ENSO). Differently from the assessment for changes in other quantities only based on the range of CMIP6 projections, additional lines of evidence enter the assessment for GSAT and GMSL change. For most results and figures based on CMIP6, one realization from each model (the first of the uploaded set) is used. Section 4.3 finally synthesizes the assessment of GSAT change using multiple lines of evidence in addition to the CMIP6 projection simulations.

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The AR5 assessed from CMIP5 simulations that the Atlantic Meridional Overturning Circulation (AMOC) will very likely weaken over the 21st century, and the projected weakening of the AMOC is consistent with CMIP5 projections of an increase of high-latitude temperature and high-latitude precipitation, with both effects causing the surface waters at high latitudes to become less dense and therefore more stable (Collins et al., 2013).

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Interannual variability of the tropical Atlantic can be described in terms of two main climate modes: the Atlantic equatorial mode and the Atlantic meridional mode (AMM; Annex IV, Section AIV2.5). The Atlantic equatorial mode, also commonly referred to as the Atlantic Niño or Atlantic Zonal Mode, is associated with SST anomalies near the equator, peaking in the eastern basin, while the AMM is characterized by an inter-hemispheric gradient of SST and wind anomalies. Both modes are associated with changes in the ITCZ and related winds and exert a strong influence on the climate in adjacent and remote regions.

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In contrast to the Northern Hemisphere, the Southern Hemisphere shows an increase in the frequency of intense ETCs in CMIP5 models (Chang, 2017), and there is high confidence that wind speeds associated with ETCs are expected to intensify in the SH storm track for high emissions scenarios. These changes in intensity are accompanied by an overall southward shift of the SH winter storm track (Figure 4.27b) due to the poleward shift in the upper-level jet and the increase in the meridional SST gradient linked to the slower warming of the Southern Ocean (Grieger et al., 2014).

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The AR5 assessed that there is low confidence in projected changes of the Tropical Atlantic Variability (TAV) because of the general failure of climate models to simulate main aspects of this variability such as the northward displaced ITCZ. The models that best represent the Atlantic meridional mode (AMM) show a weakening for future climate conditions. However, model biases in representation of Altantic Niños strongly limit an assessment of future changes.

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In addition to uncertainty from the future evolution of the mechanisms that determined the PDV, it is also unclear how the background state in the Pacific Ocean will change due to time-varying radiative forcing, and how this change will interact with variability at interannual and low-frequency time scales (Fedorov et al., 2020). Recent research suggests that the PDV will have a weaker amplitude and higher frequency with global warming (Zhang and Delworth, 2016; Xu and Hu, 2017; Geng et al., 2019). The former appears to be associated with a decrease in SST variability and the meridional gradient over the Kuroshio-Oyashio region, with a reduction in North Pacific wind stress and meandering of the subpolar/subtropical gyre interplay (Zhang and Delworth, 2016). The latter is hypothesized to rely on the enhanced ocean stratification and shallower mixed layers of a warmer climate, which would increase the phase speed of the westward-propagating oceanic waves, hence shortening the decadal to inter-decadal component (Goodman and Marshall, 1999; Zhang and Delworth, 2016; Xu and Hu, 2017). The weakening of the PDV in a warmer climate may reduce the internal variability of global mean surface temperature, to which PDV seems associated (Zhang et al., 1997; Kosaka and Xie, 2016; Geng et al., 2019). Thus, a weaker and higher frequency PDV could reduce the contribution of internal variability to the GSAT trend and eventually lead to a reduced probability of surface-warming hiatus events.

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At a fixed time horizon, the CMIP5 multi-model spread in GSAT explains only a small fraction of the spread in the shift of the Northern Hemisphere mid-latitude circulation due to an abrupt quadrupling in CO2 (Grise and Polvani, 2016). The fraction of model spread explained by GSAT in the shift of the Southern Hemisphere circulation is larger, but still fairly small (Grise and Polvani, 2014a, 2016). At a fixed time horizon and for a given emissions scenario, CMIP5 multi-model spread in storm track shifts, and the closely related mid-latitude jets, can be better explained by multi-model spread in lower and upper level meridional temperature gradients than by GSAT (Harvey et al., 2014; Grise and Polvani, 2016).

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As assessed in AR5 (Boucher et al., 2013), abruptly introducing SRM to fully offset global warming reduces temperature toward 1850–1900values with an e-folding time of only about five years (Matthews and Caldeira, 2007). A more realistic approach would be a slow ramp-up of SRM to offset further warming (MacCracken, 2016; Tilmes et al., 2016). Modelling studies have consistently shown that SRM has the potential to offset some effects of increasing GHGs on global and regional climate, including the melting of Arctic sea ice (Berdahl et al., 2014; Moore et al., 2014) and mountain glaciers (Zhao et al., 2017), weakening of Atlantic meridional overturning circulation (AMOC; Cao et al., 2016; Hong et al., 2017; Tilmes et al., 2020), changes in extremes of temperature and precipitation (Curry et al., 2014; Ji et al., 2018; Muthyala et al., 2018), and changes in frequency and intensity of tropical cyclone (Moore et al., 2015; Jones et al., 2017).

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New since AR5 is the fundamental recognition in SRCCL and in this Report (Chapter 5) that projected changes in forests strongly depend on the human disturbance and that tropical forest dieback in the absence of disturbance is largely driven by the increased potential for drought, while that in boreal forests includes both thermal and hydrological factors (Drijfhout et al., 2015). For some proposed tipping elements, the role of seasonal change has become better understood. For example, the lack of a tipping point in the reduction of summer Arctic sea ice area (Stroeve and Notz, 2015) has been further substantiated. The role of abrupt change at the edges (Bathiany et al., 2020) has also been clarified, as has been the importance of distinguishing summer from winter mechanisms and associated abruptness, because ice area reduces gradually in summer, but not necessarily in winter (Bathiany et al., 2016). For other tipping elements including AMOC (Section 9.2.3.1), mixed layer depth (Section 9.2.1.3), and sea level rise (), an increase in the diversity of model structure and sensitivity to multiple factors has led to a better understanding of the complexity of the problem, with some increase in assessed uncertainty and an assessed deep uncertainty (Glossary) related to projected sea level rise with global warming levels above 3°C (Section 9.6.3.5). In still other cases such as Antarctic sea ice (Section 9.3.2) and Southern Ocean Meridional Overturning Circulation (MOC; Section 9.2.3.1), uncertainty remains high. Finally, it has also been postulated that models may be prone to being too stable (Valdes, 2011) based on the limitations of models as well as other lines of evidence such paleo-evidence of abrupt events (Dakos et al., 2008; Klus et al., 2018; Sime et al., 2019).

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Armour, K.C., N. Siler, A. Donohoe, and G.H. Roe, 2019: Meridional Atmospheric Heat Transport Constrained by Energetics and Mediated by Large-Scale Diffusion. Journal of Climate, 32(12), 3655–3680, doi: 10.1175/jcli-d-18-0563.1.

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Bonan, D.B., K.C. Armour, G.H. Roe, N. Siler, and N. Feldl, 2018: Sources of Uncertainty in the Meridional Pattern of Climate Change. Geophysical Research Letters, 45, 9131–9140, doi: 10.1029/2018gl079429.

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Dai, Z., D.K. Weisenstein, and D.W. Keith, 2018: Tailoring Meridional and Seasonal Radiative Forcing by Sulfate Aerosol Solar Geoengineering. Geophysical Research Letters, 45(2), 1030–1039, doi: 10.1002/2017gl076472.

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Hong, Y. et al., 2017: Impact of the GeoMIP G1 sunshade geoengineering experiment on the Atlantic meridional overturning circulation. Environmental Research Letters, 12(3), 034009, doi: 10.1088/1748-9326/aa5fb8.

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Jackson, L.C., N. Schaller, R.S. Smith, M.D. Palmer, and M. Vellinga, 2014: Response of the Atlantic meridional overturning circulation to a reversal of greenhouse gas increases. Climate dynamics, 42(11–12), 3323–3336.

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Karspeck, A.R. et al., 2017: Comparison of the Atlantic meridional overturning circulation between 1960 and 2007 in six ocean reanalysis products. Climate Dynamics, 49(3), 957–982, doi: 10.1007/s00382-015-2787-7.

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Liguori, G. and E. Di Lorenzo, 2019: Separating the North and South Pacific Meridional Modes Contributions to ENSO and Tropical Decadal Variability. Geophysical Research Letters, 46(2), 906–915, doi: 10.1029/2018gl080320.

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Liu, W., S.-P. Xie, Z. Liu, and J. Zhu, 2017: Overlooked possibility of a collapsed Atlantic Meridional Overturning Circulation in warming climate. Science Advances, 3(1), e1601666, doi: 10.1126/sciadv.1601666.

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Liu, W., J. Lu, S.-P. Xie, and A. Fedorov, 2018: Southern Ocean Heat Uptake, Redistribution, and Storage in a Warming Climate: The Role of Meridional Overturning Circulation. Journal of Climate, 31(12), 4727–4743, doi: 10.1175/jcli-d-17-0761.1.

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Rind, D. et al., 2018: Multicentury Instability of the Atlantic Meridional Circulation in Rapid Warming Simulations With GISS ModelE2. Journal of Geophysical Research: Atmospheres, 123(12), 6331–6355, doi: 10.1029/2017jd027149.

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Stolpe, M.B., I. Medhaug, J. Sedláček, and R. Knutti, 2018: Multidecadal Variability in Global Surface Temperatures Related to the Atlantic Meridional Overturning Circulation. Journal of Climate, 31(7), 2889–2906, doi: 10.1175/jcli-d-17-0444.1.

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Weijer, W., W. Cheng, O.A. Garuba, A. Hu, and B.T. Nadiga, 2020: CMIP6 Models Predict Significant 21st Century Decline of the Atlantic Meridional Overturning Circulation. Geophysical Research Letters, 47, e2019GL086075, doi: 10.1029/2019gl086075.

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Wen, Q., J. Yao, K. Döös, and H. Yang, 2018: Decoding Hosing and Heating Effects on Global Temperature and Meridional Circulations in a Warming Climate. Journal of Climate, 31(23), 9605–9623, doi: 10.1175/jcli-d-18-0297.1.

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Zhang, R. et al., 2019: A Review of the Role of the Atlantic Meridional Overturning Circulation in Atlantic Multidecadal Variability and Associated Climate Impacts. Reviews of Geophysics, 57(2), 316–375, doi: 10.1029/2019rg000644.

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It has been proposed that Arctic amplification, by reducing the equator–pole temperature contrast, could result in a weaker and more meandering jet with Rossby waves of larger amplitude (Francis et al., 2017; Zhang and Luo, 2020). This may cause weather systems to travel eastward more slowly and thus, all other things being equal, Arctic amplification could lead to more persistent weather patterns over the mid-latitudes (Francis and Vavrus, 2012). The persistent large meandering flow may increase the likelihood of connected patterns of temperature and precipitation climatic impact-drivers because they frequently occur when atmospheric circulation patterns are persistent, which tends to occur with a strong meridional wind component. Another possible consequence of Arctic warming is on the NAO/AO that shows a negative trend over the 1990s and early 2000s (Robson et al., 2016; Iles and Hegerl, 2017), and has been linked to the reduction of sea ice in the Barents and Kara seas, and the increase in Eurasian snow cover (Cohen et al., 2012; Nakamura et al., 2015; Yang et al., 2016). During negative NAO/AO the storm tracks shift equatorward and winters are predominantly more severe across northern Eurasia and the eastern United States, but relatively mild in the Arctic. This temperature pattern is sometimes referred to as the ‘warm Arctic–cold continents (WACC)’ pattern (Chen et al., 2018). However, L. Sun et al. (2016) noticed that the WACC is a manifestation of natural variability. Enhanced sea ice loss in the Barents-Kara Sea has also been related to a weakening of the stratospheric polar vortex (Kretschmer et al., 2020) and its increased variability (Kretschmer et al., 2016) that would induce a negative NAO/AO (Kim et al., 2014), the WACC pattern (Kim et al., 2014), and an increase in cold air outbreaks (CAO) in mid-latitudes (Kretschmer et al., 2018). Arctic warming might also increase Eurasian snow cover in autumn caused by the moister air that is advected into Eurasia from the Arctic with reduced sea ice cover (Cohen et al., 2014; Jaiser et al., 2016), although Peings (2019) suggests a possible influence of Ural blockings on both the autumn snow cover and the early winter polar stratosphere. The circulation changes over the Ural-Siberian region are also suggested to provide a link between Barents-Kara sea ice and the NAO (Santolaria-Otín et al., 2021).

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As in winter, Arctic summer warming may result in a weakening of the westerly jet and mid-latitude storm tracks, as suggested for the recent period of Arctic warming (Coumou et al., 2015; Petrie et al., 2015; Chang et al., 2016). Additional proposed consequences are a southward shift of the jet (Butler et al., 2010) and a double jet structure associated with an increase of the land–ocean thermal gradient at the coastal boundary (Coumou et al., 2018). It is hypothesized that weaker jets, diminished meridional temperature contrast, and reduced baroclinicity might induce a larger amplitude in stationary wave response to stationary forcings (Zappa et al., 2011; Petoukhov et al., 2013; Hoskins and Woollings, 2015; Coumou et al., 2018; Mann et al., 2018; R. Zhang et al., 2020), and also that a double jet structure would favour wave resonance (Kornhuber et al., 2017; Mann et al., 2017). Some studies suggest that this is corroborated by an observed increase of quasi-stationary waves (Di Capua and Coumou, 2016; Vavrus et al., 2017; Coumou et al., 2018).

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The above proposed hypotheses are based on concepts of geophysical fluid dynamics and surface coupling and can, in principle, help explain the existence of a link between the Arctic changes and the mid-latitudes with the potential to affect many impact sectors (Barnes and Screen, 2015). However, the validity of some dynamical underlying mechanisms, such as a reduced meridional temperature contrast inducing enhanced wave amplitude, has been questioned (Hassanzadeh et al., 2014; Hoskins and Woollings, 2015). On the contrary, the reduced meridional temperature contrast has been related to reduced meridional temperature advection and thereby reduced winter temperature variability (Collow et al., 2019).

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Although El Niño–Southern Oscillation (ENSO) influences climate in southern Africa, any relationship between ENSO and Cape Town’s rainfall is weak and inconsistent, showing the strongest impact in May to June (Philippon et al., 2012). ENSO, however, does influence large-scale processes and phenomena relevant to the drought, though the relationship between ENSO and the SAM is complex, with each ENSO event influencing the SAM differently in different seasons (Ding et al., 2012). Similarly, ENSO affects meridional circulation and thus the subtropical anticyclone as well as the polar and subtropical jets (Seager et al., 2019), but only modifying, not controlling, their role in Cape Town’s rainfall.

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Buckley, M.W. and J. Marshall, 2016: Observations, inferences, and mechanisms of the Atlantic Meridional Overturning Circulation: A review. Reviews of Geophysics, 54(1), 5–63, doi: 10.1002/2015rg000493.

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Haarsma, R.J., F.M. Selten, and S.S. Drijfhout, 2015: Decelerating Atlantic meridional overturning circulation main cause of future west European summer atmospheric circulation changes. Environmental Research Letters, 10(9), 094007, doi: 10.1088/1748-9326/10/9/094007.

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Hassanzadeh, P., Z. Kuang, and B.F. Farrell, 2014: Responses of midlatitude blocks and wave amplitude to changes in the meridional temperature gradient in an idealized dry GCM. Geophysical Research Letters, 41(14), 5223–5232, doi: 10.1002/2014gl060764.

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Liu, W., S.-P. Xie, Z. Liu, and J. Zhu, 2017: Overlooked possibility of a collapsed Atlantic Meridional Overturning Circulation in warming climate. Science Advances, 3(1), e1601666, doi: 10.1126/sciadv.1601666.

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Simpson, I.R., R. Seager, M. Ting, and T.A. Shaw, 2016: Causes of change in Northern Hemisphere winter meridional winds and regional hydroclimate. Nature Climate Change, 6(1), 65–70, doi: 10.1038/nclimate2783.

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Weijer, W. et al., 2019: Stability of the Atlantic Meridional Overturning Circulation: A Review and Synthesis. Journal of Geophysical Research: Oceans, 124(8), 5336–5375, doi: 10.1029/2019jc015083.

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At the global scale, and also at the regional scale to some extent, many of the changes in extremes are a direct consequence of enhanced radiative forcing, and the associated global warming and/or resultant increase in the water-holding capacity of the atmosphere, as well as changes in vertical stability and meridional temperature gradients that affect climate dynamics (see Box 11.1). Widespread observed and projected increases in the intensity and frequency of hot extremes, together with decreases in the intensity and frequency of cold extremes, are consistent with global and regional warming (Section 11.3 and Figure 11.2). Extreme temperatures on land tend to increase more than the global mean temperature (Figure 11.2), due in large part to the land–sea warming contrast, and additionally to regional feedbacks in some regions (Section 11.1.6). Increases in the intensity of temperature extremes scale robustly, and in general linearly, with global warming across different geographical regions in projections up to 2100, with minimal dependence on emissions scenarios (Section 11.2.4, Figure 11.3,and Cross-Chapter Box 11.1; Seneviratne et al., 2016; Wartenburger et al., 2017; Kharin et al., 2018). The frequency of hot temperature extremes (see Figure 11.6), the number of heatwave days and the length of heatwave seasons in various regions also scale well, but nonlinearly (because of threshold effects, Section 11.2.1), with global mean temperatures (Wartenburger et al., 2017; Y. Sun et al., 2018a).

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Changes in the spatial distribution of temperatures can also affect temperature extremes by modifying the characteristics of weather patterns (e.g., Suarez-Gutierrez et al., 2020a). For example, a robust thermodynamic effect of polar amplification is a weakened north-south temperature gradient, which amplifies the warming of cold extremes in the Northern Hemisphere mid- and high latitudes because of the reduction of cold air advection (Holmes et al., 2015; Schneider et al., 2015; Gross et al., 2020). Much less robust is the dynamic effect of polar amplification (Section 7.4.4.1) and the reduced low-altitude meridional temperature gradient that has been linked to an increase in the persistence of weather patterns (e.g., heatwaves) and subsequent increases in temperature extremes (Cross-Chapter Box 10.1; Francis and Vavrus, 2012; Coumou et al. , 2015, 2018; Mann et al., 2017).

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In addition to the lagged effect, the climate response at a given GWL may differ before and after a period of overshoot, for example in the Atlantic Meridional Overturning Circulation (e.g., Palter et al. 2018). Finally, as assessed in IPCC SR1.5, there is a difference in the response even for temperature-related variables if a GWL is reached in a rapidly warming transient state or in an equilibrium state when the land–sea warming contrast is less pronounced (e.g., King et al. 2020). However, in this Report, GWLs are used in the context of projections for the 21st century when the climate response is mostly not in equilibrium and where projections for many variables are less dependent on the pathway than for projections beyond 2100 (Section 9.6.3.4).

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A second metric that is argued to be comparatively less sensitive to data issues than frequency- and intensity-based metrics is TC translation speed (Kossin, 2018), which exhibits a global slowdown in the best-track data over the period 1949–2016. TC translation speed is a measure of the speed at which TCs move across the Earth’s surface, and is very closely related to local rainfall amounts (i.e., a slower translation speed causes greater local rainfall). TC translation speed also affects structural wind damage and coastal storm surge by changing the hazard event duration. The slowdown is observed in the best-track data from all basins except the Northern Indian Ocean, and is also found in a number of regions where TCs interact directly with land. The slowing trends identified in the best-track data by Kossin (2018) have been argued to be largely due to data heterogeneity. Moon et al. (2019) and Lanzante (2019) provide evidence that meridional TC track shifts project onto the slowing trends, and argue that these shifts are due to the introduction of satellite data. Kossin (2019) provides evidence that the slowing trend is real by focusing on Atlantic TC track data over the contiguous USA in the 118-year period 1900–2017, which are generally considered reliable. In this period, mean TC translation speed has decreased by 17%. The slowing TC translation speed is expected to increase local rainfall amounts, which would increase coastal and inland flooding. In combination with slowing translation speed, abrupt TC track direction changes – that can be associated with track ‘meanders’ or ‘stalls’ – have become increasingly common along the North American coast since the mid-20th century, leading to more rainfall in the region (Hall and Kossin, 2019).

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Studholme, J. and S. Gulev, 2018: Concurrent Changes to Hadley Circulation and the Meridional Distribution of Tropical Cyclones. Journal of Climate, 31(11), 4367–4389, doi: 10.1175/jcli-d-17-0852.1.

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Vimont, D.J. and J.P. Kossin, 2007: The Atlantic Meridional Mode and hurricane activity. Geophysical Research Letters, 34(7), L07709, doi: 10.1029/2007gl029683.

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Zhang, W., G.A. Vecchi, H. Murakami, G. Villarini, and L. Jia, 2016a: The Pacific meridional mode and the occurrence of tropical Cyclones in the western North Pacific. Journal of Climate, 29(1), 381–398, doi: 10.1175/jcli-d-15-0282.1.

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The regional Inter-tropical Convergence Zone (ITCZ) position, width and strength determine the location and seasonality of the tropical rain belt. Since AR5, multiple studies have linked cross-equatorial energy transport to the mean ITCZ position (Donohoe et al., 2013; Frierson et al., 2013; Bischoff and Schneider, 2014; Boos and Korty, 2016; Loeb et al., 2016; Adam et al., 2018; Biasutti and Voigt, 2019). Multi-model studies agree that aerosol cooling in the NH led to a southward shift in the ITCZ and tropical precipitation after the 1950s up to the 1980s that is linked with the 1980s Sahel drought (Box 8.1; Section 8.3.2.4 and 10.4.2.1). In particular, aerosol-cloud interaction was identified as a potentially important driver of this shift (Chung and Soden, 2017) but this is uncertain since observations suggest that models may overestimate (Malavelle et al. , 2017; Toll et al. , 2017) or underestimate (Rosenfeld et al. , 2019) the aerosol cloud-mediated cooling effects. In addition, greenhouse gas forcing has been invoked in explaining much of the increase in Sahel precipitation since the 1980s through enhanced meridional temperature gradient, with only a secondary role for aerosol (Dong and Sutton, 2015).

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In summary, there is high confidence that altered atmospheric wind patterns in response to radiative forcing and evolving surface temperature patterns will affect the regional water cycle in most regions. Mean tropical circulation is expected to slow with global warming (high confidence) but temporary multi-decadal strengthening is possible due to internal variability (medium confidence). Slowing of the tropical circulation reduces the meridional P–E gradient over the Pacific and can partly offset thermodynamic amplification of P–E patterns and strengthening of monsoons (high confidence) but regional characteristics of tropical rain belt changes are not well understood. There is medium confidence in processes driving strengthening and tightening of the ITCZ that increase the contrasts between wet and dry tropical weather regimes and seasons. There is high confidence in understanding of how radiative forcing and global warming drive a poleward expansion of the subtropics and mid-latitude storm tracks but onlylow confidence in how poleward expansion influences drying of subtropical and mid-latitude climates. There is low confidence in understanding how Arctic warming amplification affects mid-latitude regional water cycles but high confidence that thermodynamic strengthening of precipitation within weather systems and in monsoons and polar regions is robust to large-scale circulation changes.

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The local tropical edges of the meridional overturning cells (as diagnosed from the horizontally divergent wind) are more closely associated with hydroclimate variations than the subtropical ridge (Staten et al., 2019). Poleward expansion of the tropical belt strongly contributes to precipitation decline in the poleward edge of the subtropics (Cai et al., 2012; Scheff and Frierson, 2012; Timbal and Drosdowsky, 2013; He and Soden, 2017; H. Nguyen et al., 2018; Tang et al., 2018), although recent modelling evidence suggests that subtropical precipitation declines are a response to direct CO2 radiative forcing mainly over ocean, irrespective of the HC expansion (He and Soden, 2017). Both reanalyses datasets and climate model simulations suggest that the HC expansion is not associated with widespread, zonally symmetric subtropical drying over land (Schmidt and Grise, 2017).

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The AR5 reported low confidence in the observed weakening of the East Asian monsoon (EAsiaM) since the mid-20th century. Since AR5, there has been improved understanding of changes in the EAsiaM, based on paleoclimatic evidence, instrumental observations and climate modeling simulations. Rainfall reconstructions from the Loess Plateau in China indicate that the northern extent of the monsoon rain belts migrated at least 300 km to the north-west from the LGM to the mid-Holocene (Yang et al., 2015). Similarly, Pliocene reconstructions indicate stronger intensity of the EAsiaM with a more northward penetration of the monsoon rain belt (S. Yang et al., 2018a). EAsiaM variability has been related to Atlantic Meridional Overturning Circulation (AMOC) dynamics, especially during the last glacial period, but whether the relationship is negative or positive remains uncertain (Sun et al., 2012; Cheung et al., 2018; Kang et al., 2018).

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In the Sahel region, the emergence of this new rainfall regime is reflected in increased number of heavy and extreme events, compared to the 1970s1980s, still not exceeding the values registered in the 1950s to 1960s (Descroix et al., 2013, 2015; Panthou et al., 2014, 2018; Sanogo et al., 2015), and in higher interannual variability (W. Zhang et al., 2017b; Akinsanola and Zhou, 2020) associated with SST variations in the tropical Atlantic, Pacific and Mediterranean Sea (Rodríguez-Fonseca et al., 2015; Diakhaté et al., 2019). Increased frequency of extreme rainfall events impacts high flow occurrences of the large Sahelian rivers as well as small to meso-scale catchments (Wilcox et al., 2018). Overall, extreme intense precipitation events are more frequent in the Sahel since the beginning of the 21st century (Giannini et al. , 2013; Panthou et al. , 2014, 2018; Sanogo et al. , 2015; Taylor et al. , 2017). Intensification of mesoscale convective systems associated with extreme rainfall in the WAfriM is favoured by enhancement of meridional temperature gradient by the warming of the Sahara desert (Taylor et al., 2017) at a pace that is two to four times greater than that of the tropical-mean temperature (K.H. Cook et al., 2015; Vizy et al., 2017). Periods of monsoon-breaks and the persistence of low rainfall events are still prominent, particularly after the onset, thus exposing West Africa simultaneously to the potential impacts of dry spells (W. Zhang et al., 2017b) and also extreme localized rains and floods (Engel et al., 2017; Lafore et al., 2017). Occurrence of extreme events is compounded by land use and land cover changes leading to increased runoff (Bamba et al., 2015; Descroix et al., 2018).

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Since AR5, several studies have examined observed variability and changes in the Australian and Maritime Continent monsoon (AusMCM) using paleoclimate records, instrumental observations and modeling studies (Denniston et al., 2016; Zhang and Moise, 2016). Paleoclimate reconstructions and modelling indicate that the Indo–Australian monsoon may vary in or out of phase with the EAsiaM, depending on whether there is a meridional displacement or expansion of the tropical rainfall belt (Ayliffe et al., 2013; Denniston et al., 2016). For instance, mid-Holocene simulations suggest that the AusMCM weakens and contracts due to a decreased net energy input and a weaker dynamic component (D’Agostino et al., 2020b).

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Model simulations since AR5 project a more noticeable and consistent weakening of the Northern Hemisphere (NH) winter Hadley cell than the Southern Hemisphere (SH) winter cell (Seo et al., 2014; Zhou et al., 2016), related to changes in meridional temperature gradient, static stability, and tropopause height (Seo et al., 2014; D’Agostino et al., 2017). Changes in SST patterns reduces the magnitude of Hadley cell weakening (Gastineau et al., 2009; Ma et al., 2012). There is considerable structure in Hadley circulation strength changes with longitude, associated with cloud-circulation interactions (Su et al., 2014). Subtropical anticyclones are projected to intensify over the north Atlantic and south Pacific but to weaken elsewhere (He et al., 2017).

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Multiple lines of evidence, including both paleoclimate reconstructions and simulations, suggest that a severe weakening or collapse of Atlantic Meridional Overturning Circulation (AMOC, see Glossary) causes abrupt and profound changes in the global hydrological cycle (Chiang and Bitz, 2005; Broccoli et al., 2006; Chiang and Friedman, 2012; Jackson et al., 2015; Renssen et al., 2018). Deep water formation in the North Atlantic is dependent on a delicate balance of heat and salt fluxes (Buckley and Marshall, 2016); disruption in either of these due to melting ice sheets, a change in precipitation and evaporation, or ocean circulation can force AMOC to cross a tipping point (SROCC; Drijfhout et al., 2015). During the last deglacial transition, one such slowdown in AMOC – during the Younger Dryas event (12,800–11,700 years ago)– caused worldwide changes in precipitation patterns. These included a southward migration of the tropical ITCZ (Peterson et al., 2000; McGee et al., 2014; Schneider et al., 2014; Mohtadi et al., 2016; Reimi and Marcantonio, 2016; P.X. Wang et al., 2017) and systematic weakening of the African and Asian monsoons (Tierney and DeMenocal, 2013; Otto-Bliesner et al. , 2014; Cheng et al. , 2016; Grandey et al. , 2016; Wurtzel et al. , 2018). Conversely, the Southern Hemisphere (SH) monsoon systems intensified (Cruz et al. , 2005; Ayliffe et al. , 2013; Stríkis et al. , 2015, 2018; Campos et al. , 2019). Drying occurred in Meso-America (Lachniet et al., 2013) while the North American monsoon system was largely unaffected (Bhattacharya et al., 2018). The mid-latitude region in North America was wetter (Polyak et al. , 2004; Grimm et al. , 2006; Wagner et al. , 2010; Voelker et al. , 2015), while Europe was drier (Genty et al., 2006; Rach et al., 2017; Naughton et al., 2019). A transient coupled climate model simulation was able to reproduce the large-scale precipitation response to such an event (Figure 8.27a; Liu et al., 2009).

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Bakker, P. et al., 2016: Fate of the Atlantic Meridional Overturning Circulation: Strong decline under continued warming and Greenland melting. Geophysical Research Letters, 43(23), 12252–12260, doi: 10.1002/2016gl070457.

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Buckley, M.W. and J. Marshall, 2016: Observations, inferences, and mechanisms of the Atlantic Meridional Overturning Circulation: A review. Reviews of Geophysics, 54(1), 5–63, doi: 10.1002/2015rg000493.

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Chen, Y., B. Langenbrunner, and J.T. Randerson, 2018: Future Drying in Central America and Northern South America Linked With Atlantic Meridional Overturning Circulation. Geophysical Research Letters, 45(17), 9226–9235, doi: 10.1029/2018gl077953.

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Fasullo, J.T., B.L. Otto-Bliesner, and S. Stevenson, 2019: The Influence of Volcanic Aerosol Meridional Structure on Monsoon Responses over the Last Millennium. Geophysical Research Letters, 46(21), 12350–12359, doi: 10.1029/2019gl084377.

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Liu, W., S.-P. Xie, Z. Liu, and J. Zhu, 2017: Overlooked possibility of a collapsed Atlantic Meridional Overturning Circulation in warming climate. Science Advances, 3(1), e1601666, doi: 10.1126/sciadv.1601666.

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Parsons, L.A., J. Yin, J.T. Overpeck, R.J. Stouffer, and S. Malyshev, 2014: Influence of the Atlantic Meridional Overturning Circulation on the monsoon rainfall and carbon balance of the American tropics. Geophysical Research Letters, 41(1), 146–151, doi: 10.1002/2013gl058454.

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Reintges, A., T. Martin, M. Latif, and N.S. Keenlyside, 2017: Uncertainty in twenty-first century projections of the Atlantic Meridional Overturning Circulation in CMIP3 and CMIP5 models. Climate Dynamics, 49(5–6), 1495–1511, doi: 10.1007/s00382-016-3180-x.

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Sandeep, N. et al., 2020: South Asian monsoon response to weakening of Atlantic meridional overturning circulation in a warming climate. Climate Dynamics, 54(7–8), 3507–3524, doi: 10.1007/s00382-020-05180-y.

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Sandler, D. and N. Harnik, 2020: Future wintertime meridional wind trends through the lens of subseasonal teleconnections. Weather and Climate Dynamics, 1(2), 427–443, doi: 10.5194/wcd-1-427-2020.

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Simpson, I.R., R. Seager, M. Ting, and T.A. Shaw, 2016: Causes of change in Northern Hemisphere winter meridional winds and regional hydroclimate. Nature Climate Change, 6(1), 65–70, doi: 10.1038/nclimate2783.

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Studholme, J. and S. Gulev, 2018: Concurrent Changes to Hadley Circulation and the Meridional Distribution of Tropical Cyclones. Journal of Climate, 31(11), 4367–4389, doi: 10.1175/jcli-d-17-0852.1.

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Sun, Y. et al., 2012: Influence of Atlantic meridional overturning circulation on the East Asian winter monsoon. Nature Geoscience, 5(1), 46–49, doi: 10.1038/ngeo1326.

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Weaver, A.J. et al., 2012: Stability of the Atlantic meridional overturning circulation: A model intercomparison. Geophysical Research Letters, 39(20), 2012GL053763, doi: 10.1029/2012gl053763.

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Yang, H. et al., 2020: Tropical Expansion Driven by Poleward Advancing Midlatitude Meridional Temperature Gradients. Journal of Geophysical Research: Atmospheres, 125(16), e2020JD033158, doi: 10.1029/2020jd033158.

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With respect to the ocean, SROCC assessed that it is virtually certain that the ocean has warmed unabated since 1970 and has taken up more than 90% of the excess heat contributed by global warming. The rate of ocean warming has likely more than doubled since 1993. Over the period 1982–2016, marine heatwaves have very likely doubled in frequency and are increasing in intensity (very high confidence). In addition, the surface ocean acidified further (virtually certain) and loss of oxygen occurred from the surface to a depth of 1000 m (medium confidence). The Report expressed medium confidence that the Atlantic Meridional Overturning Circulation (AMOC) weakened in 2004–2017 relative to 1850–1900.

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Alkhayuon, H., P. Ashwin, L.C. Jackson, C. Quinn, and R.A. Wood, 2019: Basin bifurcations, oscillatory instability and rate-induced thresholds for Atlantic meridional overturning circulation in a global oceanic box model. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 475(2225), 20190051, doi: 10.1098/rspa.2019.0051.

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Karspeck, A.R. et al., 2017: Comparison of the Atlantic meridional overturning circulation between 1960 and 2007 in six ocean reanalysis products. Climate Dynamics, 49(3), 957–982, doi: 10.1007/s00382-015-2787-7.

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Lynch-Stieglitz, J., 2017: The Atlantic Meridional Overturning Circulation and Abrupt Climate Change. Annual Review of Marine Science, 9(1), 83–104, doi: 10.1146/annurev-marine-010816-060415.

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Trenberth, K.E., Y. Zhang, J.T. Fasullo, and L. Cheng, 2019: Observation-based estimates of global and basin ocean meridional heat transport time series. Journal of Climate, 32(14), 4567–4583, doi: 10.1175/jcli-d-18-0872.1.

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Weijer, W. et al., 2019: Stability of the Atlantic Meridional Overturning Circulation: A Review and Synthesis. Journal of Geophysical Research: Oceans, 124(8), 5336–5375, doi: 10.1029/2019jc015083.

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Dai, Z., D.K. Weisenstein, and D.W. Keith, 2018: Tailoring Meridional and Seasonal Radiative Forcing by Sulfate Aerosol Solar Geoengineering. Geophysical Research Letters, 45(2), 1030–1039, doi: 10.1002/2017gl076472.

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It has long been recognized that the magnitude of climate feedbacks can change as the climate state evolves over time (Manabe and Bryan, 1985; Murphy, 1995), but the implications for projected future warming have been investigated only recently. Since AR5, progress has been made in understanding the key mechanisms behind this time- and state-dependence. Specifically, the state-dependence is assessed by comparing climate feedbacks between warmer and colder climate states inferred from paleoclimate proxies and model simulations (Section 7.4.3). The time-dependence of the feedbacks is evident between the historical period and future projections and is assessed to arise from the evolution of the surface warming pattern related to changes in zonal and meridional temperature gradients (Section 7.4.4).

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The lapse-rate (LR) feedback quantifies the change in radiative flux at the TOA due to a non­uniform change in the vertical temperature profile. In the tropics, the vertical temperature profile is mainly driven by moist convection and is close to a moist adiabat. The warming is larger in the upper troposphere than in the lower troposphere (Manabe and Wetherald, 1975; Santer et al., 2005; Bony et al., 2006), leading to a larger radiative emission to space and therefore a negative feedback. This larger warming in the upper troposphere than at the surface has been observed over the last 20 years thanks to the availability of sufficiently accurate observations (Section 2.3.1.2.2). In the extratropics, the vertical temperature profile is mainly driven by a balance between radiation, meridional heat transport and ocean heat uptake (Rose et al., 2014). Strong winter temperature inversions lead to warming that is larger in the lower troposphere (Payne et al., 2015; Feldl et al., 2017a) and a positive LR feedback in polar regions (Section 7.4.4.1; Manabe and Wetherald, 1975; Bintanja et al., 2012; Pithan and Mauritsen, 2014). However, the tropical contribution dominates, leading to a negative global mean LR feedback (Soden and Held, 2006; Dessler, 2013; Vial et al., 2013; Caldwell et al., 2016). The LR feedback has been estimated at interannual time scales using meteorological reanalysis and satellite measurements of TOA fluxes (Dessler, 2013). These estimates from climate variability are consistent between observations and ESMs (Dessler, 2013; Colman and Hanson, 2017). The mean and standard deviation of this feedback under global warming based on the cited studies are α LR= –0.50 ± 0.20 W m–2°C–1(Dessler, 2013; Caldwell et al., 2016; Colman and Hanson, 2017; Zelinka et al., 2020).

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Energy balance models that approximate atmospheric heat transport in terms of a diffusive flux down the meridional gradient of near-surface moist static energy (sum of dry-static and latent energy) are able to reproduce the atmospheric heat transport changes seen within ESMs (Flannery, 1984; Hwang and Frierson, 2010; Hwang et al., 2011; Rose et al., 2014; Roe et al., 2015; Merlis and Henry, 2018), including the partitioning of latent and dry-static energy transports (Siler et al., 2018b; Armour et al., 2019). These models suggest that polar amplification is driven by enhanced poleward latent heat transport and that the magnitude of polar amplification can be enhanced or diminished by the latitudinal structure of radiative feedbacks. Amplifying polar feedbacks enhance polar warming and in turn cause a decrease in the dry-static energy transport to high latitudes (Alexeev and Jackson, 2013; Rose et al., 2014; Roe et al., 2015; Bonan et al., 2018; Merlis and Henry, 2018; Armour et al., 2019; Russotto and Biasutti, 2020). Poleward latent heat transport changes act to favour polar amplification and inhibit tropical amplification (Armour et al., 2019), resulting in a strongly polar-amplified warming response to polar forcing and a more latitudinally uniform warming response to tropical forcing within ESMs (Alexeev et al., 2005; Rose et al., 2014; Stuecker et al., 2018). The important role for poleward latent energy transport in polar amplification is supported by studies of atmospheric reanalyses and ESMs showing that episodic increases in latent heat transport into the Arctic can enhance surface downwelling radiation and drive sea ice loss on sub-seasonal time scales (Woods and Caballero, 2016; Gong et al., 2017; Lee et al., 2017; B. Luo et al., 2017), however this may be a smaller driver of sea ice variability than atmospheric temperature fluctuations (Olonscheck et al., 2019).

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Armour, K.C., N. Siler, A. Donohoe, and G.H. Roe, 2019: Meridional Atmospheric Heat Transport Constrained by Energetics and Mediated by Large-Scale Diffusion. Journal of Climate, 32(12), 3655–3680, doi: 10.1175/jcli-d-18-0563.1.

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Bonan, D.B., K.C. Armour, G.H. Roe, N. Siler, and N. Feldl, 2018: Sources of Uncertainty in the Meridional Pattern of Climate Change. Geophysical Research Letters, 45(17), 9131–9140, doi: 10.1029/2018gl079429.

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Donohoe, A. and D.S. Battisti, 2012: What determines meridional heat transport in climate models?Journal of Climate, 25(11), 3832–3850, doi: 10.1175/jcli-d-11-00257.1.

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Donohoe, A., K.C. Armour, G.H. Roe, D.S. Battisti, and L. Hahn, 2020: The partitioning of meridional heat transport from the Last Glacial Maximum to CO2 quadrupling in coupled climate models. Journal of Climate, 33, 4141–4165, doi: 10.1175/jcli-d-19-0797.1.

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Fedorov, A., N.J. Burls, K.T. Lawrence, and L.C. Peterson, 2015: Tightly linked zonal and meridional sea surface temperature gradients over the past five million years. Nature Geoscience, 8, 975–980, doi: 10.1038/ngeo2577.

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Huang, Y. and M. Zhang, 2014: The implication of radiative forcing and feedback for meridional energy transport. Geophysical Research Letters, 41(5), 1665–1672, doi: 10.1002/2013gl059079.

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Kang, S.M. and S.P. Xie, 2014: Dependence of climate response on meridional structure of external thermal forcing. Journal of Climate, 27(14), 5593–5600, doi: 10.1175/jcli-d-13-00622.1.

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Kostov, Y., K.C. Armour, and J. Marshall, 2014: Impact of the Atlantic meridional overturning circulation on ocean heat storage and transient climate change. Geophysical Research Letters, 41(6), 2108–2116, doi: 10.1002/2013gl058998.

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Liu, W., S.-P. Xie, Z. Liu, and J. Zhu, 2017: Overlooked possibility of a collapsed Atlantic Meridional Overturning Circulation in warming climate. Science Advances, 3(1), e1601666, doi: 10.1126/sciadv.1601666.

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Wen, Q., J. Yao, K. Döös, and H. Yang, 2018: Decoding Hosing and Heating Effects on Global Temperature and Meridional Circulations in a Warming Climate. Journal of Climate, 31(23), 9605–9623, doi: 10.1175/jcli-d-18-0297.1.

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Zhang, Z. et al., 2021: Mid-Pliocene Atlantic Meridional Overturning Circulation simulated in PlioMIP2. Climate of the Past, 17(1), 529–543, doi: 10.5194/cp-17-529-2021.

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There are two major advances of this chapter compared with AR5 and SROCC facilitated by community efforts. The first is the temporal and spatial increase in observations of both the ocean and the cryosphere (Section 1.5.1.1). In particular, extended observations have allowed for improved assessment of past change and closure of both the energy and sea level budgets in a consistent way (Cross-Chapter Box 9.1) and the sea level budget for the last century (Section 9.6.1.1). Higher resolution observations have revealed the details of the Atlantic Meridional Overturning Circulation (AMOC; Section 9.2.3.1) and globally resolved glacier changes for the first time (Section 9.5.1.1). Improved methodology has resulted in a doubling of the assessed level of observed increase in global ocean 0–200 m stratification compared to SROCC assessment (Section 9.2.1.3).

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Different processes drive OHC patterns over a range of time scales. Recent literature has highlighted the role of ocean circulation variability in driving OHC patterns by decomposing the global pattern of OHC change into a combination of added heat due to climate change taken up under fixed ocean circulation (‘added heat’), and redistribution of heat associated with changing ocean currents (‘redistributed heat’; Gregory et al., 2016; Bronselaer and Zanna, 2020; Couldrey et al., 2021). Redistributed heat alters regional patterns of heat storage and carbon storage (Cross-Chapter Box 5.3; Bronselaer and Zanna, 2020; Todd et al., 2020; Couldrey et al., 2021) but does not affect the global OHC. There is medium confidence that decadal variability of the ocean circulation strengthened the rate of ocean warming in the Southern Hemisphere compared to the Northern Hemisphere in the decade from 2005 (Rathore et al., 2020; L. Wang et al., 2021; Zika et al., 2021). More generally, since 2005, the OHC pattern observed is predominantly due to heat redistribution with regions of both warming and cooling (Figure 9.6; Zika et al., 2021); however, extending analysis back to 1972 shows the importance of added heat setting a large-scale warming pattern with mid-latitude maxima consistent with subduction of water masses, particularly in Southern Hemisphere Mode Waters (Section 9.2.2.3, and Figures 9.6 and 9.8; Bronselaer and Zanna, 2020). The longer the analysis window, the more added heat dominates over redistributed heat. This translates into more ocean area with statistically significant warming trends and less area with statistically significant cooling trends (Johnson and Lyman, 2020). The region where added heat is most compensated for by redistributed cooling is in the northern North Atlantic basin, where changes in the subpolar gyre circulation and Atlantic Meridional Overturning Circulation (AMOC) result in cooling (Section 9.2.3.1; Williams et al., 2015; Piecuch et al., 2017; Zanna et al., 2019; Bronselaer and Zanna, 2020). In summary, and strengthening SROCC assessment, ocean warming is not globally uniform due to patterns of uptake predominantly along known water mass pathways, and due to changing ocean circulation redistributing heat within the ocean (high confidence).

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The Southern Ocean Meridional Dipole is driven by a northward advection of excess heat (from changes in surface fluxes) by the wind-driven circulation followed by subduction or diffusive uptake in mid-latitudes, northward redistribution of existing heat by the strengthening of that circulation, and the meridional contrast in thermal expansivity due to its temperature-dependence (Armour et al., 2016; Gregory et al., 2016; Lyu et al., 2020b; Todd et al., 2020; Couldrey et al., 2021).

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The SROCC assessed that the regional trends are closely related to meridional wind trends (high confidence). This is the case as the regional trends in the maximum northward extent of the ice cover (Figure 9.15) are determined by the balance between the northward advection of the ice that is formed in polynyas near the continental margin, and the lateral and subsurface melting through oceanic heat fluxes. The advection of the sea ice is strongly correlated with winds and cyclones (Schemm, 2018; Vichi et al., 2019; Alberello et al., 2020). Accordingly, the increasing sea ice area in the Ross Sea can be linked to a strengthening of the Amundsen Sea low (e.g., Holland et al., 2017b, 2018), while other regional sea ice trends in the austral autumn can be linked to changes in westerly winds, cyclone activity and the Southern Annular Mode (SAM) in summer and spring (Doddridge and Marshall, 2017; Holland et al., 2017a; Schemm, 2018). In addition to the wind-driven changes, increased near-surface ocean stratification (Section 9.2.1.3) has contributed to the observed increase in sea ice coverage (e.g., Purich et al., 2018; L. Zhang et al., 2019) as it tends to cool the surface ocean (Sections 9.2.1.1 and 9.2.3.2). The changes in stratification result partly from surface freshening (De Lavergne et al., 2014), associated with increased northward sea ice advection (Haumann et al., 2020) and/or melting of the Antarctic ice sheet (medium confidence) (e.g., Haumann et al., 2020; Jeong et al., 2020; Mackie et al., 2020), and amplified by local ice–ocean feedbacks (Goosse and Zunz, 2014; Lecomte et al., 2017; Goosse et al., 2018). In the Amundsen Sea, strong ice shelf melting can cause local sea ice melt next to the ice shelf front by entraining warm circumpolar deep water to the ice shelf cavity and surface ocean (medium confidence) (Sections 9.2.3.2 and 9.4.2.2; Jourdain et al., 2017; Merino et al., 2018). It has also been suggested that the observed regional increase in sea ice coverage since 1979 results from a long-term Southern Ocean surface cooling trend (e.g., Kusahara et al., 2019; Jeong et al., 2020) but the importance of this mechanism for the observed sea ice evolution is unclear owing to intricate feedbacks between sea ice change and surface cooling (Haumann et al., 2020). The importance of changing wave activity (Section 9.6.4.2; Kohout et al., 2014; Bennetts et al., 2017; Roach et al., 2018b) on sea ice is unclear due to limited process understanding. In summary, there is high confidence that regional Antarctic trends are primarily caused by changes in sea ice drift and decay, with medium confidence in a dominating role of changing wind pattern. The precise relative contribution of individual drivers remains uncertain because of limited observations, disagreement between models, unresolved processes, and temporal and spatial remote linkages caused by sea ice drift (Section 9.2.3.2; Pope et al., 2017).

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The SROCC (Oppenheimer et al., 2019) noted the occurrence of large multiannual sea level variations in the Pacific, associated with the Pacific Decadal Oscillation (PDO) in particular, and involving the El Niño Southern Oscillation (ENSO), North Pacific Gyre Oscillation (NPGO) and Indian Ocean Dipole (IOD; Annex IV; Royston et al., 2018; Hamlington et al., 2020b). There was intensified sea level rise during the 1990s and 2000s, with 10-year trends exceeding 20 mm yr–1in the western tropical Pacific Ocean, while sea level trends were negative on the North American west coast. During the 2010s, the situation reversed, with western Pacific sea level falling at more than 10 mm yr–1(Hamlington et al., 2020b). For the Atlantic Ocean, SROCC described regional sea level variability as being driven primarily by wind and heat flux variations associated with the North Atlantic Oscillation (NAO) and heat transport changes associated with Atlantic Meridional Overturning Circulation (AMOC) variability . During periods of subpolar North Atlantic warming, winds along the European coast are predominantly from the south and may communicate steric anomalies onto the continental shelf, driving regional sea level rise, with the reverse during periods of cooling (Chafik et al., 2019). High rates of sea level rise in the North Indian Ocean are accompanied by a weakening summer South Asian monsoon circulation (Swapna et al., 2017).

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Processes that change on long time scales – particularly Atlantic Meridional Overturning Circulation, ocean heat content, and ice sheets – require additional projections beyond the CMIP scenarios to explore longer-term commitment, post-forcing recovery measured in centuries rather than years or decades, and potential tipping points and thresholds. Only a few new studies focused on longer time scales, and none based on CMIP6 models.

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The Gulf Stream is part of two circulation patterns in the North Atlantic: the Atlantic Meridional Overturning Circulation (AMOC) and the North Atlantic subtropical gyre. Based on models and theory, scientific studies indicate that, while the AMOC is expected to slow in a warming climate, the Gulf Stream will not change much and would not shut down totally, even if the AMOC did. Most climate models project that the AMOC slows in the later 21st century under most emissions scenarios, with some models showing it slowing even sooner. The Gulf Stream affects the weather and sea level, so if it slows, North America will see higher sea levels and Europe’s weather and rate of relative warming will be affected.

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Buckley, M.W. and J. Marshall, 2016: Observations, inferences, and mechanisms of the Atlantic Meridional Overturning Circulation: A review. Reviews of Geophysics, 54(1), 5–63, doi: 10.1002/2015rg000493.

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Caesar, L., G.D. McCarthy, D.J.R. Thornalley, N. Cahill, and S. Rahmstorf, 2021: Current Atlantic Meridional Overturning Circulation weakest in last millennium. Nature Geoscience, 14(3), 118–120, doi: 10.1038/s41561-021-00699-z.

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Chafik, L. and T. Rossby, 2019: Volume, Heat, and Freshwater Divergences in the Subpolar North Atlantic Suggest the Nordic Seas as Key to the State of the Meridional Overturning Circulation. Geophysical Research Letters, 46(9), 4799–4808, doi: 10.1029/2019gl082110.

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Cheng, W., J.C.H.H. Chiang, and D. Zhang, 2013: Atlantic Meridional Overturning Circulation (AMOC) in CMIP5 Models: RCP and Historical Simulations. Journal of Climate, 26(18), 7187–7197, doi: 10.1175/jcli-d-12-00496.1.

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de Vries, P. and S.L. Weber, 2005: The Atlantic freshwater budget as a diagnostic for the existence of a stable shut down of the meridional overturning circulation. Geophysical Research Letters, 32(9), L09606, doi: 10.1029/2004gl021450.

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Kostov, Y., K.C. Armour, and J. Marshall, 2014: Impact of the Atlantic meridional overturning circulation on ocean heat storage and transient climate change. Geophysical Research Letters, 41(6), 2108–2116, doi: 10.1002/2013gl058998.

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Kuhlbrodt, T. et al., 2007: On the driving processes of the Atlantic meridional overturning circulation. Reviews of Geophysics, 45(2), RG2001, doi: 10.1029/2004rg000166.

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Little, C.M. et al., 2019: The Relationship Between U.S. East Coast Sea Level and the Atlantic Meridional Overturning Circulation: A Review. Journal of Geophysical Research: Oceans, 124(9), 6435–6458, doi: 10.1029/2019jc015152.

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Liu, W. and Z. Liu, 2013: A Diagnostic Indicator of the Stability of the Atlantic Meridional Overturning Circulation in CCSM3. Journal of Climate, 26(6), 1926–1938, doi: 10.1175/jcli-d-11-00681.1.

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Liu, W., J. Lu, and S.-P. Xie, 2018: Southern Ocean Heat Uptake, Redistribution, and Storage in a Warming Climate: The Role of Meridional Overturning Circulation. Journal of Climate, 31, 4727–4743, doi: 10.1175/jcli-d-17.

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Liu, W., S.-P. Xie, Z. Liu, and J. Zhu, 2017: Overlooked possibility of a collapsed Atlantic Meridional Overturning Circulation in warming climate. Science Advances, 3(1), e1601666, doi: 10.1126/sciadv.1601666.

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Pickart, R.S. and M.A. Spall, 2007: Impact of Labrador Sea convection on the North Atlantic meridional overturning circulation. Journal of Physical Oceanography, 37(9), 2207–2227, doi: 10.1175/jpo3178.1.

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Reintges, A., T. Martin, M. Latif, and N.S. Keenlyside, 2017: Uncertainty in twenty-first century projections of the Atlantic Meridional Overturning Circulation in CMIP3 and CMIP5 models. Climate Dynamics, 49(5–6), 1495–1511, doi: 10.1007/s00382-016-3180-x.

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Roberts, C.D., L. Jackson, and D. McNeall, 2014: Is the 2004–2012 reduction of the Atlantic meridional overturning circulation significant?Geophysical Research Letters, 41(9), 3204–3210, doi: 10.1002/2014gl059473.

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Roberts, M.J. et al., 2020: Sensitivity of the Atlantic Meridional Overturning Circulation to Model Resolution in CMIP6 HighResMIP Simulations and Implications for Future Changes. Journal of Advances in Modeling Earth Systems, 12(8), e2019MS002014, doi: 10.1029/2019ms002014.

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Trenberth, K.E., Y. Zhang, J.T. Fasullo, and L. Cheng, 2019: Observation-Based Estimates of Global and Basin Ocean Meridional Heat Transport Time Series. Journal of Climate, 32(14), 4567–4583, doi: 10.1175/jcli-d-18-0872.1.

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Weijer, W., W. Cheng, O.A. Garuba, A. Hu, and B.T. Nadiga, 2020: CMIP6 Models Predict Significant 21st Century Decline of the Atlantic Meridional Overturning Circulation. Geophysical Research Letters, 47(12), e2019GL086075, doi: 10.1029/2019gl086075.

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Weijer, W. et al., 2019: Stability of the Atlantic Meridional Overturning Circulation: A Review and Synthesis. Journal of Geophysical Research: Oceans, 124(8), 5336–5375, doi: 10.1029/2019jc015083.

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Zhang, R. et al., 2019: A Review of the Role of the Atlantic Meridional Overturning Circulation in Atlantic Multidecadal Variability and Associated Climate Impacts. Reviews of Geophysics, 57(2), 316–375, doi: 10.1029/2019rg000644.

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The probability of low- likelihood outcomes associated with potentially very large impacts increases with higher global warming levels (high confidence). Warming substantially above the assessed very likely range for a given scenario cannot be ruled out, and there is high confidence this would lead to regional changes greater than assessed in many aspects of the climate system. Low-likelihood, high-impact outcomes could occur at regional scales even for global warming within the very likely assessed range for a given GHG emissions scenario. Global mean sea level rise above the likely range – approaching 2 m by 2100 and in excess of 15 m by 2300 under a very high GHG emissions scenario (SSP5-8.5) (lowconfidence) – cannot be ruled out due to deep uncertainty in ice-sheet processes 123 and would have severe impacts on populations in low elevation coastal zones. If global warming increases, some compound extreme events 124 will become more frequent, with higher likelihood of unprecedented intensities, durations or spatial extent (high confidence). The Atlantic Meridional Overturning Circulation is very likely to weaken over the 21st century for all considered scenarios (high confidence), however an abrupt collapse is not expected before 2100 (medium confidence). If such a low probability event were to occur, it would very likely cause abrupt shifts in regional weather patterns and water cycle, such as a southward shift in the tropical rain belt, and large impacts on ecosystems and human activities. A sequence of large explosive volcanic eruptions within decades, as have occurred in the past, is a low-likelihood high-impact event that would lead to substantial cooling globally and regional climate perturbations over several decades. {WGI SPM B.5.3, WGI SPM C.3, WGI SPM C.3.1, WGI SPM C.3.2, WGI SPM C.3.3, WGI SPM C.3.4, WGI SPM C.3.5, WGI Figure SPM.8, WGI Box TS.3, WGI Figure TS.6, WGI Box 9.4; WGII SPM B.4.5, WGII SPM C.2.8; SROCC SPM B.2.7}. (Figure 3.4, Cross-SectionBox.2)

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Frajka-Williams, E., et al., 2019: Atlantic meridional overturning circulation: observed transport and variability. Front. Mar. Sci. , 6, 260, doi:10.3389/fmars.2019.00260.

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Perez, F.F., et al., 2018: Meridional overturning circulation conveys fast acidification to the deep Atlantic Ocean. Nature, 554 (7693), 515–518, doi:10.1038/nature25493.

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This RFC, large-scale singular events (sometimes called tipping points or critical thresholds), considers abrupt, drastic and sometimes irreversible changes in physical, ecological or social systems in response to smooth variations in driving forces (accompanied by natural variability) (Oppenheimer et al., 2014; O’Neill et al., 2017). SR15 Section 3.5.2.5 presented four examples, including the cryosphere (West Antarctic ice sheet, Greenland ice sheet), thermohaline circulation (slowdown of the Atlantic Meridional Overturning Circulation), the El Niño-Southern Oscillation (ENSO) as a global mode of climate variability, and the role of the Southern Ocean in the global carbon cycle (Hoegh-Guldberg et al., 2018b). While most of the literature assessed here focuses on the resultant changes to climate-related hazards such as sea level rise, in this assessment, evidence about the implications of accelerated sea level rise for human and natural systems is also considered. If sea level rise is accelerated by ice sheet melt, the associated impacts are projected to occur decades earlier than otherwise, directly affecting coastal systems including cities and settlements by the sea (CCP2) and wetlands (Chapter 2). The associated disruption to ports is projected to severely compromise global supply chains and maritime trade with local–global geo-political and economic consequences. To compensate for this acceleration, adaptation would need to occur much faster and at a much greater scale than otherwise, or indeed than has previously been observed (CCP2). The costs of accommodating port growth and adapting to sea level rise amount to USD 22–768 billion before 2050 globally (medium evidence, high agreement ) (see Sections 2.1, 2.2; Cross-Chapter Box SLR in Chapter 3).

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Zhang, R. et al., 2019: A Review of the Role of the Atlantic Meridional Overturning Circulation in Atlantic Multidecadal Variability and Associated Climate Impacts. Rev. Geophys. , 57(2) , 316–375, doi:10.1029/2019RG000644.

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Trade-offs and spillover effects: Potential drawbacks include subsurface ocean acidification and deoxygenation (Cao and Caldeira 2010; Oschlies et al., 2010 ; Williamson et al. 2012); altered regional meridional nutrient supply and fundamental alteration of food webs (GESAMP 2019); and increased production of N2O and CH4 (Jin and Gruber 2003; Lampitt et al. 2008). Ocean fertilisation is considered to have negative consequences for eight SDGs, and a combination of both positive and negative consequences for seven SDGs (Honegger et al. 2020).

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The probability of low- likelihood outcomes associated with potentially very large impacts increases with higher global warming levels (high confidence). Warming substantially above the assessed very likely range for a given scenario cannot be ruled out, and there is high confidence this would lead to regional changes greater than assessed in many aspects of the climate system. Low-likelihood, high-impact outcomes could occur at regional scales even for global warming within the very likely assessed range for a given GHG emissions scenario. Global mean sea level rise above the likely range – approaching 2 m by 2100 and in excess of 15 m by 2300 under a very high GHG emissions scenario (SSP5-8.5) (lowconfidence) – cannot be ruled out due to deep uncertainty in ice-sheet processes 123 and would have severe impacts on populations in low elevation coastal zones. If global warming increases, some compound extreme events 124 will become more frequent, with higher likelihood of unprecedented intensities, durations or spatial extent (high confidence). The Atlantic Meridional Overturning Circulation is very likely to weaken over the 21st century for all considered scenarios (high confidence), however an abrupt collapse is not expected before 2100 (medium confidence). If such a low probability event were to occur, it would very likely cause abrupt shifts in regional weather patterns and water cycle, such as a southward shift in the tropical rain belt, and large impacts on ecosystems and human activities. A sequence of large explosive volcanic eruptions within decades, as have occurred in the past, is a low-likelihood high-impact event that would lead to substantial cooling globally and regional climate perturbations over several decades. {WGI SPM B.5.3, WGI SPM C.3, WGI SPM C.3.1, WGI SPM C.3.2, WGI SPM C.3.3, WGI SPM C.3.4, WGI SPM C.3.5, WGI Figure SPM.8, WGI Box TS.3, WGI Figure TS.6, WGI Box 9.4; WGII SPM B.4.5, WGII SPM C.2.8; SROCC SPM B.2.7}. (Figure 3.4, Cross-SectionBox.2)

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For the water-resource planner who has to deal with potential drought like the 2015–2017 event, several lines of evidence indicate future drying: the projected precipitation by global models and RCMs of different spatial resolutions, and the observed and projected changes of circulation patterns consistent with drier conditions, the paleoclimatic evidence confirming a millennial-scale circulation–rainfall link. However, the distillation is limited by a lack of information about whether or not a relationship between Cape Town precipitation and large-scale circulation processes adequately explains droughts in the twentieth century prior to 1979.

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Since AR5, there have been updates on the observed long-term variations and changes in the North American monsoon (NAmerM). During the Last Glacial Maximum (LGM; 21,00019,000 years ago), the NAmerM was substantially weaker due to cold, dry mid-latitude air associated with the Laurentide Ice Sheet (T. Bhattacharya et al. , 2017, 2018). The NAmerM strengthened until the mid-Holocene period, in response to ice-emsheet retreat and rising summer insolation, but probably did not exceed the strength of the modern system (low confidence), as indicated by model simulations (Metcalfe et al., 2015) and paleoclimatic reconstructions (Bhattacharya et al., 2018). Paleoclimatic evidence from proxy datasets and mid-Pliocene (PlioMIP1) simulations suggest a wetter south-western USA during that warmer period (A.M. Haywood et al., 2013; Pound et al., 2014; Ibarra et al., 2018) but it is not clear whether this is due to increases of precipitation associated with the monsoon or occurring during the winter season.

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Since AR5, paleoclimatic studies have improved our view of the timing, spatial extent, and speed of transitions associated with the early Holocene (11,000–5,000 years ago) Green Sahara. Observed transitions into and out of Green Sahara states are always faster than the underlying forcing, in agreement with theoretical considerations (high confidence) (Tierney and DeMenocal, 2013; Shanahan et al., 2015; Tierney et al., 2017). However, there is low confidence in the duration of the transition because sedimentary records cannot typically resolve changes on decadal to multi-decadal time scales (Tierney and DeMenocal, 2013). Both paleoclimate data and modelling experiments suggest that the timing and speed of the transition was spatially heterogeneous (high confidence), with northern Saharan locations becoming drier thousands of years before more equatorial locations (Shanahan et al., 2015; Tierney et al., 2017; Dallmeyer et al., 2020). These observations are consistent with theoretical studies suggesting that spatial heterogeneity and diversity in ecosystems can mitigate the probability of catastrophic change (Van Nes and Scheffer, 2005; Bathiany et al., 2013). Conversely, low ecosystem diversity can produce local or regional ‘hot spots’ of abrupt change such as those seen in some paleoclimate records (Claussen et al., 2013).

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Process Understanding (Chapters 5, 6, 7, 8 and 9). These five chapters provide end-to-end assessments of fundamental Earth system processes and components: the carbon budget and biogeochemical cycles (Chapter 5), short-lived climate forcers and their links to air quality (Chapter 6), the Earth’s energy budget and climate sensitivity (Chapter 7), the water cycle (Chapter 8), and the ocean, cryosphere and sea level changes (Chapter 9). All these chapters provide assessments of observed changes, including relevant paleoclimatic information and understanding of processes and mechanisms as well as projections and model evaluation.

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Paleoclimate archives (e.g., ice cores, corals, marine and lake sediments, speleothems, tree rings, borehole temperatures, soils) permit the reconstruction of climatic conditions before the instrumental era. This establishes an essential long-term context for the climate change of the past 150 years and the projected changes in the 21st century and beyond (Chapter 3; IPCC, 2013a; Masson-Delmotte et al., 2013). Figure 1.5 shows reconstructions of three key indicators of climate change over the past 800,000 years (800 kyr)2– atmospheric CO2 concentrations, global mean surface temperature (GMST) and global mean sea level (GMSL) – comprising at least eight complete glacial–interglacial cycles (EPICA Community Members, 2004; Jouzel et al., 2007), which are largely driven by oscillations in the Earth’s orbit and consequent feedbacks on multi-millennial time scales (Berger, 1978; Laskar et al., 1993). The dominant cycles – recurring approximately every 100 kyr – can be found imprinted in the natural variations of these three key indicators. Before industrialisation, atmospheric CO2 concentrations varied between 174 ppm and 300 ppm, as measured directly in air trapped in ice at Dome Concordia, Antarctica (Bereiter et al., 2015; Nehrbass-Ahles et al., 2020). Relative to 1850–1900 CE, the reconstructed GMST changed in the range of –6°C to +1°C across these glacial–interglacial cycles (see Chapter 2, Section 2.3.1 for an assessment of different paleo-reference periods). GMSL varied between about –130 m during the coldest glacial maxima and +5 to +25 m during the warmest interglacial periods (Chapter 2; Spratt and Lisiecki, 2016). They represent the amplitudes of natural, global-scale climate variations over the last 800 kyr prior to the influence of human activity. Further climate information from a variety of paleoclimatic archives is assessed in Chapters 2, 5, 7 and 9.

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Paleoclimatic information also provides a long-term perspective on rates of change of these three key indicators. In high-resolution reconstructions from polor ice cores, the rate of increase in atmospheric CO2 observed over 1919–2019 CE is one order of magnitude higher than the fastest CO2 fluctuations documented during the Last Glacial Maximum and the last deglacial transition (Marcott et al., 2014, see Chapter 2, Section 2.2.3.2.1). Current multi-decadal GMST exhibit a higher rate of increase than over the past 2 kyr (Section 2.3.1.1.2; PAGES 2k Consortium, 2019), and in the 20th century GMSL rise was faster than during any other century over the past 3 kyr (Section 2.3.3.3).

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The AR5 WGI (IPCC, 2013b) used paleoclimatic evidence to put recent warming and sea level rise in a multi-century perspective and assessed that 1983–2012 was likely to have been the warmest 30-year period of the last 1400 years in the Northern Hemisphere (medium confidence). The AR5 also assessed that the rate of sea level rise since the mid-19th century has been larger than the mean rate during the previous two millennia (high confidence).

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Progress in climate science relies on the quality and quantity of observations from a range of platforms: surface-based instrumental measurements, aircraft, radiosondes and other upper-atmospheric observations, satellite-based retrievals, ocean observations, and paleoclimatic records. An historical perspective to these types of observations is presented in Section 1.3.1.

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Manabe, S. and K. Bryan, 1985: CO2-induced change in a coupled ocean–atmosphere model and its paleoclimatic implications. Journal of Geophysical Research: Oceans, 90(C6), 11689, doi: 10.1029/jc090ic06p11689.

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The most recent time interval when atmospheric CO2 concentration was as high as 1000 ppm (i.e., similar to the end of 21st century projection for the high-end emissions scenario RCP8.5) was around 33.5 Ma, prior to the Eocene-Oligocene transition (Zhang et al., 2013; Anagnostou et al., 2016). Atmospheric CO2 levels then reached a critical threshold (1000–750 ppm; DeConto et al., 2008) to allow for the development of permanent regional ice sheets on Antarctica, associated with changes in Southern Ocean hydrography, which would have increased deep ocean CO2 storage (Leutert et al., 2020).

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Talley, L.D. et al., 2016: Changes in Ocean Heat, Carbon Content, and Ventilation: A Review of the First Decade of GO-SHIP Global Repeat Hydrography. Annual Review of Marine Science, 8(1), 185–215, doi: 10.1146/annurev-marine-052915-100829.

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For subsurface pH changes, estimates arise from direct ship measurements from repeated hydrography programs (Carter et al., 2019), indirect estimates of pH through calcite and aragonite saturation horizons (Osborne et al., 2020; Ross et al., 2020), and the very recent biogeochemical Argo floats equipped with pH sensors (Claustre et al., 2020). Global subsurface pH has decreased over the past 20 to 30 years, with signals observed to at least 1000 m depths (Lauvset et al., 2020). Global findings are supplemented by regional findings from the Pacific Ocean (Carter et al., 2019; Ross et al., 2020); the South Atlantic (Salt et al., 2015) and Southern Ocean (Jones et al., 2017); the North Atlantic Ocean and along the AMOC (Woosley et al., 2016; Perez et al., 2018), the Arctic Ocean (Qi et al., 2017) and marginal seas (C.-T.A. Chen et al., 2017). Further details are given in Section 5.3.3.1.

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Martínez-Méndez, G. et al., 2010: Contrasting multiproxy reconstructions of surface ocean hydrography in the Agulhas Corridor and implications for the Agulhas Leakage during the last 345,000 years. Paleoceanography, 25(4), PA4227, doi: 10.1029/2009pa001879.

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Rossby, T., L. Chafik, and L. Houpert, 2020: What can Hydrography Tell Us About the Strength of the Nordic Seas MOC Over the Last 70 to 100 Years?Geophysical Research Letters, 47(12), e2020GL087456, doi: 10.1029/2020gl087456.

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Thornalley, D.J.R., H. Elderfield, and I.N. McCave, 2011: Reconstructing North Atlantic deglacial surface hydrography and its link to the Atlantic overturning circulation. Global and Planetary Change, 79(3–4), 163–175, doi: 10.1016/j.gloplacha.2010.06.003.

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Bjerknes, V.F.K., J.W. Sandström, T. Hesselberg, and O.M. Devik, 1910: Dynamic Meteorology and Hydrography. Carnegie Institution of Washington, Washington, DC, USA, 2 v. pp.

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Desbruyères, D., E.L. McDonagh, B.A. King, and V. Thierry, 2017: Global and Full-Depth Ocean Temperature Trends during the Early Twenty-First Century from Argo and Repeat Hydrography. Journal of Climate, 30(6), 1985–1997, doi: 10.1175/jcli-d-16-0396.1.

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Rossby, T., L. Chafik, and L. Houpert, 2020: What can Hydrography Tell Us About the Strength of the Nordic Seas MOC Over the Last 70 to 100 Years?Geophysical Research Letters, 47(12), e2020GL087456, doi: 10.1029/2020gl087456.

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Lehner, B., K. Verdin and A. Jarvis, 2008: New Global Hydrography Derived From Spaceborne Elevation Data. Eos, Transactions American Geophysical Union, 89 (10), 93–94, doi:10.1029/2008EO100001.

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Suca, J.J., et al., 2021: Sensitivity of sand lance to shifting prey and hydrography indicates forthcoming change to the northeast US shelf forage fish complex. ICES J. Mar. Sci. , 78 (3), 1023–1037, doi:10.1093/icesjms/fsaa251.

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Simulations of Earth models of intermediate complexity (EMIC) with coupled glacial–interglacial climate and the carbon cycle were able to reproduce first-order changes in the atmospheric CO2 content for the first time in recent years (Ganopolski and Brovkin, 2017; Khatiwala et al., 2019). The most important processes accounting for the full deglacial CO2 amplitude in the models include solubility changes, changes in oceanic circulation and marine carbonate chemistry. The effect of the terrestrial carbon cycle, variable volcanic outgassing and the temperature dependence on the oceanic remineralization length scale contribute less than 15 ppm CO2 between the glacial and interglacial intervals of the cycles. However, details in the simulated response of the marine carbon cycle and atmospheric CO2 concentrations to changes in ocean circulation depend to a large degree on model parametrization (Gottschalk et al., 2019).

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In CMIP5 models run under RCP8.5, particulate organic carbon (POC) export flux is projected to decline by 1–12% by 2100 (Taucher and Oschlies, 2011; Laufkoetter et al., 2015). Similar values are predicted in 18 CMIP6 models, with declines of 2.5–21.5% (median –14%) or 0.2–2 GtC (median –0.8 GtC) between 1900 and 2100 under the SSP5-8.5 scenario. The mechanisms driving these changes vary widely between models due to differences in parametrization of particle formation, remineralization and plankton community structure.

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Most methods for quantifying robustness assume that only one realization from each model is applied. There are challenges that arise from having heterogeneous multi-model ensembles with many members for some models and single members for others (Olonscheck and Notz, 2017; Evin et al., 2019). Furthermore, the methods that map model robustness usually ignore that sharing parametrizations or entire components across coupled models can lead to substantial model interdependence (Fischer et al., 2011; Kharin et al., 2012; Knutti et al., 2013, 2017; Leduc et al., 2015; Sanderson et al., 2015, 2017; Annan and Hargreaves, 2017; Boé, 2018; Abramowitz et al., 2019). This may lead to a biased estimate of model agreement if a substantial fraction of models is interdependent. The methodologies and results in this literature since AR5 are higher in quality and clarity. However, quantifying and accounting for model dependence in a robust way remains challenging (Abramowitz et al., 2019). Furthermore, absence of significant mean change in a certain climate variable does not imply absence of substantial impact, because there may be substantial change in variability, which is typically not mapped (McSweeney and Jones, 2013).

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Weisheimer, A., S. Corti, T. Palmer, and F. Vitart, 2014: Addressing model error through atmospheric stochastic physical parametrizations: impact on the coupled ECMWF seasonal forecasting system. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 372(2018), 20130290, doi: 10.1098/rsta.2013.0290.

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The use of these data has enhanced our understanding of precipitation processes at regional scale (high confidence), such as diurnal cycles in a large river valley (H. Chen et al., 2012), and in coastal (Hassim et al., 2016; Yokoi et al., 2017) and mountainous regions (Hirose et al., 2017). Three-dimensional observations revealed the contrasts in regional characteristics of rainfall extremes in monsoon regions and continental dry regions (Sohn et al., 2013; Hamada and Takayabu, 2018). Satellite measurements are also used to evaluate climate model performance, as well as to develop new parametrizations. As a demonstration of the utility of these products in studying model bias, a subtropical cumulus congestus regime has been identified that may be implicated in the unrealistic double Inter-tropical Convergence Zone (ITCZ) found in some climate models (Takayabu et al., 2010; Hirota et al., 2011, 2014). Another example is a parametrization of a land surface model that was developed specifically for a certain soil type. By assimilating satellite brightness temperature observations with their LDAS-UT scheme, Yang et al. (2007) successfully optimized a land surface model for the Tibetan Plateau.

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Reanalyses incorporate an increasing volume of observations from a growing number of sources over time, which sometimes presents a difficulty for trend analysis. However, regional reanalyses are valuable for regional climate assessments, since they can employ high-resolution model simulations due to their limited spatial domain. Their accuracy is also better than global reanalyses since they are often developed over regions with a high density of observational data (sometimes not freely available for all regions) to be assimilated into the model (e.g., Yamada et al., 2012). Regional reanalyses can assimilate locally dense and high-frequency observations, such as from local observation networks (Mahmood et al., 2018; Su et al., 2019) and radar precipitation (Wahl et al., 2017) in addition to the observations assimilated by global reanalyses. In some regional reanalyses, satellite-derived high-resolution sea ice (Bromwich et al., 2016, 2018) and sea surface temperature (Su et al., 2019) are also applied as lower boundary conditions. The periods of regional reanalyses are limited by the availability of the observations for assimilation and by the global reanalyses needed as lateral boundary conditions. Most regional reanalyses cover the past 10 to 30 years. There are also regional reanalysis activities that use conventional observations only, which produce consistent datasets over 60 years to capture precipitation trends, extremes and changes (Fukui et al., 2018). Existing regional reanalyses cover North America (Mesinger et al., 2006), Europe (Dahlgren et al., 2016; Jermey and Renshaw, 2016; Kaspar et al., 2020), the Arctic (Bromwich et al., 2016, 2018), South Asia (Mahmood et al., 2018), and Australia (Su et al., 2019). A project for regional reanalysis covering Japan has also started (Fukui et al., 2018), where grid spacing is between 5 and 32 km, although cumulus parametrizations are still needed to compute sub-grid scale cumulus convection. Recently, reanalyses using convection-permitting regional models have been published (e.g., Wahl et al., 2017, for central Europe).

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The regional reanalyses represent the frequencies of extremes and the distributions of precipitation, surface air temperature, and surface wind better than global reanalyses (high confidence). This is due to the use of high-resolution regional climate models (RCMs), as indicated by different regional climate modelling studies (Mesinger et al., 2006; Bollmeyer et al., 2015; Bromwich et al., 2016, 2018; Dahlgren et al., 2016; Jermey and Renshaw, 2016; Fukui et al., 2018; Su et al., 2019). Regional reanalyses, however, retain uncertainties due to deficiencies in the physical parametrization used in RCMs and by the use of relatively simple data assimilation algorithms (Bromwich et al., 2016; Jermey and Renshaw, 2016; Su et al., 2019). Regional reanalyses can provide estimates that are more consistent with observations than dynamical downscaling approaches, due to the assimilation of additional local observations (high confidence) (Bollmeyer et al., 2015; Fukui et al., 2018).

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The Coordinated Regional Climate Downscaling Experiment (CORDEX) initiative (Giorgi et al., 2009; Giorgi and Gutowski, 2015; Gutowski Jr. et al., 2016) provides ensembles of high-resolution historical (starting as early as 1950) and future climate projections for various regions. RCMs in CORDEX typically have a horizontal resolution between 10 and 50 km. But much finer spatial resolution is required to fully resolve deep convection, an important cause of precipitation in much of the world. Therefore, an emerging strand in dynamical downscaling employs simulations at convection permitting scales, at horizontal resolutions of a few kilometres, where deep-convection parametrizations can be switched off, approximately simulating deep convection (Prein et al., 2015; Stratton et al., 2018; Coppola et al., 2020). A recent study indicates that switching off the deep-convection parametrization may be beneficial also in simulations performed at coarser resolutions (Vergara-Temprado et al., 2020). Alternatively, some RCMs make use of scale-aware parametrizations that are able to adapt to increasing resolution without switching off the convection scheme (Hamdi et al., 2012; De Troch et al., 2013; Plant and Yano, 2015; Giot et al., 2016; Termonia et al., 2018; Yano et al., 2018).

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New diagnostics for multivariate dependencies are needed to characterize compound events (Section 11.8; Hobaek Haff et al., 2015; Wahl et al., 2015; Sippel et al., 2016, 2017; Tencer et al., 2016; Bevacqua et al., 2017; Careto et al., 2018; Zscheischler et al., 2018). However, their success depends on the availability of adequate observational data (Section 10.2.2). Multivariate dependencies discovered in compound events can also be used for designing and evaluating multivariate bias adjustment and statistical downscaling. Process-based diagnostics are useful for identifying the cause of model errors, although it is not always possible to associate a systematic error with a specific cause (Eyring et al., 2019). AR5 discussed two approaches of process-based evaluation: (i) the isolation of physical components or parametrizations by dedicated experiments (Section 10.3.2.4) and (ii) diagnostics conditioned on relevant regimes, usually synoptic-scale weather patterns. The regime-based approach has been used with both global models (e.g., Barton et al., 2012; Catto et al., 2015; Taylor et al., 2019) and RCMs (Endris et al., 2016; Bukovsky et al., 2017; Whan and Zwiers, 2017; Pinto et al., 2018), but also with perfect prognosis and bias adjustment methods (Marteau et al., 2015; Addor et al., 2016; Beranová and Kyselý, 2016; Soares and Cardoso, 2018; Soares et al., 2019b).

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Convection is the process of vertical mixing due to atmospheric instability. Deep moist convection is associated with thunderstorms and severe weather such as heavy precipitation and strong wind gusts. Convection may occur in single locations, in spatially extended severe events such as supercells, and organized into larger mesoscale convective systems such as squall lines or tropical cyclones, and embedded in fronts (see below). Shallow and deep convection are not explicitly simulated but parametrized in standard global and regional models. In consequence, these models suffer from several biases. AR5 has stated that many CMIP3 and CMIP5 models simulate the peak in the diurnal cycle of precipitation too early, but increasing resolution and better parametrizations help to mitigate this problem (Flato et al., 2014). Similar issues arise for RCMs with parametrized deep convection (Prein et al., 2015), which also tend to overestimate high cloud cover (Langhans et al., 2013; Keller et al., 2016).

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The special class of high-resolution global models (Sections 1.5.3.1 and 10.3.3.1, Chapter 3; Haarsma et al., 2016; Prodhomme et al., 2016) is expected to improve some of the regional processes that are not appropriately represented in standard global models (Roberts et al., 2018). There is general consensus that increasing global model resolution improves some long-standing biases (Chapter 3, Section 10.3.3.3, and Figures 10.6 and 10.7; Demory et al., 2014, 2020; Schiemann et al., 2014; Dawson and Palmer, 2015; van Haren et al., 2015; Feng et al., 2017; Fabiano et al., 2020), although the resolution increase is not a guarantee of overall improvement (Section 8.5.1; Fabiano et al., 2020; Hertwig et al., 2021). For instance, increasing resolution in global models has been shown to improve Asian monsoon rainfall anchored to orography and the monsoon circulation (Johnson et al., 2016), but fails to solve the major dry bias. It is also difficult to disentangle the role of resolution increase and model tuning on the performance of the GCM (Anand et al., 2018). Some efforts have been undertaken to complement the performance improvements of resolution by using stochastic parametrizations (Palmer, 2019), which explicitly acknowledge the multi-scale nature of the climate system, in standard resolution global models with some success (Dawson and Palmer, 2015; MacLeod et al., 2016; Zanna et al., 2017, 2019). The expectation is to achieve a similar performance to the increase in resolution at a reduced computational cost.

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Despite their known errors that affect model performance, there is high confidence that global models provide useful information for the production of regional climate information. There is robust evidence and high agreement that the increase of global model resolution helps in reducing the biases limiting performance at the regional scale, although resolution per se does not automatically solve all performance limitations shown by global models. There is robust evidence that stochastic parametrizations can help to improve some aspects of the global model performance that are relevant to regional climate information.

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An important consideration is which set of global models should be used for global model–RCM combinations. If adequate resources exist, then large numbers of global model–RCM combinations are possible (Déqué et al., 2012; Coppola et al., 2021; Vautard et al., 2021). However, coordinated downscaling programmes can be limited by the human and computational resources available, for producing ensembles of downscaled output, which limits the number of feasible global model–RCM combinations. With this limitation in mind, a small set of GCMs may be chosen that span the range of equilibrium climate sensitivity in available global models (e.g., Mearns et al., 2012, 2013; Inatsu et al., 2015), though this range may be inconsistent with the likely range (Chapter 4), or some other relevant measure of sensitivity, such as the projected range of tropical SSTs (Suzuki-Parker et al., 2018). A further choice is to emphasize models that do not have the same origins or that do not use similar parametrizations and thus might be viewed as independent, a criterion that could be applied to both global models (Chapter 4) and RCMs (Evans et al., 2014). Global models and RCMs could also be discarded that unrealistically represent processes controlling the regional climate of interest (McSweeney et al., 2015; Maraun et al., 2017; Bukovsky et al., 2019; Eyring et al., 2019). Box 4.1 offers a more detailed discussion of the issues surrounding these approaches. Finally, global models may be selected to represent different physically self-consistent changes in regional climate (Zappa and Shepherd, 2017). Statistical methods can provide estimates of outcomes from missing global model–RCM combinations in a large matrix (Déqué et al., 2012; Heinrich et al., 2014; Evin et al., 2019).

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Global (McCarthy et al., 2010; Oleson et al., 2011; Zhang et al., 2013; H. Chen et al., 2016; Katzfey et al., 2020; Sharma et al., 2020; Hertwig et al., 2021) and regional modelling groups (Oleson et al., 2011; Kusaka et al., 2012a; McCarthy et al., 2012; Hamdi et al., 2014; Trusilova et al., 2016; Daniel et al., 2019; Halenka et al., 2019; Langendijk et al., 2019a) are beginning to implement these urban parametrizations within the land surface component of their models. There is very high confidence (robust evidence and high agreement) that while all types of urban parametrizations generally simulate radiation exchanges in a realistic way, they have strong biases when simulating latent heat fluxes, though recent research incorporating in-canyon vegetation processes improved their performance. There is medium confidence (medium evidence, high agreement) (Kusaka et al., 2012b; McCarthy et al., 2012; Hamdi et al., 2014; Trusilova et al., 2016; Jänicke et al., 2017; Daniel et al., 2019) that a simple single-layer parametrization, is sufficient for the correct simulation of the urban heat island magnitude and its interplay with regional climate change.

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Zanna, L., P.G.L. Porta Mana, J. Anstey, T. David, and T. Bolton, 2017: Scale-aware deterministic and stochastic parametrizations of eddy–mean flow interaction. Ocean Modelling, 111, 66–80, doi: 10.1016/j.ocemod.2017.01.004.

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Extreme precipitation can also be enhanced by dynamic responses and feedbacks occurring within storms that result from the extra latent heat released from the thermodynamic increases in moisture(Lackmann, 2013; Willisonet al. , 2013; Marcianoet al. , 2015; Nieet al. , 2018; Mizuta and Endo, 2020). The extra latent heat released within storms has been shown to increase precipitation extremes by strengthening convective updrafts and the intensity of the cyclonic circulation (e.g., Molnar et al., 2015; Nie et al., 2018), although weakening effects have also been found in mid-latitude cyclones (e.g., Kirshbaum et al., 2017). Additionally, the increase in latent heat can also suppress convection at larger scales due to atmospheric stabilization (Nie et al., 2018; Tandon et al., 2018; Kendon et al., 2019). As these dynamic effects result from feedback processes within storms where convective processes are crucial, their proper representation might require improving the horizontal/vertical resolution, the formulation of parametrizations, or both, in current climate models (i.e., Kendon et al. , 2014; Westra et al. , 2014; Ban et al. , 2015; Meredith et al. , 2015; Prein et al. , 2015; Nie et al., 2018).

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Evaluating climate model competence in simulating heavy precipitation extremes is challenging due to a number of factors, including the lack of reliable observations and the spatial scale mismatch between simulated andobserved data (Avila et al., 2015; Alexander et al., 2019). Simulated precipitation represents areal means, but station-based observations are conducted at point locations and are often sparse. The areal-reduction factor, the ratio between pointwise station estimates of extreme precipitation and extremes of the areal mean, can be as large as 130% at CMIP6 resolutions (about 100 km) (Gervais et al., 2014). Hence, the order in which gridded station based extreme values are constructed (i.e., if the extreme values are extracted at the station first and then gridded, or if the daily station values are gridded and then the extreme values are extracted) represents different spatial scales of extreme precipitation and needs to be taken into account in model evaluation (Wehner et al. 2020). This aspect has been considered in some studies. Reanalysis products are used in place of station observations for their spatial completeness as well as spatial-scale comparability(Sillmann et al., 2013a; Kim et al., 2020; Li et al., 2021). However, reanalyses share similar parametrizations to the models themselves, reducing the objectivity of the comparison.

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Different generations of CMIP models have improved over time, though quite modestly (Flato et al., 2013; Watterson et al., 2014). Improvements in the representation of the magnitude of the Expert Team on Climate Change Detection and Indices (ETCCDI) in CMIP5 over CMIP3(Sillmann et al., 2013a; Chen and Sun, 2015a) have been attributed to higher resolution, as higher-resolution models represent smaller areas at individual grid boxes. Additionally, the spatial distribution of extreme rainfall simulated by high-resolution models is generally more comparable to observations (Sillmann et al., 2013b; Kusunoki, 2017, 2018b; Scher et al., 2017) as these models tend to produce more realistic storms compared to coarser models (Section 11.7.2). Higher horizontal resolution alone improves simulation of extreme precipitation in some models (Wehner et al., 2014; Kusunoki, 2017, 2018b), but this is insufficient in other models (Bador et al., 2020) as parametrization also plays a significant role (M. Wu et al., 2020). A simple comparison of climatology may not fully reflect the improvements of the new models that have more comprehensive process formulations (Di Luca et al., 2015). Dittus et al. (2016) found that many of the eight CMIP5 models they evaluated reproduced the observed increase in the difference between areas experiencing an extreme high (90%) and an extreme low (10%) proportion of the annual total precipitation from heavy precipitation (R95p/PRCPTOT) for Northern Hemisphere regions. Additionally, CMIP5 models reproduced the relation between changes in extreme and non-extreme precipitation: an increase in extreme precipitation is at the cost of a decrease in non-extreme precipitation (Thackeray et al., 2018), a characteristic found in the observational record (Gu and Adler, 2018).

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Studies using regional climate models (RCMs), for example, CORDEX (Giorgi et al., 2009) over Africa (Dosio et al., 2015; Klutse et al., 2016; Pinto et al., 2016; Gibba et al., 2019), Australia, East Asia (Park et al., 2016), Europe (Prein et al., 2016a; Fantini et al., 2018), and parts of North America (Diaconescu et al., 2018) suggest that extreme rainfall events are better captured in RCMs compared to their host GCMs due to their ability to address regional characteristics, for example, topography and coastlines. However, CORDEX simulations do not show good skill over South Asia for heavy precipitation, and do not add value with respect to their GCM source of boundary conditions (Mishra et al., 2014b; S. Singh et al., 2017). The evaluation of models in simulating regional processes is discussed in detail in Section 10.3.3.4. The high-resolution simulation of mid-latitude winter extreme precipitation over land is of similar magnitude to point observations. Simulation of summer extreme precipitation has a large bias when compared with observations at the same spatial scale. Simulated extreme precipitation in the tropics also appears to be too large, indicating possible deficiencies in the parametrization of cumulus convection at this resolution. Indeed, precipitation distributions at both daily and sub-daily time scales are much improved with a convection-permitting model (Belušić et al., 2020) over Western Africa (Berthou et al., 2019b), East Africa (Finney et al., 2019), North America and Canada (Cannon and Innocenti, 2019; Innocenti et al., 2019) and over Belgium in Europe (Vanden Broucke et al., 2019).

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Confidence in the projection of intense TCs, such as those of Category 4–5, generally becomes higher as the resolution of the models becomes higher. The Coupled Model Intercomparison Project Phases 5 and 6 (CMIP5/6) class climate models (around 100–200 km grid spacing) cannot simulate TCs of Category 4–5 intensity. They do simulate storms of relatively high vorticity that are at best described as ‘TC-like’, but metrics such as storm counts are highly dependent on tracking algorithms (Camargo, 2013; Wehner et al., 2015; Zarzycki and Ullrich, 2017; Roberts et al., 2020a). High-resolution GCMs (around 10–60 km grid spacing), as used in HighResMIP (Haarsma et al., 2016; Roberts et al., 2020a), begin to capture some structures of TCs more realistically, as well as produce intense TCs of Category 4–5 despite the effects of parametrized deep cumulus convection processes (Murakami et al., 2015; Wehner et al., 2015; Yamada et al., 2017; Roberts et al., 2018; Moon et al., 2020). Convection-permitting models (around 1–10 km grid-spacing), such as used in some dynamical downscaling studies, provide further realism with capturing TC eye-wall structures (Tsuboki et al., 2015). Model characteristics besides resolution, especially details of convective parametrization, can influence a model’s ability to simulate intense TCs (Reed and Jablonowski, 2011; Zhao et al., 2012; He and Posselt, 2015; D. Kim et al., 2018; Zhang and Wang, 2018; Camargo et al., 2020). However, models’ dynamical cores and other physics also affect simulated TC properties (Reed et al., 2015; Vidale et al., 2021). Both wide-area regional and global convection-permitting models without the need for parameterized convection are becoming more useful for TC regional model projection studies (Tsuboki et al., 2015; Kanada et al., 2017a; Gutmann et al., 2018) and global model projection studies (Satoh et al., 2015, 2017; Yamada et al., 2017), as they capture more realistic TC eye wall structures (Kinter III et al., 2013) and are becoming more useful for investigating changes in TC structures (Kanada et al., 2013; Yamada et al., 2017). Large ensemble simulations of GCMs with 60 km grid spacing provide TC statistics that allow more reliable detection of changes in the projections, which are not well captured in any single experiment (Yoshida et al., 2017; Yamaguchi et al., 2020). Variable resolution global models offer an alternative to regional models for individual TC or basin-wide simulations (Yanase et al., 2012; Zarzycki et al., 2014; Harris et al., 2016; Reed et al., 2020; Stansfield et al., 2020). Computationally less intense than equivalent uniform resolution global models, they also do not require lateral boundary conditions, thus reducing this source of error (Hashimoto et al., 2016). Confidence in the projection of TC statistics and properties is increased by the use of higher-resolution models with more realistic simulations.

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Analysis of CMIP5 models suggests that atmospheric evaporative demand will increase over most areas of the world in high-emissions scenarios (virtually certain), mostly as a consequence of an increase in vapour pressure deficit (Scheff and Frierson, 2014, 2015; Greve and Seneviratne, 2015; Vicente-Serrano et al., 2020). CMIP5 models also project an increase in evapotranspiration over most land areas (medium confidence) (Laîné et al., 2014). However, regional changes in evapotranspiration can also be influenced by changes in soil moisture and vegetation, which modulate the moisture flux from the land to the atmosphere. Several studies of CMIP5 projections suggest that increases in plant water use efficiency will limit or counteract rising evapotranspiration (Milly and Dunne, 2016; Swann et al. , 2016; Lemordant et al. , 2018; Y. Yang et al. , 2018). However, other studies have found that transpiration increases due to the impact of climate change on growing season length, leaf area, and evaporative demand (Section 8.2.3.3; Frank et al. , 2015; Mankin et al. , 2017, 2018, 2019; Guerrieri et al. , 2019; S. Zhou et al. , 2019; Vicente-Serrano et al. , 2020). The parametrizations accounting for these complex physiological processes in global climate models may also be insufficient (Franks et al., 2017; Peters et al., 2018; Peano et al., 2019). Thus, there is currentlylow confidence in the role of vegetation physiology in modulating future projections of evapotranspiration.

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Moist convection is fundamental to the water cycle through its vertical transport of momentum, heat, and moisture across the atmosphere. It is particularly active in the tropics where it contributes to more than half of annual precipitation and to the development of severe weather events. Given limitations in computing resources, the current-generation GCMs cannot yet represent small-scale cloud processes and consequently shallow and deep convection is determined by sub-grid-scale parametrizations. While such parametrizations can be evaluated against field observations (e.g., Abdel-Lathif et al., 2018), it remains challenging to estimate convective entrainment that is valid for both shallow and deep convection (G.J. Zhang et al., 2016). Comparisons between regional projections with explicit compared with parametrized convection also highlight the limitations of parametrized convection for assessing climate change (Kendon et al., 2019; Jackson et al., 2020).

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Since AR5, spatial aggregation of tropical convection has also received growing attention in both observational (Holloway et al., 2017) and modelling studies (Muller and Bony, 2015; Wing et al., 2017; Tan et al., 2018). The changing degree of convective organization was highlighted as a key mechanism for dynamic changes in extreme precipitation (Pendergrass, 2020a). Yet, convective parametrizations do not represent all aspects of mesoscale convective systems (Hourdin et al., 2013; Park et al., 2019). This is related to the complexity of mechanisms involved from synoptic to mesoscale dynamics, which are only partially resolved by models. Cloud-resolving models (CRMs, Section 8.5.1.2.2) represent a useful benchmark for improving the parametrization of mesoscale convective systems. Machine learning can also be used to parametrize moist convection after training the model with a conventional or a super parametrization scheme (Gentine et al., 2018; O’Gorman and Dwyer, 2018), but has not yet been used in the CMIP framework.

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While some global modelling centres have reported progress in their parametrization of convection and in their simulation of seasonal, daily and sub-daily precipitation (e.g., Danabasoglu et al., 2020; Roehrig et al., 2020), CMIP6 models as a whole only show limited improvements in their simulation of the tropical precipitation climatology compared to CMIP5 (Figure 3.10; Fiedler et al., 2020). For instance, the double-ITCZ syndrome is still prominent (Tian and Dong, 2020) despite being reduced in some models (e.g., Qin and Lin, 2018). This systematic bias was shown to arise from atmospheric processes including cloud feedbacks (Tian, 2015; Dixit et al., 2018; Talib et al., 2018) and the SST threshold at which deep convection occurs in the tropics (Oueslati and Bellon, 2015; Xiang et al., 2017; Adam et al., 2018). Such biases can also arise from a too weak sensitivity of seasonal tropical precipitation to local SSTs compared with observations (Good et al., 2021). These biases are large enough to alter forced precipitation changes, and consequently limit our confidence in projected precipitation changes (Samanta et al., 2019; Aadhar and Mishra, 2020). Observational constraints can be used to narrow model response uncertainties (DeAngelis et al., 2015; G. Li et al., 2017; Ham et al., 2018; Watanabe et al., 2018), although there is still no consensus that model selection or weighting is a reliable alternative to the ‘one-model-one-vote’ approach used in Section 8.4 (Box 4.1). The detrimental influence of model errors can also be mitigated by focusing on phenomena or events (Polson and Hegerl, 2017; Weller et al., 2017), implementing bias adjustment techniques (Section 10.2.3.2), or adopting a non-probabilistic storyline approach (Zappa and Shepherd, 2017).

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In summary, since AR5 empirical convective parametrization schemes and associated precipitation biases have improved in some but not all global climate models. There is still low confidence in their ability to accurately simulate the spatio-temporal features of present-day precipitation, especially in the tropics where a double-ITCZ bias is still apparent in many models. While such biases limit the reliability of precipitation projections in some cases, there is currently only medium confidence that model selection or weighting is a better alternative to the one-model-one-vote approach (Box 4.1). Improved water cycle projections can be achieved by focusing on phenomena or weather events, such as a thermodynamic intensification of convective events (high confidence, Section 8.2.2.1), however accurate quantitative estimates are currently hampered by complex, model-dependent dynamical responses (Section 8.2.2.2).

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Couvreux, F. et al., 2015: Representation of daytime moist convection over the semi-arid Tropics by parametrizations used in climate and meteorological models. Quarterly Journal of the Royal Meteorological Society, 141(691), 2220–2236, doi: 10.1002/qj.2517.

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A key approach in climate science is the comparison of results from multiple model simulations with each other and against observations. These simulations have typically been performed by separate models with consistent boundary conditions and prescribed emissions or radiative forcings, as in the Coupled Model Intercomparison Project phases (CMIP, Meehl et al., 2000, 2007a; Taylor et al., 2012; Eyring et al., 2016). Such multi-model ensembles (MMEs) have proven highly useful in sampling and quantifying model uncertainty, within and between generations of climate models. They also reduce the influence on projections of the particular sets of parametrizations and physical components simulated by individual models. The primary usage of MMEs is to provide a well-quantified model range, but when used carefully they can also increase confidence in projections (Knutti et al., 2010). Presently, however, many models also share provenance (Masson and Knutti, 2011) and may have common biases that should be acknowledged when presenting and building on MME-derived conclusions (Section 1.5.4.6; Boé, 2018; Abramowitz et al., 2019).

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This section assesses our current understanding of SLCF emissions by natural systems. Many naturally occurring emission processes in the Earth system have been perturbed by the growing influence of human activities either directly (e.g., deforestation, agriculture) or via human-induced atmospheric CO2 increase and climate change, and therefore cannot be considered as purely natural emissions. The temporal evolution and spatial distribution of natural SLCF emissions are highly variable and their estimates rely on models with rather uncertain parametrizations for production mechanisms. For some SLCFs, the natural processes by which emissions occur are also not well understood. In the following sections, we assess the level of confidence in present-day SLCF emissions by natural systems, in their perturbation since the pre-industrial period and their sensitivity to future changes. When available, the assessment also includes estimates from the CMIP6 model ensemble. Note that volcanic SO2 emissions are discussed in Section 2.2.2 and natural sources of methane and N2O are assessed in Sections 5.2.2 and 5.2.3.

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Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP) models in CMIP5 used a range of LNOx4 between 1.2–9.7 TgN y−1 (Lamarque et al., 2013b). In CMIP6, the corresponding LNOx range is between 3.2–7.6 TgN y−1 (Griffiths et al., 2021). All CMIP6 models (as well as most models included in CMIP5, Young et al., 2013) apply a parametrization that relates cloud-top height to lightning intensity (Price and Rind, 1992), projecting an increase in LNOx in a warmer world in the range of 0.27–0.61 TgN yr−1°C–1 (Thornhill et al., 2021a). However, models using parametrizations based on convection (Grewe et al., 2001), updraft mass flux (Allen and Pickering, 2002) or ice flux (Finney et al., 2016a) show either much less sensitivity or a negative response (Finney et al., 2016b, 2018; Clark et al., 2017).

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In summary, the total present-day global lightning NOx emissions are still estimated to be within a factor of two. There is high confidence that LNOx are perturbed by climate change; however, there is low confidence in the sign of the change due to fundamental uncertainties in parametrizations.

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Most CMIP6 models use overly simplistic parametrizations and project an increase in global BVOC emissions in response to warming temperatures (Turnock et al., 2020). This good agreement actually reflects the lack of diversity in BVOC-emissions parametrizations in global models that do not fully account for the complex processes influencing emissions that are discussed above.

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BC and OA lifetimes are estimated to be 5.5 days ± 35% and 6.0 days ± 29% (median ± 1 standard deviation), respectively, based on an ensemble of 14 models (Gliß et al., 2021). Disagreement in simulated lifetime leads to horizontal and vertical variations in predicted carbonaceous aerosol concentrations, with implications for radiative forcing (Samset et al., 2013; Lund et al., 2018b). Airborne campaigns have provided valuable vertical-profile measurements of carbonaceous aerosol concentrations (Schwarz et al. , 2013; Freney et al. , 2018; Hodgson et al. , 2018; Schulz et al. , 2019; D. Zhao et al. , 2019; Morgan et al. , 2020). Compared to those measurements, models tend to transport BC too high in the atmosphere, suggesting that lifetimes are not larger than 5.5 days (Samset et al., 2013; Lund et al., 2018b). Newly developed size-dependent wet-scavenging parametrization for BC (Taylor et al., 2014; Schroder et al., 2015; Ohata et al., 2016; G. Zhang et al., 2017; Ding et al., 2019; Moteki et al., 2019; Motos et al., 2019) may lead to decreased BC lifetimes and improve agreement with observed vertical profiles.

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Simulated OA burdens also show a large spread among global models, with Gliß et al. (2021) reporting a multi-model median of 1.91 ± 0.65 Tg for the year 2010. The large spread reflects the wide range in the complexity of the OA parametrizations, particularly for SOA formation, as well as in the primary OA emissions (Tsigaridis et al., 2014; Gliß et al., 2021). The uncertainties are particularly large in model estimates of SOA production rates, which vary between 10 and 143 Tg yr–1 (Tsigaridis et al., 2014; Hodzic et al., 2016; Tilmes et al., 2019). While the level of complexity in the representation of OA in global models has increased since AR5 (Shrivastava et al., 2017; Hodzic et al., 2020), limitations in process-level understanding of the formation, ageing and removal of organic compounds lead to uncertainties in the global model predictions of global OA burden and distribution as well as the relative contribution of POA and SOA to OA. Jo et al. (2016) estimated that BrC contributes about 20% of total OA burden. That would give BrC a burden similar to that of BC (low confidence), enhancing the overall forcing exerted by carbonaceous aerosol absorption (Zhang et al., 2020).

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Quantifying the effects of shipping on climate is particularly challenging because (i) the sulphate cooling impact is dominated by aerosol–cloud interactions and (ii) ship emissions contain NOx, SOx and BC, which lead to mixed particles. Previous estimates of the sulphate radiative effects from present-day shipping span the range –47 to –8 mW m–2 (direct radiative effect) and –600 to –38 mW m–2 (indirect radiative effects) (Lauer et al. , 2007; Balkanski et al. , 2010; Eyring et al. , 2010; Lund et al. , 2012). Partanen et al. (2013) reported a global mean ERF for year-2010 shipping aerosol emissions of –390 mW m–2. The temperature change has been shown to be highly sensitive to the choice of aerosol–cloud parametrization (Lund et al., 2012). One year of global present-day shipping emissions, not considering the impact of recent low sulphur fuel regulation (IMO, 2016), are estimated to cause net cooling in the near term (–0.0024°C ± 0.0025°C) and slight warming (+0.00033°C ± 0.00015°C) on a 100-year horizon (Lund et al., 2020).

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Gettelman et al. (2021) extended Forster et al.’s (2020) results using two ESMs, and found a spring peak aerosol-induced ERF ranging from 0.12 to 0.3 W m–2, depending on the aerosol parametrization. They also estimated an ERF of –0.04 W m–2 from loss of contrail warming. Overall, they report a peak ERF of 0.04 to 0.2 W m–2, and a subsequent decline to around half the peak value. Two independent ESM studies Weber et al. (2020) and Yang et al. (2020) found consistent results in time evolution and component contributions but included fewer forcing components.

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ESM estimates of future concentrations of various SLCFs vary considerably even when using the same future emissions scenarios, which is related to sources of model structural uncertainty in the several physical, chemical and natural emissions model parametrizations. The general uncertainties in understanding and representing chemical and physical processes governing the life cycle of SLCFs (Box 6.1) necessarily also applies to simulations of future concentrations and ERF. In addition, how the models are able to simulate climate changes (i.e., circulation and precipitation) that affect the dispersion and removal of SLCFs constitute a structural uncertainty in the models. Also SLCF-related climate feedbacks (e.g., NOx from lightning or BVOCs from vegetation; Section 6.4.5) add to the uncertainty.

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The radiation components of the surface energy budget are associated with substantially larger uncertainties than at the TOA, since they are less directly measured by passive satellite sensors and require retrieval algorithms and ancillary data for their estimation (Raschke et al., 2016; Kato et al., 2018; Huang et al., 2019). Confidence in the quantification of the global mean surface radiation components has increased recently, as independent estimates now converge to within a few W m–2(Wild, 2017). Current best estimates for downward solar and thermal radiation at Earth’s surface are approximately 185 W m–2 and 342 W m–2, respectively (Figure 7.2). These estimates are based on complementary approaches that make use of satellite products from active and passive sensors (L’Ecuyer et al., 2015; Kato et al., 2018) and information from surface observations and Earth system models (ESMs; Wild et al., 2015). Inconsistencies in the quantification of the global mean energy and water budgets discussed in AR5 (Hartmann et al., 2013) have been reconciled within the (considerable) uncertainty ranges of their individual components (Wild et al., 2013, 2015; L’Ecuyer et al., 2015). However, on regional scales, the closure of the surface energy budgets remains a challenge with satellite-derived datasets (Loeb et al., 2014; L’Ecuyer et al., 2015; Kato et al., 2016). Nevertheless, attempts have been made to derive surface energy budgets over land and ocean (Wild et al., 2015), over the Arctic (Christensen et al., 2016b), and over individual continents and ocean basins (L’Ecuyer et al., 2015; Thomas et al., 2020). Since AR5, the quantification of the uncertainties in surface energy flux datasets has improved. Uncertainties in global monthly mean downward solar and thermal fluxes in the CERES-EBAF surface dataset are, respectively, 10 W m–2 and 8 W m–2(converted to 5–95% ranges; Kato et al., 2018). The uncertainty in the surface fluxes for polar regions is larger than in other regions (Kato et al., 2018) due to the limited number of surface sites and larger uncertainty in surface observations (Previdi et al., 2015). The uncertainties in ocean mean latent and sensible heat fluxes are approximately 11 W m–2 and 5 W m–2(converted to 5–95% ranges), respectively (L’Ecuyer et al., 2015). A recent review of the latent and sensible heat flux accuracies over the period 2000–2007 highlights significant differences between several gridded products over ocean, where root-mean-squared differences between the multi-product ensemble and data at more than 200 moorings reached up to 25 W m–2 for latent heat and 5 W m–2 for sensible heat (Bentamy et al., 2017). This uncertainty stems from the retrieval of flux-relevant meteorological variables, as well as from differences in the flux parametrizations (Yu, 2019). Estimating the uncertainty in sensible and latent heat fluxes over land is difficult because of the large temporal and spatial variability. The flux values over land computed with three global datasets vary by 10–20% (L’Ecuyer et al., 2015).

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In a first attempt to systematically evaluate equilibrium climate sensitivity (ECS) based on fully coupled general circulation models (GCMs) in AR4, diverging cloud feedbacks were recognized as a dominant source of uncertainty. An advance in understanding the cloud feedback was to assess feedbacks separately for different cloud regimes (Gettelman and Sherwood, 2016). A thorough assessment of cloud feedbacks in different cloud regimes was carried out in AR5 (Boucher et al., 2013), which assigned high or medium confidence for some cloud feedbacks butlow or no confidence for others (Table 7.9). Many studies that estimate the net cloud feedback using CMIP5 simulations (Vial et al., 2013; Caldwell et al., 2016; Zelinka et al., 2016; Colman and Hanson, 2017) show different values depending on the methodology and the set of models used, but often report a large inter-model spread of the feedback, with the 90% confidence interval spanning both weak negative and strong positive net feedbacks. Part of this diversity arises from the dependence of the model cloud feedbacks on the parametrization of clouds and their coupling to other sub-grid-scale processes (Zhao et al., 2015).

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Despite the reduction of anvil cloud amount supported by several lines of evidence, estimates of radiative feedback due to high-cloud amount changes is highly uncertain in models. The assessment presented here is guided by combined analyses of TOA radiation and cloud fluctuations at interannual time scale using multiple satellite datasets. The observationally based local cloud amount feedback associated with optically thick high-clouds is negative, leading to its global contribution (by multiplying the mean tropical anvil cloud fraction of about 8%) of –0.24 ± 0.05 W m–2°C–1(one standard deviation) for LW (Vaillant de Guélis et al., 2018). Also, there is a positive feedback due to increase of optically thin cirrus clouds in the tropopause layer, estimated to be 0.09 ± 0.09 W m–2°C–1(one standard deviation; Zhou et al., 2014). The negative LW feedback due to reduced amount of thick high-clouds is partly compensated by the positive SW feedback (due to less reflection of solar radiation), so that the tropical high-cloud amount feedback is assessed to be equal to or smaller than their sum. Consistently, the net high-cloud feedback in the tropical convective regime, including a part of the altitude feedback, is estimated to have the global contribution of –0.13 ± 0.06 W m–2°C–1(one standard deviation; Williams and Pierrehumbert, 2017). The negative cloud LW feedback is considerably biased in CMIP5 GCMs (Mauritsen and Stevens, 2015; Su et al., 2017; Li et al., 2019) and highly uncertain, primarily due to differences in the convective parametrization (Webb et al., 2015). Furthermore, high-resolution CRM simulations cannot alone be used to constrain uncertainty because the results depend on parametrized cloud microphysics and turbulence (Bretherton et al., 2014; Ohno et al., 2019). Therefore, the tropical high-cloud amount feedback is assessed as negative but with low confidence given the lack of modelling evidence. Taking observational estimates altogether and methodological uncertainty into account, the global contribution of the high-cloud amount feedback is assessed to be –0.15 ± 0.2 W m–2°C–1(one standard deviation).

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The ECS of a model is the net result of the model’s effective radiative forcing from a doubling of CO2 and the sum of the individual feedbacks and their interactions. It is well known that most of the model spread in ECS arises from cloud feedbacks, and particularly the response of low-level clouds (Bony and Dufresne, 2005; Zelinka et al., 2020). Since these clouds are small-scale and shallow, their representation in climate models is mostly determined by sub-grid-scale parametrizations. It is sometimes assumed that parametrization improvements will eventually lead to convergence in model response and therefore a decrease in the model spread of ECS. However, despite decades of model development, increases in model resolution and advances in parametrization schemes, there has been no systematic convergence in model estimates of ECS. In fact, the overall inter-model spread in ECS for CMIP6 is larger than for CMIP5; ECS and TCR values are given for CMIP6 models in Supplementary Material 7.SM.4 based on Schlund et al. (2020) for ECS and Meehl et al. (2020) for TCR (see also Figure 7.18 and FAQ 7.3). The upward shift does not apply to all models traceable to specific modelling centres, but a substantial subset of models have seen an increase in ECS between the two model generations. The increased ECS values, as discussed in (Section 7.4.2.8, are partly due to shortwave cloud feedbacks (Flynn and Mauritsen, 2020) and it appears that in some models extra-tropical clouds with mixed ice and liquid phases are central to the behaviour (Zelinka et al., 2020), probably borne out of a recent focus on biases in these types of clouds (McCoy et al., 2016; Tan et al., 2016). These biases have recently been reduced in many ESMs, guided by process understanding from laboratory experiments, field measurements and satellite observations (Lohmann and Neubauer, 2018; Bodas-Salcedo et al., 2019; Gettelman et al., 2019). However, this and other known model biases are already factored into the process-level assessment of cloud feedback (Section 7.4.2.4), and furthermore the emergent constraints used here focus on global surface temperature change and are therefore less susceptible to shared model biases in individual feedback parameters than emergent constraints that focus on specific physical processes (Section 7.5.4). The high values of ECS and TCR in some CMIP6 models lead to higher levels of surface warming than CMIP5 simulations and also the AR6 projections based on the assessed ranges of ECS, TCR and ERF (Box 4.1 and FAQ 7.3; Forster et al., 2020).

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It is generally difficult to determine which information enters the formulation and development of parametrizations used in ESMs. Climate models frequently share code components, and in some cases entire sub-model systems are shared and slightly modified. Therefore, models cannot be considered independent developments, but rather families of models with interdependencies (Knutti et al., 2013). It is therefore difficult to interpret the collection of models (Knutti, 2010), and it cannot be ruled out that there are common limitations and therefore systematic biases to model ensembles that are reflected in the distribution of ECS as derived from them. Although ESMs are typically well-documented, in ways that increasingly include information on critical decisions regarding tuning (Mauritsen et al., 2012; Hourdin et al., 2017; Schmidt et al., 2017a; Mauritsen and Roeckner, 2020), the full history of development decisions could involve both process-understanding and sometimes also other information such as historical warming. As outlying or poorly performing models emerge from the development process, they can become re-tuned, reconfigured or discarded and so might not see publication (Hourdin et al., 2017; Mauritsen and Roeckner, 2020). In the process of addressing such issues, modelling groups may, whether intentionally or not, modify the modelled ECS.

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In AR5 (Flato et al., 2013), a marginal improvement was noted in Coupled Model Intercomparison Project Phase 5 (CMIP5) climate model SST biases compared to Phase 3 (CMIP3) models in AR4, with a reduction in the magnitude of biases. The AR5 noted that, in several regions, large SST biases are symptomatic of errors in the representation of important processes, such as dynamics in the equatorial Pacific and North Atlantic, and Southern Ocean. Common regional biases in SST or historical SST trends are not exclusively linked to the representation of the ocean (high confidence), but can have multiple causes, including: errors in the representation of long-term historical trends in equatorial winds (Section 9.2.1.2); misrepresentation of the forced equatorial ocean response (Karnauskas et al., 2012; Kohyama et al., 2017; Coats and Karnauskas, 2018); thermocline depth errors (Linz et al., 2014); errors in atmospheric model cloud-related shortwave radiation (Hyder et al., 2018); biases in ocean circulation variability (C. Wang et al., 2014); and deficiencies in upper ocean (Q. Li et al., 2019) and atmospheric (Bates et al., 2012) boundary layer parametrizations. In CMIP6, the mid-latitude biases in the Northern Hemisphere are improved in the multi-model mean, and the inter-model standard deviation of the zonal mean SST error is significantly decreased in the northern Hemisphere south of 50°N compared to CMIP5, though biases in equatorial regions remain essentially unchanged (Section 3.5.1.1 and Figures 3.23, 3.24 and 9.3). Some long-standing ocean model biases have been reduced through increases in model resolution in CMIP6 (Bock et al., 2020) and improved parametrizations (Fox-Kemper et al., 2011; Q. Li et al., 2016; Qiao et al., 2016; Reichl and Hallberg, 2018). The High Resolution Model Intercomparison Project (HighResMIP) ensemble (Figure 9.3) has smaller cold biases in the North Atlantic and the tropical Pacific, and smaller warm biases in the upwelling regions off the western coasts of Africa, North and South America (Roberts et al., 2018, 2019; Caldwell et al., 2019; Docquier et al., 2019). In summary, CMIP6 models show persistent regional biases in representing the climatological SST state (very high confidence), but higher resolution reduces some biases, particularly in the North Atlantic and eastern boundary upwelling systems (Figure 9.3; high confidence).

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Air–sea flux biases result from common causes in most models, and many are the same as during AR5 (Rhein et al., 2013). Important currents (e.g., Gulf Stream, Kuroshio, Antarctic Circum-polar Current patterns) are often found in erroneous locations in models, affecting SST and flux signatures (Bates et al., 2012; Beadling et al., 2020; J.-L.F. Li et al., 2020), but their locations are improved in high-resolution ocean models (Chassignet et al., 2017, 2020; Hewitt et al., 2020), and high-resolution coupled models reduce the mean air–sea flux biases (Delworth et al., 2012; Sakamoto et al., 2012; Small et al., 2014; Haarsma et al., 2016; Caldwell et al., 2019; L.C Jackson et al., 2020). Oceanic variability stems either from internal chaotic variability or atmospheric forcing (Hasselmann, 1976; Sérazin et al., 2016, 2017). Large-scale variability in the ocean tends to follow atmospheric forcing in low-resolution models, while in high-resolution coupled models ocean variability drives atmospheric variability on small scales (Bishop et al., 2017; Small et al., 2019), allowing these high-resolution models to mimic the coupling with clouds, precipitation, and atmospheric and oceanic boundary layers apparent in observations (Chelton and Xie, 2010; Frenger et al., 2013). Even coarse-resolution models, such as the ocean and sea ice components used in CMIP6, show significant sensitivity in the mean and variability of SST and sea ice to modest changes in flux forcing (Tsujino et al., 2020). Finally, there is still considerable disagreement between different parametrizations of air–sea fluxes used in models and strong scatter in direct observations (Renault et al., 2016; Brodeau et al., 2017). In summary, there is very high confidence that air–sea heat flux and stress biases are reduced in coupled models with high ocean resolution over coarse-resolution models, although the effect on trends remain unclear.

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The SROCC assessed that upper-ocean stratification will continue to increase in the 21st century under increased radiative forcing (high confidence), due to increased surface temperature and high-latitude surface freshening (Bindoff et al., 2019). New climate model simulations concur with SROCC assessment of a future increase of the 0–200 m stratification under increased radiative forcing in all regions of the world ocean (Kwiatkowski et al., 2020). In addition, CMIP6 climate models project a shallowing of the mixed-layer in summer and winter by the end of the century under increased radiative forcing (Figure 9.5; Kwiatkowski et al., 2020), with the exception of the Arctic showing deepening of the mixed layer as a result of sea ice retreat (Figure 9.5; Lique et al., 2018). The regions of largest shallowing are associated with the deepest climatological mixed layer, in both winter and summer, particularly affecting the North Atlantic and the Southern Ocean basins (Figure 9.5). While CMIP6 models tend to project shallowing mixed layers under a warming climate, except at high latitudes (Figure 9.5; Lique et al., 2018; Kwiatkowski et al., 2020), a deepening in the summer mixed-layer depth by intensification of the surface winds and storms may explain inconsistency among models in many regions (Figure 9.5; Young and Ribal, 2019), although model mixed-layer biases are large in the summer in the Southern Ocean (Belcher et al., 2012; Sallée et al., 2013a; Q. Li et al., 2016; Tsujino et al., 2020). Lack of observed ocean turbulence and climate model limitations do not allow for direct assessment of ocean surface turbulence change and limit confidence in past and future mixed-layer change. Understanding of turbulent processes, their representation in ocean and climate models, and their effect on mixed-layer biases have been an active and rapidly evolving topic of research since AR5 (Buckingham et al., 2019; Q. Li et al., 2019). Small-scale mixed-layer processes are not resolved in climate models (D’Asaro, 2014; Buckingham et al., 2019; McWilliams, 2019) and despite significant improvements in their parametrization over the last decade (Fox-Kemper et al., 2011; Jochum et al., 2013; Q. Li et al., 2016, 2019; Qiao et al., 2016) and significant improvement in some models (Li and Fox-Kemper, 2017; Dunne et al., 2020), biases in mixed-layer representation generally persist (Heuzé, 2017; Williams et al., 2018; Cherchi et al., 2019; Golaz et al., 2019; Voldoire et al., 2019; Yukimoto et al., 2019; Boucher et al., 2020; Danabasoglu et al., 2020; Dunne et al., 2020; Kelley et al., 2020). In summary, the representation of upper-ocean stratification and mixed layers has improved in CMIP6 compared to CMIP5. While it is virtually certain that the global mean upper ocean will continue to stratify in the 21st century, there is only low confidence in the future evolution of mixed-layer depth, which is projected to mostly shoal under high emissions, except in high-latitude regions where sea ice retreats.

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For the upper cell overturning circulation, SROCC concluded that: its transport has experienced significant inter-decadal variability in response to wind forcing since the 1990s; and there is low confidence in the assessments of a long-term increase in upper-ocean overturning. Consistent with SROCC, the importance of eddy processes and winds in driving long-term change and variability have been reinforced, with a potential fast wind response partially counteracted by a slower eddy response (Doddridge et al., 2019; Waugh et al., 2019; Stewart et al., 2020). Eddy parametrizations affect the strength of overturning, its sensitivity to winds and the ACC transport (Mak et al., 2017). Even in eddy-resolving simulations, sub-gridscale dissipation affects the overturning and ACC (Pearson et al., 2017). In addition, there has been progress in understanding the importance of Antarctic Ice Shelf meltwater and sea ice, in driving the observed changes in the near surface and in the upper overturning cell over the past decades, on top of changes induced by winds and eddies (Bronselaer et al., 2020; Haumann et al., 2020; Jeong et al., 2020; Rye et al., 2020). In particular, increased stratification caused by increased freshwater flux to the surface ocean (Section 9.2.1.3) can cause a shoaling and warming of the CDW layer, and create a positive feedback, enhancing basal melt of the Antarctic Ice Sheet (Section 9.4.2.1; Bronselaer et al., 2018; Golledge et al., 2019; Schloesser et al., 2019; Sadai et al., 2020). There is medium confidence in the existence of this feedback mechanism but low agreement on the magnitude of the feedback. The SROCC reported that CMIP5 models project that the overall transport of upper-ocean overturning cell will increase by up to 20% in the 21st century, and no new studies alter that assessment.

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Under future scenarios RCP4.5 and RCP8.5, AR5 (Collins et al., 2013) assessed an intensification and poleward extension of the southern Hemisphere subtropical gyres in the 21st century. New evidence since AR5 further reinforces their conclusions, which are now extended to all subtropical gyre systems in the Northern and Southern hemispheres (Yang et al., 2016, 2020). CMIP6 models project changes in WBCs that are consistent with projected changes in the surface winds. Under strong radiative forcing, in scenario SSP5-8.5, CMIP6 models project that the East Australian Current Extension, Agulhas Current Extension and Brazil Current will intensify in the 21st century, while the Gulf Stream will weaken (Figure 9.11). Although CMIP5/CMIP6 are limited in resolution, medium confidence is given to changes in WBCs due to consistency across generations of climate models, including CMIP6, despite changes in model structure, resolution and parametrizations.

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The SROCC (Meredith et al., 2019) stated that there is low confidence in understanding coastal glacier response to ocean forcing because submarine melt rates, calving rates, bed and fjord geometry and the roles of ice mélange and subglacial discharge are poorly understood. Ice–ocean interactions remain poorly understood and difficult to model, with parametrizations often used for calving of marine-terminating glaciers (Mercenier et al., 2018) and submarine and plume-driven melt (Beckmann et al., 2019). Due to the difficulties of modelling the large number of marine-terminating glaciers and limited availability of high-resolution bedrock data, the majority of recent modelling work on Greenland outlet glaciers is focused on individual or a limited number of glaciers (Krug et al., 2014; Bondzio et al., 2016, 2017; Morlighem et al., 2016b; Muresan et al., 2016; Choi et al., 2017; Beckmann et al., 2019), or a specific region (Morlighem et al., 2019). Since SROCC, using a flowline model that includes calving and submarine melting, Beckmann et al. (2019) concluded that the AR5 upscaling of contributions from four of the largest glaciers (Nick et al., 2013) overestimated the total glacier contribution from the Greenland Ice Sheet, due to differences in response between large and small glaciers. The regional study of Morlighem et al. (2019) confirms that ice–ocean interactions have the potential to trigger extensive glacier retreat over decadal time scales, as indicated by observations (Section 9.4.1.1). One focus of continental ice-sheet models has been the improved treatment of marine-terminating glaciers via the inclusion of calving processes and freely moving calving fronts (Aschwanden et al., 2019; Choi et al., 2021). An improved bedrock topographic dataset (Morlighem et al., 2017) allows for ice discharge to be better captured for outlet glaciers in continental ice-sheet models, and simulations indicate that bedrock topography controls the magnitude and rate of retreat (Aschwanden et al., 2019; Rückamp et al., 2020). Overall, although there is high confidence that the dynamic response of Greenland outlet glaciers is controlled by bedrock topography, there is low confidence in quantification of future mass loss from Greenland triggered by warming ocean conditions, due to limitations in the current understanding of ice–ocean interactions, its implementation in ice-sheet models, and knowledge of bedrock topography.

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Improvements in the representation of grounding line evolution in ice-sheet models since AR5 (such as sub-grid schemes for basal friction and ice-shelf melt, and local grid refinement) means that most of the model simulations presented in SROCC were dominated by physical processes. Since then, these advances have been applied in several model intercomparison projects – such as ISMIP6 and LARMIP-2 (see Box 9.3); MISMIP+ (Cornford et al. 2020); and ABUMIP (Sun et al. 2020). All models participating in ISMIP6 and LARMIP-2 simulate ice-shelf and grounding-line evolution, and include sub-shelf melt parametrization, which was not the case in the Sea-level Response to Ice Sheet Evolution (SeaRISE) project intercomparison (Bindschadler et al., 2013; Nowicki et al., 2013). Simulations of grounding line evolution (Seroussi et al., 2017, 2020) have benefitted from improved bedrock topography (Morlighem et al., 2020). Treatment of sub-shelf melting, however, remains one of the causes of large differences in AIS models, particularly for partially floating grid cells in models with coarse resolution (Levermann et al., 2020; Edwards et al., 2021). Due to the limitations in resolving cavities in ocean models, as described above, basal melt rates are generally parameterized at the ice shelf base, based on ocean model simulations of temperatures and salinity instead (Nowicki et al., 2020b; Seroussi et al., 2020). While this has the advantage of connecting melt rates to emissions scenarios, a large variety of melt parametrizations exist (DeConto and Pollard, 2016; Lazeroms et al., 2018; Reese et al., 2018; Hoffman et al., 2019; Pelle et al., 2019; Jourdain et al., 2020), and there is low agreement due to limited observational constraints (ocean temperature, salinity, velocity, and ice shelf draft)(Jourdain et al., 2020), uncertainty in the physics of parametrized processes, missing processes (e.g., tides), and uncertainty in the treatment of ice-sheet–climate feedbacks (Donat-Magnin et al., 2017; Bronselaer et al., 2018; Golledge et al., 2019). Parametrizations are usually calibrated to present-day melt rates, but can respond differently to projected ocean warming (Favier et al., 2019; Jourdain et al., 2020). Two different calibrations were used in ISMIP6 (Box 9.3; Jourdain et al., 2020; Nowicki et al., 2020b): one reproducing melt rates averaged around the whole continent (MeanAnt: Figure 9.19), and the other reproducing melt rates near the grounding line of Pine Island Glacier (PIGL; see Figure 9.19), leading to large differences in melt rates. Evaluation with observations and two cavity-resolving models suggests that the MeanAnt parametrization better reproduces observed melt rates and projected increases in both the warm Amundsen Sea Embayment and cold Ronne-Filchner shelf cavity, as well as total Antarctic melting (Jourdain et al., 2020). The PIGL calibration represents the upper end for increased basal melt sensitivity that would be caused by continent-wide changes to ocean water properties and circulation under strong future forcing (Jourdain et al., 2020). The basal sliding law also has a strong influence on grounding line retreat and glacier acceleration in response to perturbations, and varies spatially (Sun et al., 2020). Sliding laws (Joughin et al., 2019) can only be constrained with observations in regions experiencing significant change, and with sufficiently long observational records.

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The AR5 assessed the median and likely (66–100% probability) sea level contributions of the AIS in 2100 relative to 1986–2005 to be 0.06 (–0.04 to +0.16) m SLE under RCP2.6 and 0.04 (–0.08 to +0.14) m SLE under RCP8.5 (Table 9.3; no change when using the AR6 baseline). The AR5 stated that only the collapse of the marine-based sectors of the AIS, if initiated, could cause GMSL to rise substantially above the likely range during the 21st century, with medium confidence that this would not exceed several tenths of a metre during this period. The assessment of the dynamical contribution had no dependence on emissions scenarios, due to the lack of literature, so the decrease in sea level contribution in the higher-emissions scenario was solely due to increased SMB (Section 9.4.2.3). The SROCC (Oppenheimer et al., 2019) assessed the total contribution based on five new ice-sheet modelling studies that incorporated marine ice-sheet dynamics, combining their estimates and interpreting the 5–95th percentile range of the resulting distribution as the likely range (17–83% probability interval, i.e., not open-ended as in the AR5). The median and likely range contributions by 2100 were 0.04 (0.01–0.11) m under RCP2.6 and 0.12 (0.03–0.28) m under RCP8.5 (Table 9.3). The positive scenario-dependence in SROCC – where increases in dynamic losses driven by ocean warming and ice-shelf disintegration under higher emissions (Section 9.4.2.3) dominate over increases in SMB – arose from a combination of physical processes and model limitations. Modelling improvements in these studies included improved representations of grounding line response to drivers, more extensive exploration of uncertainties, and inclusion of a positive feedback of meltwater on climate (Golledge et al., 2019). However, two of the projections did not include SMB changes that would offset dynamic losses (Levermann et al., 2014; Ritz et al., 2015), and the scenario dependence may have been further amplified by highly sensitive sub-shelf melt parametrizations and use of simplified SMB schemes (Golledge et al., 2015, 2019; Bulthuis et al., 2019; Oppenheimer et al., 2019).

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The SROCC assessed the median and likely range of Antarctic SLE contributions at 2300 as 0.16 (0.07–0.37) m under RCP2.6 and 1.46 (0.60–2.89) m under RCP8.5, based on three studies. It was noted that deep uncertaintyremained beyond 2100: while solid Earth feedbacks could reduce ice loss over multi-century time scales, MICI (Section 9.4.2.4) might give contributions higher than the likely ranges. The SROCC also presented structured expert judgement (SEJ) projections for comparison (Bamber et al., 2019), which give higher values. Since SROCC, three studies have made projections to 2300: (i) Rodehacke et al. (2020) assessed two methods for implementing precipitation changes (based on repeating 2071–2100 forcings beyond 2100), which both gave negative projections at 2300 because the dynamic response was very small (–0.11 to –0.01 m SLE for RCP2.6; –0.25 to –0.07 m for RCP8.5 forcing); (ii) In contrast, simulations forced by 2081–2100 ocean-only projections under RCP8.5/SSP5-8.5 beyond 2100, using two implementations of the ISMIP6 ‘non-local’ basal melt parametrizations (Box 9.3 and Section 9.4.2.2) and two sliding laws, are all positive (0.08 m to 0.96 m SLE by 2300), though these do not include the negative contribution from SMB changes (Lipscomb et al., 2021); (iii) Finally, DeConto et al. (2021) update projections for the MICI hypothesis (Section 9.4.2.4) using the extensions of the RCPs to 2300, and obtain far higher contributions: median (17–83%) ranges of 1.09 (0.71–1.35) m SLE under RCP2.6 and 9.60 (6.87–13.54) m SLE under RCP8.5. These are larger than previous estimates (DeConto and Pollard, 2016), particularly at the upper end: 0.68 (0.29–1.13) m SLE for RCP2.6 and 8.40 (7.47–9.76) m for RCP8.5 (Edwards et al., 2019), which can largely be explained by the higher maximum ice cliff calving rate. LARMIP-2 dynamic projections (Box 9.3) are also estimated under the extended SSPs and corrected with SMB (as in Section 9.4.2.5), giving median (17–83%) ranges of 0.40 (0.18–0.78) m SLE at 2300 under SSP1-2.6 and 1.57 (0.68–3.14) m under SSP5-8.5. The longer time scale may invalidate the linear response assumption of LARMIP-2, which neglects any self-dampening or self-amplifying processes. The ranges of projections for 2300 without MICI (Golledge et al., 2015; Bulthuis et al., 2019; Levermann et al., 2020; Rodehacke et al., 2020; Lipscomb et al., 2021; ‘assessed ice-sheet contributions’ in Section 9.6.3.5 are –0.14 to +0.78 m SLE under RCP2.6/SSP1-2.6, and –0.27 to 3.14 m SLE under RCP8.5/SSP5-8.5). The lower bounds are the 5th percentile of Bulthuis et al. (2019) and the lowest mean/median from Rodehacke et al. (2020), respectively; the upper bounds are the 83% percentiles of the LARMIP-2 estimates. These ranges are wider than SROCC likely ranges, and more consistent with the SEJ (Bamber et al., 2019). However, projections in which Antarctica contributes much more than the assessed ranges under sustained very high greenhouse gas emissions – that is, around 7–14 m to GMSL by 2300 (DeConto et al., 2021), cannot be ruled out, and are taken as a sensitivity case (Section 9.6.3.5; Table 9.11). In summary, there is high confidence that Antarctic mass loss will be greater beyond 2100 under high greenhouse gas emissions, but the large range of projections mean we have only low confidence in the likely AIS contribution to GMSL by 2300 for a given scenario. Deep uncertaintyremains in the role of AIS instabilities under very high emissions.

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Since SROCC, Marzeion et al. (2020) projected 21st century global-scale glacier mass changes based on seven global-scale and four regional-scale glacier models (Annex II). All models used the same initial and boundary conditions, forming a more coherent ensemble of projections compared to SROCC. Nevertheless, challenges remain because of scarcity of glacier thickness, surface mass balance (SMB) and frontal ablation data for model calibration, but also due to uncertainties in glacier outlines, surface elevations and ice velocities. The global SMB models are of varying complexity, including mass balance sensitivity approaches (van de Wal and Wild, 2001), temperature-index methods (Anderson and Mackintosh, 2012; Marzeion et al., 2012; Radić et al., 2014; Huss and Hock, 2015; Kraaijenbrink et al., 2017; Maussion et al., 2019; Zekollari et al., 2019; Rounce et al., 2020) and simplified energy balance calculations (Sakai and Fujita, 2017; Shannon et al., 2019). Compared to simpler, empirical parametrizations, full energy-balance models are not necessarily the most appropriate choice for simulating future glacier response to climate change, even at the local scale (Réveillet et al., 2017, 2018), because of parameter and forcing uncertainties. All models account for glacier retreat and advance, but only two models (Anderson and Mackintosh, 2012; Huss and Hock, 2015) include frontal ablation.

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Ongoing development of the Global Extreme Sea Level Analysis (GESLA) tide gauge database (Woodworth et al., 2016) along with data archaeology (Talke and Jay, 2013) extends availability of tide gauge records back to the mid 19th century (or earlier). Dynamical datasets used to assess trends in ESL at global or regional scales – for example, tide and surge contributions from the Global Tide and Surge Reanalysis (GTSR; Muis et al., 2016, 2020), or wave setup/swash contributions from available wave hindcasts/reanalyses (Melet et al., 2018) – have model biases introduced with resolution and parametrization limitations, incomplete atmospheric data, and currently span only a few decades, so they are not yet long or accurate enough to assess long-term trends in ESLs. Therefore, there is medium confidence in observed trends in ESWL, but only low confidence in modelled ESL trends.

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Lazeroms, W.M.J., A. Jenkins, G.H. Gudmundsson, and R.S.W. van de Wal, 2018: Modelling present-day basal melt rates for Antarctic ice shelves using a parametrization of buoyant meltwater plumes. The Cryosphere, 12(1), 49–70, doi: 10.5194/tc-12-49-2018.

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Soilán, M., B. Riveiro, P. Liñares, and M. Padín-Beltrán, 2018: Automatic Parametrization and Shadow Analysis of Roofs in Urban Areas from ALS Point Clouds with Solar Energy Purposes. ISPRS Int. J. Geo-Information, 7(8) , 301, doi:10.3390/ijgi7080301.

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Fischereit, J., R. Brown, X G. Larsén, J. Badger, and G. Hawkes, 2021: Review of Mesoscale Wind-Farm Parametrizations and Their Applications. Boundary-Layer Meteoro. , 182(2) , 1–50, doi:10.1007/s10546-021-00652-y.

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Besides emissions, and possible avoided emissions, related to the supply chain, the GHG effects of using bioenergy depend on: (i) change in GHG emissions when bioenergy substitutes another energy source; and (ii) how the associated land use and possible land-use change influence the amount of carbon that is stored in vegetation and (Calvin et al. 2021) soils over time. Studies arrive at varying mitigation potentials for bioenergy and BECCS due to the large diversity of bioenergy systems, and varying conditions concerning where and how they are deployed (Elshout 2015; Harper et al.2018; Muri 2018; Kalt et al.2019; Brandão et al. 2019; Buchspies et al. 2020; Cowie et al. 2021; Calvin et al. 2021). Important factors include feedstock type, land management practice, energy conversion efficiency, type of bioenergy product (and possible co-products), emissions intensity of the products being displaced, and the land use/cover prior to bioenergy deployment (Zhu et al. 2017; Staples et al. 2017; Daioglou et al. 2017; Carvalho et al. 2017; Hanssen et al. 2020; Mouratiadou et al. 2020). Studies arrive at contrasting conclusions also when similar bioenergy systems and conditions are analysed, due to different methodologies, assumptions, and parametrization (Harper et al.2018; Kalt et al.2019; Brandão et al. 2019; Albers et al. 2019; Buchspies et al. 2020; Bessou et al. 2020; Rolls and Forster 2020; Cowie et al. 2021).

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Understanding the mechanisms driving interannual variability in the carbon cycle has the potential to provide insights into whether and to what extent the carbon cycle can affect the climate (carbon–climate feedback), with particular interests over the highly climate-sensitive tropical carbon cycle (e.g., Cox et al., 2013; X. Wang et al., 2014; Fang et al., 2017; Jung et al., 2017; Humphrey et al., 2018; Malhi et al., 2018; see Section 5.4). Consistent findings from studies with atmospheric inversions, satellite observations and DGVMs (e.g., Malhi et al., 2018; Rödenbeck et al., 2018) lead to high confidence that the tropical net land CO2 sink is reduced under warmer and drier conditions, particularly during El Niño events. Interannual variations in tropical land-atmosphere CO2 exchange are significantly correlated with anomalies of tropical temperature, water availability and terrestrial water storage (X. Wang et al., 2014; Jung et al., 2017; Humphrey et al., 2018; Piao et al., 2020), whose relative contribution are difficult to separate due to covariations between these climatic factors. At continental scale, the dominant climatic driver of interannual variations of tropical land-atmosphere CO2 exchange was temperature variations (Figure 5.11; Piao et al., 2020), which could partly result from the spatial compensation of the water availability effects on land-atmospheric CO2 exchange (Jung et al., 2017).

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The atmospheric methane (CH4) growth rate has varied widely over the past three decades, and the causes have been extensively studied since AR5. The mean growth rate decreased from 15 ± 5 ppb yr–1 in the 1980s to 0.48 ± 3.2 ppb yr–1 during 2000–2006 (the so-called quasi-equilibrium phase) and returned to an average rate of 7.6 ± 2.7 ppb yr–1 in the past decade (2010–2019) (based on data in Figure 5.14). Atmospheric CH4 grew faster (9.3 ± 2.4 ppb yr–1) over the last six years (2014–2019) – a period with prolonged El Niño conditions, which contributed to high CH4 growth rates consistent with behaviour during previous El Niño events (Figure 5.14b). Because of large uncertainties in both the emissions and sinks of CH4, it has been challenging to quantify accurately the methane budget and ascribe reasons for the growth over 1980–2019. In the context of CH4 emissions mitigation, it is critical to understand if the changes in growth rates are caused by emissions from human activities or by natural processes responding to changing climate. If CH4 continues to grow at rates similar to those observed over the past decade, it will contribute to decadal scale climate change and hinder the achievement of the long-term temperature goals of the Paris Agreement (Section 7.3.2.2; Nisbet et al., 2019).

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There has been a positive trend in globally averaged surface CO2 mixing ratios since 1958 (Figure 2.5a), that reflects the imbalance of sources and sinks (Section 5.2). The growth rate has increased overall since the 1960s (Figure 2.5a inset), while annual growth rates have varied substantially, for example, reaching a peak during the strong El Niño events of 1997–1998 and 2015–2016 (Bastos et al., 2013; Betts et al., 2016). The average annual CO2 increase from 2000 through 2011 was 2.0 ppm yr–1 (standard deviation 0.3 ppm yr–1), similar to what was reported in AR5. From 2011 through 2019 it was 2.4 ppm yr1 (standard deviation 0.5 ppm yr–1), which is higher than that of any comparable time period since global measurements began. Global networks consistently show that the globally averaged annual mean CO2 has increased by 5.0% since 2011, reaching 409.9 ± 0.4 ppm in 2019 (NOAA measurements). Further assessment of changing seasonality is undertaken in Section 2.3.4.1.

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Human intervention on river discharge linked to increases in evapotranspiration and some reduction of intra-annual streamflow variability (Jaramillo and Destouni, 2015; Chai et al., 2020) might affect the detection of trends in extreme daily streamflow events (Do et al., 2017; Gudmundsson et al., 2019). However, these activities have a minor impact on annual streamflow compared to climate variations (Dai et al., 2009; Alkama et al., 2013). Available global studies post-1950 generally concur that there have been more rivers experiencing decreases than increases in runoff (Do et al., 2017; Su et al., 2018; Gudmundsson et al., 2019; X. Shi et al., 2019). Most of the rivers have not experienced statistically significant changes in streamflow, and when globally aggregated there is no significant change (Dai and Zhao, 2017). Global streamflow variability is strongly modulated by ENSO and PDV, with below-normal global streamflow as a response to El Niño events and vice-versa during La Niña episodes (Dai, 2016; Liang et al., 2016; Kim, 2019). The response of streamflow to changes in precipitation associated with ENSO and PDV has heterogeneous regional patterns at subcontinental scales (Section 8.3.2.9.1). No significant trends are found for reanalysis-based discharge estimates over 1993 to 2015 (Chandanpurkar et al., 2017). Uncertainties in global streamflow trends arise predominantly from changes in instrumentation, gauge restoration, recalibration of rating curves, flow regulation or channel engineering (Alkama et al., 2011; Gudmundsson et al., 2018; Ghiggi et al., 2019).

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The AR5 reported with medium confidence that ENSO-like variability existed, at least sporadically, during the warm background state of the Pliocene. It was also found (high confidence) that ENSO has remained highly variable during the last 7 kyr with no discernible orbital modulation. The AR5 concluded that large variability on interannual to decadal timescales, and differences between datasets, precluded robust conclusions on any changes in ENSO during the instrumental period. The SROCC reported epochs of strong ENSO variability throughout the Holocene, with no indications of a systematic trend in ENSO amplitude, but with some indication that the ENSO amplitude over 1979–2009 was greater than at any point in the period from 1590–1880 CE. It was also reported that the frequency and intensity of El Niño events in the period from 1951–2000 was high relative to 1901–1950.

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Since AR5, updates to datasets used widely in prior ENSO assessments resulted in substantial and important revisions to observed tropical Pacific SST data (Section 2.3.1.1). In particular, ERSSTv4, and then ERSSTv5, addressed known SST biases in ERSSTv3 in the equatorial Pacific which affected the derived mean state and amplitude of indices based on that dataset (Huang et al., 2015). During the instrumental period, there is no robust indication of any significant century-scale trend in the east-west SST gradient across the equatorial Pacific Ocean, with periods when gradients have been stronger and weaker than the long-term average on decadal timescales, associated with a predominance of La Niña or El Niño events respectively. The frequency of El Niño and La Niña events is also subject to considerable decadal variability (e.g., Hu et al., 2013) but with no indication of a long-term signal in the frequency of events. The ENSO amplitude since 1950 has increased relative to the 1910–1950 period, as confirmed by independent proxy records (e.g., Gergis and Fowler, 2009), the Southern Oscillation Index (SOI) (Braganza et al., 2009) and SSTs (e.g., Ohba, 2013; Yu and Kim, 2013), although there is a spread between different proxy and instrumental sources as to the magnitude of that increase (Figure 2.36). The El Niño events of 1982–1983, 1997–1998 and 2015–2016 had the strongest anomalies in the Niño 3.4 SST index since 1950. Their predominance was less evident from indices based on de-trended data such as the Oceanic Niño Index (ONI) (which still ranked them as the three strongest events since 1950, but only by a small margin), and in the SOI. B. Huang et al. (2019a) also note that analyses based upon buoy and Argo data, which are only available since the 1990s, are more capable of resolving strong events than analyses which do not include such data.

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There is a distinction (Annex IV.2.3.1) between El Niño events centred in the eastern Pacific (‘Eastern Pacific’ (EP) or ‘classical’ events) and those centred in the Central Pacific (‘Central Pacific’ (CP) or ‘Modoki’ events), which have different typical teleconnections (e.g., Ashok et al., 2007; Ratnam et al., 2014; Capotondi et al., 2015; Timmermann et al., 2018). A number of studies, using a range of indicators, have found an increase in recent decades of the fraction of CP El Niño events, particularly after 2000 (Yu and Kim, 2013; Lübbecke and McPhaden, 2014; Pascolini-Campbell et al., 2015; Jiang and Zhu, 2018). Johnson (2013) found that the frequency of CP El Niño events had increased (although not significantly) over the 1950–2011 period, being accompanied by a significant increase in the frequency of La Niña events with a warm (as opposed to cool) western Pacific warm pool. A coral-based reconstruction starting in 1600 CE (Freund et al., 2019) found that the ratio of CP to EP events in the last 30 years was substantially higher than at any other time over the last 400 years. Variations in the proportion of CP and EP events have also been found in earlier periods, with Carré et al. (2014) finding a period of high CP activity around 7 ka.

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In the instrumental period, teleconnections associated with ENSO are well known to vary on decadal to multi-decadal timescales (e.g., He et al., 2013; Lee and Ha, 2015; Ashcroft et al., 2016; Jin et al., 2016; Wang et al., 2019). Yun and Timmermann (2018) found that decadal variations in teleconnections between ENSO and the Indian monsoon did not extend beyond what would be expected from a stochastic process. Many observed decadal changes in teleconnections in the instrumental period are consistent with a shift to more central Pacific El Niño events (Evtushevsky et al., 2018; Yeh et al., 2018; Yu and Sun, 2018; Zhao and Wang, 2019). Effects of the PDV (Kwon et al., 2013; S. Wang et al., 2014; Dong et al., 2018) and the AMV (Kayano et al., 2019) can also modulate ENSO teleconnections, and affect the frequency of CP versus EP events (Ashok et al., 2007). Chiodi and Harrison (2015) found that teleconnections over the most recent decades are broadly consistent with those over the last 100 years. Variability in teleconnections can also occur on timescales longer than characteristic PDV timescales (e.g., Gallant et al., 2013).

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In summary, there is medium confidence that both ENSO amplitude and the frequency of high-magnitude events since 1950 are higher than over the period from 1850 and possibly as far back as 1400, but low confidence that they are outside the range of variability over periods prior to 1400, or higher than the average of the Holocene as a whole. Overall, there is no indication of a recent sustained shift in ENSO or associated features such as the Walker Circulation, or in teleconnections associated with these, being beyond the range of variability on decadal to millennial timescales. A high proportion of El Niño events in the last 20–30 years have been based in the central, rather than eastern Pacific, but there is low confidence that this represents a long-term change.

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Pascolini-Campbell, M. et al., 2015: Toward a record of Central Pacific El Niño events since 1880. Theoretical and Applied Climatology, 119(1), 379–389, doi: 10.1007/s00704-014-1114-2.

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Yu, J.-Y. and S.T. Kim, 2013: Identifying the types of major El Niño events since 1870. International Journal of Climatology, 33(8), 2105–2112, doi: 10.1002/joc.3575.

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The continuum of El Niño events are typically stratified into two types (often termed ‘flavours’), Central Pacific and East Pacific, where the name denotes the location of the events’ largest SST anomalies (Annex IV.2.3; Capotondi et al., 2015). As discussed in Section 2.4.2, the different types of events tend to produce distinct teleconnections and climatic impacts (e.g., Taschetto et al., 2020). The characteristics of El Niño events of these two flavours in CMIP5 were generally comparable to the observations (Taschetto et al., 2014). CMIP6 models, however, display a statistically significant improvement in the representation of this ENSO event-to-event SST anomaly diversity when compared with CMIP5 models (Planton et al., 2021). In addition to this ENSO event diversity, the short observational record also displays an increase in the number of the Central Pacific-type events in recent decades (Ashok et al., 2007; McPhaden et al., 2011), which has also been identified as unusual in the context of the last 500–800 years based on recent paleo-climatic reconstructions (Section 2.4.2; Y. Liu et al., 2017; Freund et al., 2019). However, the short observational record combined with observational (L’Heureux et al., 2013) and paleo-climatic reconstruction uncertainties preclude firm conclusions being made about the long-term changes in the occurrence of different El Niño event types. Initial analysis with a selected number of CMIP3 models suggested that there may be a forced component to this recent prominence of Central Pacific-type events (Yeh et al., 2009), but analysis since then suggests that this behaviour is (i) consistent with that expected from internal variability (Newman et al., 2011); and (ii) not apparent across the full CMIP5 ensemble of historical simulations (Taschetto et al., 2014). Analysis of single-model large ensembles suggests that changes to ENSO event type in response to historical radiative forcing are not significant (e.g., Stevenson et al., 2019). These same results, however, also suggest that multiple forcings can have significant influences on ENSO type and that the net response will depend on the accurate representation of the balance of these forcings (Stevenson et al., 2019).

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Freund, M.B. et al., 2019: Higher frequency of Central Pacific El Niño events in recent decades relative to past centuries. Nature Geoscience, 12(6), 450–455, doi: 10.1038/s41561-019-0353-3.

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Even though there is low agreement in simulated changes in ENSO SST variability, the majority of models project an increase in amplitude of ENSO rainfall variability attributable to the increase in mean SST and moisture in CMIP5 (Power et al., 2013; Watanabe et al., 2014; Huang and Xie, 2015) and CMIP6 (Yun et al., 2021). It is likely that extreme El Niño events, accompanied by the eastern equatorial Pacific rainfall exceeding the 5 mm day–1rainfall threshold, will increase in intensity (Cai et al., 2014a, 2017). However, it has also been suggested that historical model biases over the equatorial Pacific cold tongue in CMIP5 may lead to the greater precipitation mean change and amplification of extreme ENSO-associated rainfall in CMIP5 (Stevenson et al., 2021).

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There is limited intermodel agreement on future changes in ENSO teleconnections largely depending on changes in the mean state and changes in ENSO properties (Yeh et al., 2018). Many CMIP5 and CMIP6 models project that the centres of the extratropical teleconnection over North Pacific and North America will shift eastward in association with an eastward shift in tropical convective anomalies (Yeh et al., 2018; Fredriksen et al., 2020). There is an indication that tropical cyclones will become more frequent during future El Niño events (and less frequent during future La Niña events) by the end of the 21st century (Chand et al., 2017), thus contributing to the projected increase in ENSO-associated hydro-climate impacts.

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Cai, W. et al., 2014a: Increasing frequency of extreme El Niño events due to greenhouse warming. Nature Climate Change, 4(2), 111–116, doi: 10.1038/nclimate2100.

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In summary, the 2015–2016 extreme El Niño and the 2018 boreal spring/summer extremes were two examples of recent concurrent extremes. The El Niño event in 2015–2016 was one of the three extreme El Niño events since the 1980s, and there are many extreme events concurrently observed in this period including droughts, heavy precipitation, and more frequent intense tropical cyclones. Both the ENSO amplitude and the frequency of high-magnitude events since 1950 is higher than over the pre-industrial period (medium confidence), suggesting that global extremes similar to those associated with the 2015–2016 extreme El Niño would occur more frequently under further increases in global warming. The 2018 boreal spring/summer extremes were characterized by heat extremes and enhanced droughts in wide areas of the mid-latitudes in the Northern Hemisphere and extremely heavy rainfall in East Asia. These concurrent events were generally related to the abnormal condition of the jet and North Pacific Subtropical High, but also amplified by background global warming. It is virtually certain that these 2018 concurrent extreme events would not have occurred without human-induced global warming. Recent years have seen a more frequent occurrence of such concurrent events. However, it is still unknown which types of concurrent extreme events could occur under increasing global warming.

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Cai, W. et al., 2014b: Increasing frequency of extreme El Niño events due to greenhouse warming. Nature Climate Change, 4(2), 111–116, doi: 10.1038/nclimate2100.

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ENSO influences precipitation and evaporation dynamics, river flow and flooding at a global scale (Figure 3.37; Ward et al., 2014, 2016; Martens et al., 2018). Reconstruction (18042005) of Thailand’s Chao Praya River peak season streamflow displays a strong correlation with ENSO (Xu et al., 2019). Based on water storage estimates from 2002 to 2015, drought conditions over the Yangtze River basin followed La Niña events and flood conditions followed El Niño events (Z. Zhang et al., 2015). Strong correlation between ENSO and terrestrial water storage has been identified mostly in the subtropics but with diverse intensities and time lags depending on the region (Ni et al., 2018). The likelihood of increased/decreased flood hazard during ENSO events has a complex spatial pattern with large uncertainties (Emerton et al., 2017).

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Rifai, S.W., S. Li, and Y. Malhi, 2019: Coupling of El Niño events and long-term warming leads to pervasive climate extremes in the terrestrial tropics. Environmental Research Letters, 14(10), 105002, doi: 10.1088/1748-9326/ab402f .

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Freund, M.B. et al., 2019: Higher frequency of Central Pacific El Niño events in recent decades relative to past centuries. Nature Geoscience, 12(6), 450–455, doi: 10.1038/s41561-019-0353-3.

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Globally, there is overall high confidence that suitable shellfish aquaculture habitat will decline by 2100 under projected warming, ocean acidification and primary productivity changes, with significant negative impacts for some regions and species before 2100 (Table 5.9, Froehlich et al., 2018a; Ghezzo et al., 2018). Shellfish growth will increase with warming waters until tolerances are reached, such as through extreme El Niño events (high confidence) (Beveridge et al., 2018b; Dabbadie et al., 2018; Liu et al., 2018b; Liu et al., 2020). Rising temperatures and ocean acidification will result in losses of primary productivity and farmed species from tropical and subtropical regions, and gains in higher latitudes (high confidence) (Froehlich et al., 2018a; Aveytua-Alcazar et al., 2020; Chapman et al., 2020; Des et al., 2020; Oyinlola et al., 2020), but net marine production gains could be achieved under strong mitigation (Thiault et al., 2019). Shellfish Vibrio infections will increase with warming waters and extreme events, increasing shellfish mortalities (medium confidence) (Green et al., 2019; Montanchez et al., 2019), with ocean acidification impairing immune responses (limited evidence) (Cao et al., 2018b). Bivalve larvae are known to be highly vulnerable to ocean acidification (high confidence) (see Section 3.3, Bindoff et al., 2019), with projected regional and species-specific levels of impact (high confidence) (Ekstrom et al., 2015; Zhang et al., 2017b; Mangi et al., 2018) (Greenhill et al., 2020). Ocean acidification is also projected to weaken shells, affecting productivity and processing (high confidence) (Martinez et al., 2018; Cummings et al., 2019) and dependent livelihoods (Doney et al., 2020).

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Chapman, S. et al., 2020: Compounding impact of deforestation on Borneo’s climate during El Niño events. Environmental Research Letters, 15 (8), 084006, doi:10.1088/1748–9326/ab86 f5.

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Santidrián Tomillo, P., et al., 2020: The impacts of extreme El Niño events on sea turtle nesting populations. Clim. Change, 159 (2), 163–176, doi:10.1007/s10584-020-02658-w.

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Morera, S.B., et al., 2017: The impact of extreme El Niño events on modern sediment transport along the western Peruvian Andes (1968–2012). Sci. Rep. , 7 (1), 11947, doi:10.1038/s41598-017-12220-x.

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Cai, W. et al., 2014: Increasing frequency of extreme El Niño events due to greenhouse warming. Nature Climate Change, 4 (2), 111–116, doi:10.1038/nclimate2100.

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Cai, W., et al., 2014: Increasing frequency of extreme El Niño events due to greenhouse warming. Nature Clim. Change, 4 (2), 111–116, doi:10.1038/nclimate2100.

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Tropical and subtropical islands face risks from vector-borne diseases, such as malaria, dengue fever, and the Zika virus. El Niño events can increase the risk of diseases such as Zika virus by increasing biting rates, decreasing mosquito mortality rates and shortening the time required for the virus to replicate within the mosquito (Caminade et al., 2017). By combining disease prediction models with climate indicators that are routinely monitored, alongside evaluation tools, it is possible to generate probabilistic dengue outlooks in the Caribbean and early warning systems (Oritz et al., 2015; Lowe et al., 2018). Projections suggest that more individuals will become at risk of dengue fever by the 2030s and beyond because of an increasing abundance of mosquitos and larger geographic range (Ebi et al., 2018). Projected increases in mean temperature could double the dengue burden in New Caledonia by 2100 (Teurlai et al., 2015). In the Caribbean, Saharan dust transported across the Atlantic can interact with Caribbean seasonal climatic conditions to become respirable and contribute to asthma presentations at the emergency department (See Table 15.5; Akpinar-Elci et al., 2015).

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For central Chile, a significant increase (5% to 20% in the last 60 years) in wave heights in the sea has been observed (Martínez et al., 2018). From 1982 to 2016, sea levels at central Chile have increased 5 mm yr −1, where El Niño events of 1982–1983 and 1997–1998 caused an extreme increase of 15 to 20 cm in the mean sea level (Campos-Caba, 2016; Martínez et al., 2018).

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Morera, S.B., et al., 2017: The impact of extreme El Niño events on modern sediment transport along the western Peruvian Andes (1968–2012). Sci. Rep. , 7 (1), 11947–11947, doi:10.1038/s41598-017-12220-x.

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Leaf stomata closure can have large effects on land freshwater availability because of reduced plant transpiration, leading in some regions to higher soil moisture and runoff (Roderick et al., 2015; Milly and Dunne, 2016; Y. Yang et al., 2019). However, increased water availability is often not realized because other CO2 physiological effects that enhance ecosystem evapotranspiration might offset the gains. These effects include plant growth and leaf area expansion (Ainsworth and Long, 2005; Ukkola et al., 2016; McDermid et al., 2021), lengthening of the vegetative growing season (Frank et al., 2015; Lian et al., 2021), and the effects of stomatal closure on near-surface atmosphere that leads to increased air temperature and VPDs (Berg et al., 2016; Vogel et al., 2018; Zhou et al., 2019; Grossiord et al., 2020).

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The impacts of different CDR options on the water cycle depend crucially on regional climate, prior land cover, and scale of deployment (Trabucco et al., 2008). Extensive irrigation for afforestation in drier areas will have larger downstream impacts than in wetter regions, with the difference in water use between the afforested landscapes and its previous vegetation determining the level of potential impacts on evapotranspiration and runoff (Jackson et al., 2005; Teuling et al., 2017). Afforestation and reforestation sometimes enhances precipitation through atmospheric feedbacks such as increased convection, at least in the tropics (Ellison et al., 2017) and the increase in precipitation can, in some regions, even cancel out the increased evapotranspiration (Li et al., 2018).

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Biochar is produced by burning biomass at high temperatures under anoxic conditions (pyrolysis) and, when added to soils, can increase soil carbon stocks and fertility for decades to centuries (Woolf et al., 2010; Lehmann et al., 2015). Biochar application improves many soil qualities and increases crop yield (medium confidence) (Ye et al., 2020; SRCCL, Chapter 4.9.5), particularly in already degraded or weathered soils (Woolf et al., 2010; Lorenz and Lal, 2014; Jeffery et al., 2016), increases soil water holding capacity (medium confidence) (Karhu et al., 2011; Liu et al., 2016; B.M.C. Fischer et al., 2019; Verheijen et al., 2019) and evapotranspiration (low confidence)(B.M.C. Fischer et al., 2019). The use of biochar reduces nutrient losses (low confidence) (Woolf et al., 2010), enhances fertilizer nitrogen use efficiency and improves the bioavailability of phosphorus (Figure 5.36; Clough et al., 2013; Shen et al., 2016; Z. Liu et al., 2017). Biochar addition may decrease methane (CH4) emissions in inundated and acid soils such as rice fields (low confidence)(Jeffery et al., 2016; Huang et al., 2019; Wang et al., 2019; Yang et al., 2019). In non-inundated, neutral soils, CH4 uptake from the atmosphere is suppressed after biochar application (low confidence) (Jeffery et al., 2016), and soil N2O emissions decline (medium confidence) (Cayuela et al., 2014; Kammann et al., 2017). The potential risks of introducing harmful contaminants into the soil environment are not well understood (Lorenz and Lal, 2014). With low confidence, application of biochar can have co-benefits for soil microbial biodiversity (P. Smith et al., 2018), while the potential trade-offs for biodiversity are due to land requirements (Tisserant and Cherubini, 2019).

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SRM-mediated cooling also affects the terrestrial carbon cycle. Relative to a high-GHG world without SRM, the simulated responses of net primary production (NPP) to SRM differ widely between models, such that even the sign of global mean change is uncertain (Glienke et al., 2015). SRM-induced cooling would decrease NPP at high latitudes by reducing the length of the growing season (Glienke et al., 2015). At low latitudes, the NPP response to SRM-induced cooling is sensitive to the effect of nitrogen limitation (Glienke et al., 2015; Duan et al., 2020). SRM-induced cooling tends to increase NPP in models without the nitrogen cycle because of reduced heat stress. However, in models including the nitrogen cycle, this is counteracted by reductions in NPP because of reductions in nitrogen mineralization and nitrogen availability (Glienke et al., 2015). SRM-induced changes in the hydrological cycle (Section 8.6.3), including changes in evapotranspiration, precipitation, and soil moisture, also pose strong constraints on the vegetation response (Dagon and Schrag, 2019). For the same amount of global mean cooling, different SRM options, such as SAI, MCB, and CCT, would have different effects on gross primary production (GPP) and NPP because of different spatial patterns of temperature, available sunlight and hydrological cycle changes (Section 4.6.3.3) (Duan et al., 2020). Modelling studies show that SRM-induced cooling would reduce plant and soil respiration (Tjiputra et al., 2016; Cao and Jiang, 2017; Muri et al., 2018; C.-E. Yang et al., 2020). Despite the large uncertainty in modelled NPP response, existing modelling studies consistently show that SRM would increase the global land carbon sink relative to a high-CO2 world without SRM (high confidence).

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Milly, P.C.D. and K.A. Dunne, 2016: Potential evapotranspiration and continental drying. Nature Climate Change, 6(10), 946–949, doi: 10.1038/nclimate3046.

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Teuling, A.J. et al., 2019: Climate change, reforestation/afforestation, and urbanization impacts on evapotranspiration and streamflow in Europe. Hydrology and Earth System Sciences, 23(9), 3631–3652, doi: 10.5194/hess-23-3631-2019.

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Since AR5, significant effort has been devoted to understanding the mechanisms for the decrease in near-surface land RH under global warming, and the relevance of RH changes for the land–sea warming contrast and the water cycle. For the near-surface RH decrease over land, both the moisture transport from the ocean and land–atmosphere feedback processes contribute. For changes in specific humidity over land, the moisture transport from the ocean is dominant while the role of evapotranspiration is secondary (Byrne and O’Gorman, 2016; Chadwick et al., 2016). Nevertheless, the changes in near-surface land RH are also strongly influenced by evapotranspiration, which is suppressed by the drying of soils and plant responses to increasing CO2 related to stomatal closure under climate change (Byrne and O’Gorman, 2015; Berg et al., 2016; Chadwick et al., 2016; Swann et al., 2016; Lemordant et al., 2018). The combination of oceanic and continental influences can explain the spatially diverse trends in the near-surface RH over land in the observations for the recent decades, with a generally dominant negative trend at the global scale (Vicente-Serrano et al., 2018). There is a strong feedback between the near-surface land RH decrease and land–ocean warming contrast under future warming projections (Section 4.5.1.1).

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Changes in land RH can modulate the response of the water cycle to global warming (Chadwick et al., 2013; Byrne and O’Gorman, 2015). Most CMIP5 models project higher precipitation associated with higher near-surface RH and temperature under climate change (Lambert et al., 2017). Over land, the spatial gradients of fractional changes in near-surface RH contribute to a drying tendency in precipitation minus evapotranspiration with warming, which partly explains why the ‘wet gets wetter, dry gets drier’ paradigm does not hold over land (Byrne and O’Gorman, 2015). Terrestrial aridity is projected to increase over land, as manifested by a decrease in the ratio of precipitation to potential evapotranspiration, in which the decrease in near-surface land RH has a contribution of about 35% in CMIP5 models under doubled CO2 forcing (Fu and Feng, 2014). The aridity can be further amplified by the feedbacks of projected drier soils on land surface temperature, RH, and precipitation (Berg et al., 2016).

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Because of different sensitivity of precipitation change to CO2 and solar forcings (Myhre et al., 2017), if shortwave-based SRM is used to fully offset GHG-induced global mean warming, there would be a overcompensation of GHG-induced increase in global mean precipitation (Kravitz et al., 2013a; Tilmes et al., 2013; Irvine et al., 2016). Further, regional SRM approaches such as aerosol injections into the Arctic stratosphere are likely to remotely influence on tropical monsoon precipitation by shifting the mean position of ITCZ (Nalam et al., 2018). However, the shift could be avoided by simultaneously cooling the southern hemisphere (MacCracken et al., 2013; Kravitz et al., 2016; Nalam et al., 2018). The SRM response of precipitation minus evapotranspiration (P–E) is found to be smaller than that of precipitation because of reduction in both precipitation and evapotranspiration (Tilmes et al., 2013; Nalam et al., 2018; Irvine et al., 2019). Thus, global mean soil moisture could be effectively maintained, though with significant regional variability (Cheng et al., 2019).

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Internal variability could mask the response to solar radiation modification (SRM)-related forcing in the near term (Section 4.6.3.1). A detection of the global scale climate system response to stratospheric sulphate aerosol injection will likely require a forcing of the size produced by the 1991 Mount Pinatubo eruption (Robock et al., 2010). In model simulations of where 5 Tg SO2 is injected into the stratosphere continuously (roughly one fourth of the 1991 Pinatubo eruption per year) under RCP 4.5, it is shown that, relative to the high-GHG world without SRM, the effect of SRM on global temperature and precipitation is detectable after one to two decades (Bürger and Cubasch, 2015; Lo et al., 2016) which is similar to the time scale for the emergence of GSAT trends due to strong mitigation (Section 4.6.3.1). The detection time is sensitive to detection methods and filtering techniques (Lo et al., 2016). An analysis using GLENS simulation (MacMartin et al., 2019) compares response in temperature, precipitation, and precipitation minus evapotranspiration (P-E) between a climate state with GHG-induced 1.5°C global mean temperature change and that with the same global mean temperature but under RCP4.5 emissions and a limited deployment of SO2 injection. It is found that at grid-scale, difference in climate response between these two climate states are not detectable by the end of this century. However, for higher emissions scenarios of the RCP8.5 and correspondingly larger SRM deployment for maintaining the same global mean temperature change of 1.5°C, the regional differences are detectable before the end of the century. In addition to surface temperature and precipitation, observations of aerosol burden and temperature in the stratosphere via the deployment of stratospheric aerosol observing system might facilitate the detection of climate response to SAI.

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Again, the manifestation of changes in the hydrological cycle for a high-warming storyline is not limited to precipitation, but would substantially affect other variables such as soil moisture, runoff, atmospheric humidity, and evapotranspiration. The changes are also not limited to annual mean precipitation but may be stronger or weaker for individual seasons and for precipitation extremes and dry spells.

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Byrne, M.P. and P.A. O’Gorman, 2015: The Response of Precipitation Minus Evapotranspiration to Climate Warming: Why the “Wet-Get-Wetter, Dry-Get-Drier” Scaling Does Not Hold over Land. Journal of Climate, 28(20), 8078–8092, doi: 10.1175/jcli-d-15-0369.1.

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Water management and irrigation were generally not accounted for by CMIP5 global models available at the time of SRCCL. Additional water can modify regional energy and moisture balance particularly in areas with highly productive agricultural crops with high rate of evapotranspiration. Urbanization increases the risks associated with extreme events (high confidence). Urbanization suppresses evaporative cooling and amplifies heatwave intensity (high confidence) with a strong influence on minimum temperatures (high confidence).

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Persistent hydroclimatic drought in south-western North America remains a much-studied event. Drought is a regular feature of the south-western North America’s climate regime, as can be seen in both the modern record, and through paleoclimate reconstructions (Cook et al., 2010; Woodhouse et al., 2010; Williams et al., 2020), as well as in future climate model projections (Cook et al., 2015a). Since the early 1980s, which were relatively wet in terms of precipitation and streamflow, the region has experienced major multi-year droughts such as the turn-of-the-century drought that lasted from 1999 to 2005, and the most recent and extreme 2012–2014 drought that in certain locations is perhaps unprecedented in the last millennium (Section 8.3.1.6; Griffin and Anchukaitis, 2014; Robeson, 2015). Shorter dry spells also happened between these multi-year droughts making 1980 to present a period with an exceptionally steep trend from wet to dry (Figure 10.13a), leading to strong declines in Rio Grande and Colorado river flows (Lehner et al., 2017b; Udall and Overpeck, 2017). While robust attribution of this trend is complicated by the large natural variability in this region, the 20th century warming has been suggested to increase the chances for hydrological drought periods by lowering runoff efficiency (Woodhouse et al., 2016; Lehner et al., 2017b; Woodhouse and Pederson, 2018) and affecting evapotranspiration (Williams et al., 2020). There is some evidence suggesting that the Last Glacial Maximum, a period of low atmospheric CO2, about 21 ka ago, has a thermodynamically-driven zonal mean precipitation response similar to that of the current state with relatively high CO2 levels when compared with the pre-industrial period. Pluvial conditions at that time and a reduction in precipitation from the Last Glacial Maximum to the pre-industrial period are consistent with drying trends for the region in models with GHG concentrations exceeding pre-industrial levels. However, the dominant large-scale drivers responsible for the precipitation changes observed during these two transitions are markedly different: mainly ice-sheet retreat and increasing insolation on one hand, increasing GHGs on the other hand. This suggests that the Last Glacial Maximum correspondence is fortuitous which strongly limits its use to capture future hydrological cycle changes (Section 8.3.2.4.4; Morrill et al., 2018; Lowry and Morrill, 2019). Furthermore, the conclusion of the Last Glacial Maximum drying versus wetting seems to strongly depend on the physical property of interest, hydrologic or vegetation indicators (Scheff et al., 2017). Droughts are characterized by deficits in total soil moisture content that can be caused by a combination of decreasing precipitation and warming temperature, which promotes greater evapotranspiration. Regional-scale attribution of the prevalence of south-western North America drought since 1980 then mostly focuses on the attribution of change in these two variables.

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There is high confidence that drought severity and intensity will increase in the Mediterranean. Increased evapotranspiration due to growing atmospheric water demand will decrease soil moisture (high confidence). The seasonality of runoff and streamflow (the annual difference between the wettest and driest months of the year) is expected to increase with global warming (high confidence). Annual runoff is very likely to decrease. Under middle or high-emissions scenarios, the likelihood of extreme droughts increases by 200–300% in the Mediterranean. The paleoclimate record provides context for these future expected changes: climate change will shift soil moisture outside the range of observed and reconstructed values spanning the last millennium (high confidence) (Sections 8.4.1.5 and 8.4.1.6).

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Finally, extreme events may also regionally amplify one another. For example, this is the case for heatwaves and droughts, with high temperatures and stronger radiative forcing leading to drying tendencies on land due to increased evapotranspiration (Section 11.6), and drier soils then inducing decreased evapotranspiration and higher sensible heat flux and hot temperatures (Box 11.1, Section 11.8; Seneviratne et al., 2013; Miralles et al., 2014a; Vogel et al., 2017; Zscheischler and Seneviratne, 2017; S. Zhou et al., 2019; Kong et al., 2020).

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Several regional climate models (RCMs) have also been evaluated in terms of their performance in simulating the climatology of extremes in various regions of the Coordinated Regional Downscaling Experiment (CORDEX) (Giorgi et al., 2009), especially in East Asia (Ji and Kang, 2015; Yu et al., 2015; Park et al., 2016; Bucchignani et al., 2017; Gao et al., 2017a; Niu et al., 2018; Y. Sun et al., 2018b; Wang et al., 2019), Europe (Vautard et al., 2013, 2021; Smiatek et al., 2016; Gaertner et al., 2018; Cardoso et al., 2019; Lorenz et al., 2019; Jacob et al., 2020; Kim et al., 2020), and Africa (J. Kim et al., 2014; Diallo et al., 2015; Dosio, 2017; Samouly et al., 2018; Mostafa et al., 2019). Compared to GCMs, RCM simulations show an added value in simulating temperature-related extremes, though this depends on topographical complexity and the parameters employed (see Section 10.3.3). The improvement with resolution is noted in East Asia (Park et al., 2016; W. Zhou et al., 2016; Shi et al., 2017; Hui et al., 2018). However, in the European CORDEX ensemble, different aerosol climatologies with various degrees of complexity were used in projections (Bartók et al., 2017; Lorenz et al., 2019) and the land surface models used in the RCMs do not account for physiological CO2 effects on photosynthesis leading to enhanced water-use efficiency and decreased evapotranspiration (Schwingshackl et al., 2019), which could lead to biases in the representation of temperature extremes in these projections (Boé et al., 2020). In addition, there are key cold biases in temperature extremes over areas with complex topography (Niu et al., 2018). Over North America, 12 RCMs were evaluated over the ARCTIC-CORDEX region (Diaconescu et al., 2018). Models performed well at simulating climate indices related to mean air temperature and hot extremes over most of the Canadian Arctic, with the exception of the Yukon region where models displayed the largest biases related to topographic effects. Two RCMs were evaluated against observed extremes indices over North America over the period 1989–2009, with a cool bias in minimum temperature extremes shown in both RCMs (Whan and Zwiers, 2016). The most significant biases are found in TXx and TNn, with fewer differences in the simulation of annual minimum daily maximum temperature (TXn) and annual maximum daily minimum temperature (TNx) in Central and Western North America. Over Central and South America, maximum temperatures from the Eta RCM are generally underestimated, although hot days, warm nights, and heatwaves are increasing in the period 1961–1990, in agreement with observations (Chou et al., 2014b; Tencer et al., 2016; Bozkurt et al., 2019).

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In regions with a seasonal snow cover, snowmelt is the main cause of extreme river flooding over large areas (Pall et al., 2019). Extensive snowmelt combined with heavy and/or long-duration precipitation can cause significant floods (D. Li et al., 2019; Krug et al., 2020). Changes in floods in these regions can be uncertain because of the compounding and competing effects of the responses of snow and rain to warming that affect snowpack size: warming results in an increase in precipitation, but also a reduction in the time period of snowfall accumulation (Teufel et al., 2019). An increase in atmospheric CO2 enhances water-use efficiency by plants (Roderick et al., 2015; Milly and Dunne, 2016; Swann et al., 2016; Swann, 2018); this could reduce evapotranspiration and contribute to the maintenance of soil moisture and streamflow levels under enhanced atmospheric CO2 concentrations (Yang et al., 2019). This mechanism would suggest an increase in the magnitude of some floods in the future (Kooperman et al., 2018). But this effect is uncertain as an increase in leaf area index, and vegetation coverage could also result in overall larger water consumption (Mátyás and Sun, 2014; Mankin et al., 2019; Teuling et al., 2019), and there are also other CO2 -related mechanisms that come into play (Cross-Chapter Box 5.1).

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Droughts refer to periods of time with substantially below-average moisture conditions, usually covering large areas, during which limitations in water availability result in negative impacts for various components of natural systems and economic sectors (Wilhite and Pulwarty, 2017; Ault, 2020). Depending on the variables used to characterize it and the systems or sectors being impacted, drought may be classified in different types (Figure 8.6 and Appendix Table 11.A.1) such as meteorological (precipitation deficits), agricultural(e.g., crop yield reductions or failure, often related to soil moisture deficits), ecological (related to plant water stress that causes e.g., tree mortality), or hydrologicaldroughts (e.g., water shortage in streams or storages such as reservoirs, lakes, lagoons, and groundwater; see Glossary). The distinction of drought types is not absolute, as drought can affect different sub-domains of the Earth system concomitantly, but sometimes also asynchronously, including propagation from one drought type to another (Brunner and Tallaksen, 2019). Because of this, drought cannot be characterized using a single universal definition (Lloyd-Hughes, 2014) or directly measured based on a single variable (SREX Chapter 3; Wilhite and Pulwarty, 2017). Drought can happen on a wide range of timescales – from ‘flash droughts’ on a scale of weeks, and characterized by a sudden onset and rapid intensification of drought conditions (Hunt et al., 2014; Otkin et al., 2018; Pendergrass et al., 2020) to multi-year or decadal rainfall deficits – sometimes termed ‘megadroughts’ (see Glossary; Ault et al., 2014; Cook et al., 2016b; Garreaud et al., 2017). Droughts are often analysed using indices that are measures of drought severity, duration and frequency (Sections 8.3.1.6, 8.4.1.6, 12.3.2.6 and 12.3.2.7, and Table 11.A.1). There are many drought indices published in the scientific literature, as also highlighted in SREX (SREX Chapter 3). These can range from anomalies in single variables (e.g., precipitation, soil moisture, runoff, evapotranspiration) to indices combining different atmospheric variables.

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Similar to many other extreme events, droughts occur as a combination of thermodynamic and dynamic processes (Box 11.1). Thermodynamic processes contributing to drought, which are modified by greenhouse gas forcing both at global and regional scales, are mostly related to heat and moisture exchanges, and are also partly modulated by plant coverage and physiology. They affect, for instance, atmospheric humidity, temperature, and radiation, which in turn affect precipitation and/or evapotranspiration in some regions and time frames. However, dynamic processes are particularly important to explain drought variability on different time scales, from a few weeks (flash droughts) to multiannual (megadroughts). There is low confidence in the effects of greenhouse gas forcing on changes in atmospheric dynamic (Section 2.4; Section 4.3.3), and on associated changes in drought occurrence. Thermodynamic processes are thus the main driver of drought changes in a warming climate (high confidence).

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Atmospheric evaporative demand (AED) quantifies the maximum amount of actual evapotranspiration (ET) that can happen from land surfaces if they are not limited by water availability (Table 11.A.1). AED is affected by radiative and aerodynamic components. For this reason, the atmospheric dryness, often quantified with the relative humidity or the vapour pressure deficit (VPD), is not equivalent to the AED, as other variables are also highly relevant, including solar radiation and wind speed (Hobbins et al., 2012; McVicar et al., 2012a; Sheffield et al., 2012). AED can be estimated using different methods (McMahon et al., 2013), and those solely based on air temperature (e.g., Hargreaves, Thornthwaite) usually overestimate it in terms of magnitude and temporal trends (Sheffield et al., 2012), in particular, in the context of substantial background warming. Physically-based combination methods such as the Penman-Monteith equation are more adequate and recommended since 1998 by the United Nations Food and Agriculture Oganization (Pereira et al., 2015). For this reason, the assessment of this Chapter, when considering atmospheric-based drought indices, only includes AED estimates using the latter (see also Section 11.9). AED is generally higher than ET, since AED represents an upper bound for ET. Hence, an AED increase does not necessarily lead to increased ET (Milly and Dunne, 2016), in particular under drought conditions given soil moisture limitation (Bonan et al., 2014; Berg et al., 2016; Konings et al., 2017; Stocker et al., 2018). In general, AED is highest in regions where ET is lowest (e.g., desert areas), further illustrating the decoupling between the two variables under limited soil moisture.

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Although there are many atmospheric-based drought indices, two are assessed in this chapter: the Palmer Drought Severity Index (PDSI) and the Standardized Precipitation Evapotranspiration Index (SPEI). The PDSI has been widely used to monitor and quantify drought severity (Dai et al., 2018), but is affected by some constraints (SREX Chapter 3; Mukherjee et al., 2018a). Although the calculation of the PDSI is based on a soil water budget, the PDSI is essentially a climate drought index that mostly responds to the precipitation and the AED (van der Schrier et al., 2013; Vicente-Serrano et al., 2015; Dai et al., 2018). The SPEI also combines precipitation and AED, being equally sensitive to these two variables (Vicente-Serrano et al., 2015). The SPEI is more sensitive to AED than the PDSI (Cook et al., 2014a; Vicente-Serrano et al., 2015), although under humid and normal precipitation conditions, the effects of AED on the SPEI are small (Tomas-Burguera et al., 2020). Given the limitations associated with temperature-based AED estimates (Section 11.6.1.2), only studies using the Penman-Monteith-based SPEI and PDSI (hereafter SPEI-PM and PDSI-PM) are considered in this assessment and in the regional tables in Section 11.9.

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In summary, different drought types exist and they are associated with different impacts and respond differently to increasing greenhouse gas concentrations. Precipitation deficits and changes in evapotranspiration govern net water availability. A lack of sufficient soil moisture, sometimes amplified by increased atmospheric evaporative demand, result in agricultural and ecological drought. Lack of runoff and surface water result in hydrological drought. Drought events are the result of dynamic and/or thermodynamic processes, with thermodynamic processes being the main driver of drought changes under human-induced climate change (high confidence).

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In summary, human influence has contributed to increases in agricultural and ecological droughts in the dry season in some regions due to increases in evapotranspiration (medium confidence). The increases in evapotranspiration have been driven by increases in atmospheric evaporative demand induced by increased temperature, decreased relative humidity and increased net radiation over affected land areas (high confidence). There is low confidence that human influence has affected trends in meteorological droughts in most regions, butmedium confidence that they have contributed to the severity of some single events. There is medium confidence that human-induced climate change has contributed to increasing trends in the probability or intensity of recent agricultural and ecological droughts, leading to an increase of the affected land area. Human-induced climate change has contributed to global-scale change in low flow, but human water management and land-use changes are also important drivers (medium confidence).

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Agricultural and ecological droughts are primarily assessed based on observed and projected changes in total column soil moisture, complemented by evidence on changes in surface soil moisture, water-balance (precipitation minus evapotranspiration (ET)) and metrics driven by precipitation and atmospheric evaporative demand (AED) such as the SPEI and PDSI (Section 11.6). In the latter, only studies including estimates based on the Penman–Monteith equation (SPEI-PM and PDSI-PM) are considered because of biases associated with temperature-only approaches (Section 11.6). Medium to high confidence in drying was assigned in the assessment for arid regions if a signal was also identifiable in total soil moisture in addition to surface soil moisture or metrics that combine AED and precipitation, which tend to dry more in these regions. For observed changes, evidence is drawn from several sources: Padrón et al. (2020) for changes in precipitation minus ET, as well as soil moisture from the multi-model Land Surface Snow and Soil Moisture Model Intercomparison Project within CMIP6 (11.SM; van Den Hurk et al., 2016); Greve et al. (2014) for changes in precipitation minus ET, and precipitation minus AED; Spinoni et al. (2019) for changes in SPEI-PM; and Dai and Zhao (2017) for changes in PDSI-PM.

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Beguería, S., S.M. Vicente-Serrano, F. Reig, and B. Latorre, 2014: Standardized precipitation evapotranspiration index (SPEI) revisited: Parameter fitting, evapotranspiration models, tools, datasets and drought monitoring. International Journal of Climatology, 34(10), 3001–3023, doi: 10.1002/joc.3887.

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Byrne, M.P. and P.A. O’Gorman, 2015: The Response of Precipitation Minus Evapotranspiration to Climate Warming: Why the “Wet-Get-Wetter, Dry-Get-Drier” Scaling Does Not Hold over Land. Journal of Climate, 28(20), 8078–8092, doi: 10.1175/jcli-d-15-0369.1.

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Chai, R., S. Sun, H. Chen, and S. Zhou, 2018: Changes in reference evapotranspiration over China during 1960–2012: Attributions and relationships with atmospheric circulation. Hydrological Processes, 32(19), 3032–3048, doi: 10. 1002/hyp.13252.

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Chen, H. and J. Sun, 2015b: Changes in Drought Characteristics over China Using the Standardized Precipitation Evapotranspiration Index. Journal of Climate, 28(13), 5430–5447, doi: 10.1175/jcli-d-14-00707.1.

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Condon, L.E., A.L. Atchley, and R.M. Maxwell, 2020: Evapotranspiration depletes groundwater under warming over the contiguous United States. Nature Communications, 11(1), 873, doi: 10.1038/s41467-020-14688-0.

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Gao, X. et al., 2017b: Temporal and spatial evolution of the standardized precipitation evapotranspiration index (SPEI) in the Loess Plateau under climate change from 2001 to 2050. Science of The Total Environment, 595, 191–200, doi: 10.1016/j.scitotenv.2017.03.226.

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Gocic, M. and S. Trajkovic, 2014: Analysis of trends in reference evapotranspiration data in a humid climate. Hydrological Sciences Journal, 59(1), 165–180, doi: 10.1080/02626667.2013.798659.

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Hobbins, M., D. McEvoy, and C. Hain, 2017: Evapotranspiration, Evaporative Demand, and Drought. In: Drought and Water Crises: Integrating Science, Management, and Policy (2nd Edition)[Wilhite, D.A. and R.S. Pulwarty (eds.)]. CRC Press, Boca Raton, FL, USA, pp. 259–287, doi: 10.1201/b22009.

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Jhajharia, D. et al., 2015: Reference evapotranspiration under changing climate over the Thar Desert in India. Meteorological Applications, 22(3), 425–435, doi: 10.1002/met.1471.

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Liuzzo, L., F. Viola, and L. Noto, 2016: Wind speed and temperature trends impacts on reference evapotranspiration in Southern Italy. Theoretical and Applied Climatology, 123(1–2), 43–62, doi: 10.1007/s00704-014-1342-5.

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Maček, U., N. Bezak, and M. Šraj, 2018: Reference evapotranspiration changes in Slovenia, Europe. Agricultural and Forest Meteorology, 260–261, 183–192, doi: 10.1016/j.agrformet.2018.06.014.

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Milly, P.C.D. and K.A. Dunne, 2016: Potential evapotranspiration and continental drying. Nature Climate Change, 6(10), 946–949, doi: 10.1038/nclimate3046.

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Mueller, B. and S. Seneviratne, 2014: Systematic land climate and evapotranspiration biases in CMIP5 simulations. Geophysical Research Letters, 41(1), 128–134, doi: 10.1002/2013gl058055.

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Pereira, L.S., R.G. Allen, M. Smith, and D. Raes, 2015: Crop evapotranspiration estimation with FAO56: Past and future. Agricultural Water Management, 147, 4–20, doi: 10.1016/j.agwat.2014.07.031.

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Piticar, A. et al., 2016: Spatiotemporal distribution of reference evapotranspiration in the Republic of Moldova. Theoretical and Applied Climatology, 124(3–4), 1133–1144, doi: 10.1007/s00704-015-1490-2.

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Shi, L., P. Feng, B. Wang, D.L. Liu, and Q. Yu, 2020: Quantifying future drought change and associated uncertainty in southeastern Australia with multiple potential evapotranspiration models. Journal of Hydrology, 590, 125394, doi: 10.1016/j.jhydrol.2020.125394.

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Tabari, H. and M.-B. Aghajanloo, 2013: Temporal pattern of aridity index in Iran with considering precipitation and evapotranspiration trends. International Journal of Climatology, 33(2), 396–409, doi: 10.1002/joc.3432.

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Teuling, A.J. et al., 2013: Evapotranspiration amplifies European summer drought. Geophysical Research Letters, 40(10), 2071–2075, doi: 10. 1002/grl.50495.

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Teuling, A.J. et al., 2019: Climate change, reforestation/afforestation, and urbanisation impacts on evapotranspiration and streamflow in Europe. Hydrology and Earth System Sciences, 23, 3631–3652, doi: 10.5194/hess-2018-634.

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Tomas-Burguera, M. et al., 2020: Global characterization of the varying responses of the Standardized Evapotranspiration Index (SPEI) to atmospheric evaporative demand (AED). Journal of Geophysical Research: Atmospheres, 125, e2020JD0330178, doi: 10.1029/2020jd033017.

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Vicente-Serrano, S.M. et al., 2015: Contribution of precipitation and reference evapotranspiration to drought indices under different climates. Journal of Hydrology, 526, 42–54, doi: 10.1016/j.jhydrol.2014.11.025.

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Yu, M., Q. Li, M.J. Hayes, M.D. Svoboda, and R.R. Heim, 2014: Are droughts becoming more frequent or severe in China based on the Standardized Precipitation Evapotranspiration Index: 1951–2010?International Journal of Climatology, 34(3), 545–558, doi: 10.1002/joc.3701.

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As shown in Figure 8.1, the global water cycle is the continuous, naturally occurring movement of water through the climate system from its liquid, solid and gaseous forms among reservoirs of the ocean, atmosphere, cryosphere and land (Stocker et al., 2013). In the atmosphere, water primarily occurs as a gas (water vapour), but it is also present as ice and liquid water within clouds where it substantially affects Earth’s energy balance (Sections 7.4.2.2 and 7.4.2.4). The water cycle primarily involves the evaporation1 and precipitation of moisture at the Earth’s surface including transpiration associated with biological processes. Water that falls on land as precipitation, supplying soil moisture, groundwater recharge, and river flows, was once evaporated from the ocean or sublimated from ice-covered regions before being transported through the atmosphere as water vapour, or in some areas was generated over land through evapotranspiration (Gimeno et al., 2010; van der Ent and Savenije, 2013). In addition, the net flux of atmospheric and continental freshwater is a key driver of sea surface salinity, which in turn influences the density and circulation of the ocean (Chapter 9).

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(Chapter 8 has multiple links across all AR6 WGI chapters, so necessarily includes references to other chapter subsections and figures. Model evaluation of large-scale circulation and precipitation is mostly covered by Chapter 3, while hydrological extremes are covered by Chapter 11. Chapter 8 focuses on key processes relevant to the water cycle and their resolution-dependent representation in models. Observed and projected changes in large-scale circulation and precipitation are primarily assessed in Chapters 2, 3 and 4. Beyond global and regional mean precipitation amounts, Chapter 8 also focuses on other precipitation properties (e.g., frequency, intensity and seasonality) and other water cycle variables (evapotranspiration, runoff, soil moisture and aridity, solid and liquid freshwater reservoirs). Key regional phenomena (e.g., tropical overturning circulations, monsoons, extratropical stationary waves and storm tracks, modes of variability and related teleconnections) are also assessed given their major dynamical contribution to regional water cycle changes. Although the biosphere and the cryosphere are key components of the water cycle, a more comprehensive assessment of their responses can be found in Chapters 5 and 9, respectively. Further assessment on regional water cycle changes can be found in Chapters 10 to 12 and in the Atlas. The reader is also referred to the interactive (Atlas for a more detailed assessment of the range of model biases and responses at the regional scale. Beyond WGI, water is also a major topic for both adaptation and mitigation policies so has strong connections with both WGII and WGIII. Assessment of hydrological impacts at basin and catchment scales, including a broader discussion on adaptation and vulnerability, potential threats to water security, societal responses, improving resilience in water systems and related case studies is provided in WGII (Chapter 4).

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Vegetation is a crucial interface between subsurface water storage (in soil moisture and groundwater) and the atmosphere. Plants alter evapotranspiration and the surface energy balance, and thus can have a large influence on regional aridity (Lemordant et al., 2018). SRCCL concluded there is high confidence that higher atmospheric CO2 increases the ratio of plant CO2 uptake to water loss (water-use efficiency; WUE) through the combined enhancement of photosynthesis and stomatal regulation (Section 5.4.1; DeKauwe et al. , 2013; C.D. Jones et al. , 2013; Deryng et al. , 2016; Swann et al. , 2016; Cheng et al. , 2017; Knauer et al. , 2017; Peters et al. , 2018; Guerrieri et al. , 2019). Modelling studies suggest that increasing WUE can partly counteract water losses from increased evaporative demand in a warmer atmosphere, potentially mitigating aridification (Milly and Dunne, 2016; Bonfils et al. , 2017; Cook et al. , 2018; Y. Yang et al. , 2018). However, observational studies suggest that this effect may be counter-balanced by the increase in plant growth in response to elevated CO2, which results in increased water consumption (De Kauwe et al. , 2013; Donohue et al. , 2013; Ukkola et al. , 2016b; Yang et al. , 2016; Guerrieri et al. , 2019; Mankin et al. , 2019; A. Singh et al. , 2020). In semi-arid regions, increased plant water consumption can reduce streamflow and exacerbate aridification (Ukkola et al. , 2016b; Mankin et al. , 2019; A. Singh et al. , 2020). Thus, there is low confidence that increased WUE in plants can counterbalance increased evaporative demand (Cross-Chapter Box 5.1).

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A drought is a period of abnormally dry weather that persists for long enough to cause a serious hydrological imbalance (Glossary; Wilhite and Glantz, 1985; Wilhite, 2000; Cook et al., 2018). Most droughts begin as persistent precipitation deficits (‘meteorological drought’) that propagate over time into deficits in soil moisture, streamflow, and water storage (Figure 8.6), leading to a reduction in water supply (‘hydrological drought’). Increased atmospheric evaporative demand increases plant water stress, leading to ‘agricultural and ecological drought’ (Williams et al. , 2013; C.D. Allen et al. , 2015; Anderegg et al. , 2016; McDowell et al. , 2016; Grossiord et al. , 2020). Evaporative demand affects plants in two ways. It increases evapotranspiration, depleting soil moisture and stressing plants through lack of water (Teuling et al., 2013; Sperry et al., 2016), and also directly affects plant physiology, causing a decline in hydraulic conductance and carbon metabolism, leading to mortality (Figure 8.6; Breshears et al., 2013; Hartmann, 2015; McDowell and Allen, 2015; Fontes et al., 2018). While droughts are traditionally viewed as ‘slow moving’ disasters that typically take months or years to develop, rapidly evolving and often unpredictable flash droughtscan also occur (Otkin et al., 2016, 2018). Flash droughtscan develop within a few weeks, causing substantial disruption to agriculture and water resources (Pendergrass et al., 2020). Conversely, droughts that persist for a long time (usually a decade or more) are called megadroughts. Droughts span a large range of spatial and temporal scales, arise through a variety of climate system dynamics (e.g., internal atmospheric variability, ocean teleconnections), and can be amplified or alleviated by a variety of physical and biological processes. As such, droughts occupy a unique space within the framework of extreme climate and weather events, possessing no singular definition.

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While the role of precipitation in droughts is obvious, other climatic drivers are also important, such as temperature, radiation, wind, and humidity (Figure 8.6). These factors have a strong influence on atmospheric evaporative demand, which affects evapotranspiration and soil moisture (Figure 8.6). In snow-dominated regions, high temperatures increase the fraction of precipitation falling as rain instead of snow and advance the timing of spring snowmelt (high confidence) (Vincent et al. , 2015; Mote et al. , 2016, 2018; Berg and Hall, 2017; Solander et al. , 2018). This can result in lower than normal snowpack levels (a ‘snow drought’), and thus reduced streamflow, even if total precipitation is at or above normal for the cold season (Harpold et al., 2017). Plants also affect the severity of droughts by modulating evapotranspiration (Figure 8.6). As discussed above, the effect of elevated CO2 on plants has the potential to both increase and reduce water loss through evapotranspiration via enhanced WUE and plant growth, respectively (Figure 8.6), but there is low confidence in whether one process dominates over another at the global scale.

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In summary, there is high confidence that a warming climate drives an increase in atmospheric evaporative demand, decreasing available soil moisture. There is high confidence that higher atmospheric CO2 increases plant water-use efficiency, but low confidence that this physiological effect can counterbalance water losses. Since drought can be defined in a number of ways, there are potentially different responses under a warming climate depending on drought type. Beyond a lack of precipitation, changes in evapotranspiration are critical components of drought, because these can lead to soil moisture declines (high confidence). Under very dry soil conditions, evapotranspiration becomes restricted and plants experience water stress in response to increased atmospheric demand (medium confidence). Human activities and decision-making have a critical impact on drought severity (high confidence).

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The AR5 assessed that there was medium confidence that pan evaporation declined in most regions over the last 50 years, yetmedium confidence that evapotranspiration increased from the early 1980s to the late 1990s. Since AR5, these conflicting observations have been attributed to internal variability and by the fact that evapotranspiration is less sensitive to trends in wind speed and is partly controlled by vegetation greening (K. Zhang et al., 2015; Y. Zhang et al., 2016; Z. Zeng et al., 2018b). Observation-based estimates show a robust positive trend in global terrestrial evapotranspiration between the early 1980s and the early 2010s (Miralles et al., 2014b; Z. Zeng et al., 2014, 2018b; K. Zhang et al., 2015; Y. Zhang et al., 2016). The rate of increase varies among datasets, with an ensemble mean terrestrial average rate of 7.6 ± 1.3 mm yr1 per decade for 1882–2011 (Z. Zeng et al., 2018a). In addition, a decreasing trend in pan evaporation plateaued or reversed after the mid-1990s (C.M. Stephens et al., 2018) has been reported as due to a shift from a dominant influence of wind speed to a dominant effect of water vapour pressure deficit, which has increased sharply since the 1990s (Yuan et al., 2019). The absence of a trend in evapotranspiration in the decade following 1998 was shown to be at least partly an episodic phenomenon associated with ENSO variability (Miralles et al. , 2014b; K. Zhang et al. , 2015; Martens et al. , 2018). Thus, there is medium confidence that the apparent pause in the increase in global evapotranspiration from 1998 to 2008 is mostly due to internal variability. In contrast to AR5, there are now consistent trends in pan evaporation and evapotranspiration at the global scale, given the recent increase in both variables since the mid-1990s (medium confidence). Given the growing number of quantitative studies, there is high confidence that global terrestrial annual evapotranspiration has increased since the early 1980s.

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Since AR5, the predominant contribution of transpiration to the observed trends in terrestrial evapotranspiration has been revisited and confirmed (Good et al., 2015; Wei et al., 2017). Using satellite and ecosystem models, Zhu et al. (2016) found a positive trend in leaf area index during 19822009, indicating that greening could contribute to the observed positive trend of evapotranspiration, in line withsimilar studies that focused on the 1981–2012(Y. Zhanget al. , 2016) and1982–2013(K. Zhanget al. , 2015) periods. Zeng et al. (2018) determined that the 8% global increase in satellite-observed leaf area index between the 1980s and the 2010s may explain an increase in evapotranspiration of 12.0 ± 2.4 mm yr–1 (about 55 ± 25% of the total observed increase). Forzieri et al. (2020) estimated that the recent increase in leaf area index led to 3.66 ± 0.45 W m–2 in latent heat flux (about 51 ± 6 mm yr–1) and that the sensitivity of energy fluxes to leaf area index increased by about 20% over the 1982–2016 period. Overall, there is medium confidence that greening has contributed to the global increase in evapotranspiration since the 1980s.

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Plant water use efficiency(WUE)is expected to rise with CO2levels (high confidence) (Section 8.2.3.3 and Box 5.2), andcan in theory counteract rising evapotranspiration in a warmer atmosphere (Section 8.2.3.3). However, observational studies suggest that this may not be the case in some ecosystems. For example, Frank et al. (2015) found that while the WUE increased in European forests across the 20th century, transpiration also increased due to more plant growth, a lengthened growing season, and increased evaporative demand. Likewise Guerrieri et al. (2019) observed that while WUE and photosynthesis increased in North American forests, stomatal conductance experienced only modest declines that were restricted to moisture-limited forests. Other studies further suggest that in many ecosystems increased WUE will not compensate for increased plant growth, amplifying declines in surface water availability(De Kauweet al. , 2013; Ukkolaet al. , 2016b; A. Singhet al. , 2020), while drought conditions can also offset the CO2 fertilization effect and lead to a decline in WUE (N. Liu et al., 2020). There is low confidence regarding the impact of plant physiological effects on observed trends in evapotranspiration.

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Since AR5, there is a growing body of evidence suggesting that future projections in evapotranspiration are driven by changes in temperature and relative humidity (Laîné et al. , 2014; Pan et al. , 2015; Ukkola et al. , 2016a), as well as precipitation patterns, as found in AR5.

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CMIP6 models project a geographical pattern of changes in evapotranspiration similar to previous generation models (Figure 8.17), although the magnitude is generally larger than found for CMIP5 projections (X. Liu et al., 2020). There is, however, a strong seasonality in many regions, with a larger relative increase in the winter season of the Northern Hemisphere (NH) and smaller relative changes in the summer (Figure 8.17). Evapotranspiration increases in most land regions, except in areas that are projected to become moisture-limited (due to reduced precipitation and increased evaporative demand), such as the Mediterranean, South Africa, and the Amazonian basin (medium confidence). The patterns of change increase in magnitude from low to high-emissions SSP scenarios (medium confidence).

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Although there are regions where multiple models predict consistent and significant changes in soil moisture, as with evapotranspiration (Section 8.4.1.4), there is still uncertainty in these projections related to the response of plants to elevated CO2. Most models project increases in two variables that have opposite effects on surface water availability: plant water use efficiency (WUE) and leaf area index (LAI; see Section 8.4.1.4). As discussed in Sections 8.2.3.3, 8.3.1.4 and 8.4.1.4, there is low confidence in how these changes in plant physiology will affect future projections of evapotranspiration, and likewise, drought and aridity.

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In summary, based on the results of Chapter 9, it is nowvirtually certain that future NH snow cover extent and duration will continue to decrease with global warming. While most studies have focused on the NH, process understanding suggests with high confidence that these results apply to the Southern Hemisphere (SH) as well. There is high confidence in snowmelt occurring earlier in the year. Changes to the timing and amount of snowmelt will have a strong influence on all the other aspects of the water cycle in regions with seasonal snow, including run-off, soil moisture, and evapotranspiration.

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The physiological response of plants to increasing atmospheric CO2 is generally accounted for, but only using empirical models of stomatal conductance that are characterized by a single critical parameter of intrinsic water-use efficiency (Franks et al., 2017, 2018). This reflects a lack of structural diversity and caution about the consensus of the photosynthesis response to increasing CO2 (Knauer et al., 2015; Huang et al., 2016), which has implications for the ability of the current-generation models to account for uncertainty in future evapotranspiration changes. Most CMIP5 models underestimate the ratio of plant transpiration to total terrestrial evapotranspiration, which may suggest that they also underestimate the impact of plant physiology on the water cycle (Lian et al., 2018). Plant hydraulics are not explicitly considered in many land surface models, which may lead to an underestimation of the influence of the increasing atmospheric moisture stress on plant transpiration under climate change (Massmann et al. , 2019; Grossiord et al. , 2020; Y. Liu et al. , 2020). Most ESMs underestimate the water use efficiency measured at many sites and, consequently overestimate the ratio of evapotranspiration to precipitation (J. Li et al., 2018).

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Beyond changes in land surface water fluxes, non-linearities in the response of soil moisture and freshwater reservoirs have not been well documented in global climate projections but deserve further attention given the complex interactions between the water, energy and carbon cycles (Berg and Sheffield, 2018a), the growing direct human influence on rivers and groundwater (Abbott et al., 2019), and a possible offset between the linear components of changes in precipitation and evapotranspiration. Significant non-linearities were found in water scarcity projections, as seen by the stronger sensitivity to the first 2°C increase in global warming (Gosling and Arnell, 2016).

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Zhang, K. et al., 2015: Vegetation Greening and Climate Change Promote Multidecadal Rises of Global Land Evapotranspiration. Scientific Reports, 5(1), 15956, doi: 10.1038/srep15956.

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Zhang, Y. et al., 2016: Multi-decadal trends in global terrestrial evapotranspiration and its components. Scientific Reports, 6, 1–12, doi: 10.1038/srep19124.

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Fisher, J.B. et al., 2017: The future of evapotranspiration: Global requirements for ecosystem functioning, carbon and climate feedbacks, agricultural management, and water resources. Water Resources Research, 53(4), 2618–2626, doi: 10.1002/2016wr020175.

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By the end of the 21st century, the overall nitrogen fixation in non-agricultural ecosystems could be 40% larger than in 2000, due to increased enzyme activity with growing temperatures, but the emission rates of NO (and N2O) could be dominated by changes in precipitation patterns and evapotranspiration fluxes (Fowler et al., 2015). Current Earth system models (ESMs) incorporate biophysical and biogeochemical processes only to a limited extent (Jia et al., 2019), precluding adequate climate sensitivity studies for SNOx. Hence, while the current strength source of soil NOx has been better constrained over the last decade, adequate representations of SNOx and how it escapes from the canopy, which could provide quantitative estimates of climate-driven changes in SNOx, are still missing in ESMs.

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VanLoocke, A., A.M. Betzelberger, E.A. Ainsworth, and C.J. Bernacchi, 2012: Rising ozone concentrations decrease soybean evapotranspiration and water use efficiency whilst increasing canopy temperature. New Phytologist, 195(1), 164–171, doi: 10.1111/j.1469-8137.2012.04152.x.

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Turbulent fluxes of latent and sensible heat are also an important part of the surface energy budget (Figure 7.2). Large uncertainties in measurements of surface turbulent fluxes continue to prevent the determination of their decadal changes. Nevertheless, over the ocean, reanalysis-based estimates of linear trends from 1948–2008 indicate high spatial variability and seasonality. Increases in magnitudes of 4 to 7 W m–2 per decade for latent heat and 2 to 3 W m–2 per decade for sensible heat in the western boundary current regions are mostly balanced by decreasing trends in other regions (Gulev and Belyaev, 2012). Over land, the terrestrial latent heat flux is estimated to have increased in magnitude by 0.09 W m–2 per decade from 1989–1997, and subsequently decreased by 0.13 W m–2 per decade from 1998–2005 due to soil-moisture limitation mainly in the Southern Hemisphere (derived from Mueller et al., 2013). These trends are small in comparison to the uncertainty associated with satellite-derived and in situ observations, as well as from land-surface models forced by observations and atmospheric reanalyses. Ongoing advances in remote sensing of evapotranspiration from space (Mallick et al., 2016; Fisher et al., 2017; McCabe et al., 2017a, b), as well as terrestrial water storage (Rodell et al., 2018) may contribute to future constraints on changes in latent heat flux.

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Fisher, J.B. et al., 2017: The future of evapotranspiration: Global requirements for ecosystem functioning, carbon and climate feedbacks, agricultural management, and water resources. Water Resources Research, 53(4), 2618–2626, doi: 10.1002/2016wr020175.

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Mueller, B. et al., 2013: Benchmark products for land evapotranspiration: LandFlux-EVAL multi-data set synthesis. Hydrology and Earth System Sciences, 17(10), 3707–3720, doi: 10.5194/hess-17-3707-2013.

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In contrast to the global glacier mass decline (Figure 9.21, Table 9.5, and Supplementary Material 9.SM.2), a few glaciers have gained mass or advanced due to internal glacier dynamics or locally restricted climatic causes. The SROCC discusses the ‘Karakoram anomaly’ (centred on the western Kunlun range (at about 80°E, 35°N), but also covering part of the Pamir and Karakoram ranges), where glaciers have been close to balance since at least the 1970s, and had a slightly positive mass balance since the 2000s. Since SROCC, new evidence suggests that this anomaly is related to a combination of low-temperature sensitivity of debris-covered glaciers, a decrease of summer air temperatures (Cross-Chapter Box 10.3), and an increase in snowfall, possibly caused by increases in evapotranspiration from irrigated agriculture (Bonekamp et al., 2019; de Kok et al., 2020; Farinotti et al., 2020; Shean et al., 2020). However, a recent geodetic mass balance estimate suggests substantially increased thinning rates of High Mountain Asian glaciers after about 2010 (Hugonnet et al., 2021). There is limited evidence to assess whether the Karakoram anomaly will persist in coming decades but, due to the projected increase in air temperature throughout the region, its long-term persistence is unlikely (high confidence) (Cross-Chapter Box 10.3; Kraaijenbrink et al., 2017; de Kok et al., 2020; Farinotti et al., 2020).

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Aridity indices may track long-term changes in precipitation, evapotranspiration demand, surface water, groundwater or soil moisture (Sherwood and Fu, 2014; Herrera-Pantoja and Hiscock, 2015; B.I. Cook et al., 2020). Changes in soil moisture and surface water can shift the rate of carbon uptake by ecosystems (Humphrey et al., 2018) and alter suitable climate zones for wild species and agricultural cultivation (Feng and Fu, 2013; Garcia et al., 2014; Huang et al., 2016a; Schlaepfer et al., 2017; Fatemi et al., 2018; IPCC, 2019c) as well as the prevalence of related pests and pathogen-carrying vectors (Paritsis and Veblen, 2011; Smith et al., 2020). Water table depth, in relation to rooting depth, is also important for farms and forests under dry conditions (Feng et al., 2006). A reduction in water availability (via aridity or hydrological drought) challenges water supplies needed for for municipal, industrial, agriculture and hydropower use (Schaeffer et al., 2012; Arnell and Lloyd-Hughes, 2014; Schewe et al., 2014; Gosling and Arnell, 2016; van Vliet et al., 2016).

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Agricultural and ecological drought indices relate to the ability of plants to meet growth and transpiration needs (Table 11.3; Zargar et al., 2011; Lobell et al., 2015; Pedro-Monzonís et al., 2015; Bachmair et al., 2016; Wehner et al., 2017; Naumann et al., 2018) and the timing and duration of droughts can lead to substantially different impacts (Peña-Gallardo et al., 2019). Drought stress for agriculture and ecosystems is difficult to directly observe, and therefore scientists use a variety of drought indices (Table 11.3), proxy information about changes in precipitation supply and reference evapotranspiration demand, the ratio of actual/potential evapotranspiration or a deficit in available soil water content, particularly at rooting level (Park Williams et al., 2013; Trnka et al., 2014; C.D. Allen et al., 2015; Svoboda and Fuchs, 2017; Mäkinen et al., 2018; Otkin et al., 2018). Severe water stress can lead to crop failure, in particular when droughts persist for an extended period or occur during key plant developmental stages (Hatfield et al., 2014; Jolly et al., 2015; Leng and Hall, 2019). Projections of high wind speed and low humidity (even for just a portion of the day) can also inform studies examining fruit desiccation and rice cracking (Grotjahn, 2021). Drought also raises disease infection rates for West Nile virus (Paull et al., 2017), and the alternation of dry and wet spells induces swelling and shrinkage of clay soils that can lead to sinkholes and destabilize buildings (Hadji et al., 2014).

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Changes in surface solar and longwave radiation fluxes alter photosynthesis rates and potential evapotranspiration for natural ecosystems and food, fibre and energy crops (Mäkinen et al., 2018); changes in radiation fluxes can also shift solar energy resources (Schaeffer et al., 2012; Jerez et al., 2015; Wild et al., 2015; Fant et al., 2016; Craig et al., 2018). Plants and aquatic systems particularly respond to changes in photosynthetically active radiation (PAR) and the fraction of diffuse radiation (Proctor et al., 2018; Ren et al., 2018; Ryu et al., 2018). Increases in ultraviolet radiation can also detrimentally affect ecosystems and human health (Barnes et al., 2019).

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Studies examining a 2°C GWL give low confidence for projected broad changes to agricultural and ecological drought across all Asia regions, although at 4°C GWL agricultural and ecological drought increases are projected for West Central Asia and East Asia along with a decrease in South Asia (medium confidence) (Section 11.9). Summer temperature increase will enhance evapotranspiration, facilitating ecological and agricultural drought over Central Asia towards the latter half of this century (Chapter 11; see also Figure 12.4 for soil moisture and DF indices; Ozturk et al., 2017; Reyer et al., 2017b; Senatore et al., 2019). However, broader changes in droughts could not be determined in Asia due to the mixture of total precipitation signals together with temperature increase patterns (Section 11.9 and Atlas.5).

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Agricultural and ecological drought: There is medium confidence in observations of agricultural and ecological droughts increasing in SAU and decreasing in NAU, while there is low confidence of changes elsewhere in the region (Section 11.9). More regional studies have observed an increase in agricultural and ecological drought intensity in south-west Australia and an increase in drought intensity in parts of south-east Australia, while the length of droughts therein has increased (Section 11.9). In New Zealand, since 1972–73, soils at 7 of 30 monitored sites became drier, while the 2012–13 drought was one of the most extreme in the previous 41 years (MfE and Stats NZ, 2017). Future evaporative demand is projected to lead to medium confidence increases in agricultural and ecological droughts for 2°C of global warming in SAU and EAU and low confidence for changes in CAU, NAU and NZ, although there is medium confidence of increases in CAU with 4°C of global warming (Section 11.9). There is medium confidence for more time in agricultural and ecological drought in SAU by mid-21st century (Coppola et al., 2021b) as well as by the end of the 21st century (Herold et al., 2018). The Standardized Precipitation Evapotranspiration Index (SPEI) shows a springtime intensification in SAU with moderate and severe droughts in the south-west and moderate droughts in the south-east (Herold et al., 2018). There is consensus among the different model ensembles (CORDEX-CORE, CMIP5 and CMIP6) that the drought frequency (DF), one of several proxies for agricultural and ecological drought, will increase in all four Australian regions for both mid-century (NAU 0.2–2 DF increase, CAU 0.5–2 DF increase, SAU 1–3 DF increase and EAU 0.8–3 DF increase) and end-century (0.8–2.7 DF increase for NAU, 1.2–2 DF increase for CAU, 2.2–3.8 for SAU and 0.2–3 for EAU) for both RCP8.5 and SSP5-8.5, with CMIP6 showing the lowest increase (Figure 12.4g–l and Figure 12.SM.4; Coppola et al., 2021b).

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The opposite tendency is projected in Northern, Eastern and central Europe where higher precipitation that outweighs the effects of increased evapotranspiration is expected to result in a decrease in streamflow drought frequency (Forzieri et al., 2014). For a 2°C GWL droughts will become more intense in the MED and in France and longer mainly due to less rainfall and higher evapotranspiration. A reduction of drought length and magnitude is projected for NEU and EEU (Roudier et al., 2016). In the southern Alps, both winter and summer low flows are projected to be more severe, with a 25% decrease in the 2050s (Vidal et al., 2016).

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Aridity: Current estimates identify many small islands as being under water stress and thus particularly sensitive to variations in rainfall and groundwater, population growth and demand, and land-use change, among others (Cross-Chapter Box Atlas.2; Holding et al., 2016). From 1950 to 2016, a heterogeneous but prevalent drying trend is found in CAR (low confidence), where drought variability is modulated by the tropical Pacific and North Atlantic oceans (Table 11.15 and Cross-Chapter Box Atlas.2, Table 1; Herrera and Ault, 2017). In the future, increased aridity and decreased freshwater availability are projected in many small islands due to higher evapotranspiration in a warmer climate that partially offsets increases or exacerbates reductions in precipitation (Karnauskas et al., 2016, 2018b; Hoegh-Guldberg et al., 2018). Increased aridity is projected for the majority of the small islands, such as in CAR, southern Pacific and western Indian Ocean, by 2041–2059 relative to 1981–1999 under RCP8.5 or at 1.5°C and 2°C GWLs, which will further intensify by 2081–2099 (medium confidence) (Karnauskas et al., 2016, 2018b). Groundwater recharge is projected to increase in Maui, Hawaii except on the leeward side of the island, which underscores the importance of topography and elevation on freshwater availability in different island microclimates (Brewington et al., 2019; Mair et al., 2019).

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Agricultural and ecological drought: Recent trends toward more frequent and severe droughts have been noted in the small islands but only with low confidence in broad trend patterns, given high spatial variability including heightened drought on the leeward side of islands (e.g., Frazier and Giambelluca, 2017; Herrera and Ault, 2017; McGree et al., 2019; see Table 11.15, Cross-Chapter Box Atlas.2, Table 1). Agricultural and ecological droughts are projected to increase in frequency, duration, magnitude, and extent in small islands, such as in CAR (medium confidence) and parts of the Pacific (low confidence), particularly where future declines in precipitation are compounded by higher evapotranspiration, under increasing levels of warming (Naumann et al., 2018; Taylor et al., 2018; Vichot-Llano et al., 2021). Relative to the period 1985–2014, decreases in annual surface and total column soil moisture become more robust in more areas in CAR by 2071–2100 under SSP3-7.0 and SSP5-8.5 scenarios (B.I. Cook et al., 2020), but reliably representing drought features in small island domains with global simulations is challenging (see also Section 11.9).

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Observed and projected rainfall trends vary spatially across the small islands. Higher evapotranspiration under a warming climate are projected to partially offset future increases or amplify future reductions in rainfall, resulting in drier conditions and increased water stress in the small islands (medium confidence).

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Fire weather: Fire season lengthened from 1979 to 2015 over Arctic portions of North America (Jain et al., 2017), corresponding also to a 1975–2015 increase in lightning-ignited fires in Arctic North-Western North America (Girardin et al., 2013; Veraverbeke et al., 2017). Abatzoglou et al. (2019) climate model simulations project significant fire weather index increases in boreal forests of Arctic Europe, Arctic Russia and Arctic North-Eastern North America (medium confidence). Trends towards more frequent fires in tundra regions are expected to continue, driven in particular by increasing potential evapotranspiration and changes in vegetation (high confidence) (Hu et al., 2015; AMAP, 2017; Young et al., 2017).

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In the wake of the El Niño-induced 2015–2016 severe drought, which caused major damage to agricultural land in the Central Highlands of Vietnam, the WEIDAP project was initiated to improve water productivity of irrigated agriculture. Proposed project interventions include a package of both ‘soft’ (e.g., policy, institutional and capacity building, on-farm water efficiency practices) and ‘hard’ (modernized irrigation schemes) activities. To ensure that the project delivers expected benefits under a changing climate, consultants were recruited to carry out a detailed CRA, working as part of the overall project processing team. Through extensive consultations with the rest of the project team and review of literature including relevant climate projections, the CRA consultants chose to construct three broad climate scenarios for the 2050s (a time frame appropriate for the lifetime of the irrigation schemes being proposed under the project): a warm-and-wet, a hot-and-wet, and a hotter future. Outputs from a selection of CMIP5 models were analysed under these three scenarios, to derive changes in temperature, rainfall and potential evapotranspiration, which in turn were used as inputs to hydrological, crop and agro-economics models to assess the impacts of climate change on the overall project performance. Table 1 presents the summary of the key parameters under the three scenarios. Recommendations from the CRA included (largely minor) refinements and additional activities for drought planning, detailed engineering design of the relevant project components (such as access roads, river crossings and foundations), and support for poorer farmers who may not be able to afford access to water and climate-resilient technologies.

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Cross-Chapter Box 12.2, Table 1 | Summary of annual province-level changes in temperature, precipitation and evapotranspiration under the three broad scenarios in southern Vietnam. Scenario 1: warm-and-wet; Scenario 2: hot-and-wet; Scenario 3: hotter. Source: Table 3 in ADB (2020).

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ΔT = change in temperature; ΔP = change in precipitation; ΔPET = change in potential evapotranspiration.

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Feng, T., T. Su, R. Zhi, G. Tu, and F. Ji, 2019: Assessment of actual evapotranspiration variability over global land derived from seven reanalysis datasets. International Journal of Climatology, 39(6), 2919–2932, doi: 10.1002/joc.5992.

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Tam, B.Y. et al., 2019: CMIP5 drought projections in Canada based on the Standardized Precipitation Evapotranspiration Index. Canadian Water Resources Journal, 44(1), 90–107, doi: 10.1080/07011784.2018.1537812.

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Wu, M. et al., 2020: Spatiotemporal variability of standardized precipitation evapotranspiration index in mainland China over 1961–2016. International Journal of Climatology, 40(11), 4781–4799, doi: 10.1002/joc.6489.

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Yao, N. et al., 2020: Projections of drought characteristics in China based on a standardized precipitation and evapotranspiration index and multiple GCMs. Science of The Total Environment, 704, 135245, doi: 10.1016/j.scitotenv.2019.135245.

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Cocoa grown under shade in some situations may be more resilient to climate change (Schwendenmann et al., 2010; Schroth et al., 2016). Schwendenmann et al. (2010) implemented drought experimentally in the field and found shade trees increased drought resilience. Shade trees insulate the understory crop from the warming and drying sun (Schroth et al., 2016). On the other hand, full-sun cocoa systems may be more climate resilient in some cases (Abdulai et al., 2018), as interactions between understory trees and shade trees are complex; in addition to shade effects, evapotranspiration and root interactions must be considered (Niether et al., 2017; Wartenberg et al., 2020). Moving to a full-sun system may also involve additional inputs in irrigation, fertilizer and labour. Neither (2020) reviewed the literature comparing the two cocoa production systems and concluded that the agroforestry system was superior in terms of climate adaptation.

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Ágreda, T., et al., 2015: Increased evapotranspiration demand in a Mediterranean climate might cause a decline in fungal yields under global warming. Glob. Chang. Biol. , 21 (9), 3499–3510, doi:10.1111/gcb.12960.

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Lian et al. (2020) observed that earlier spring leaf-out in the Northern Hemisphere is causing increases in evapotranspiration that are not fully compensated by increased precipitation. The consequence is a greater soil moisture deficit in summer, expected to exacerbate impacts of heat waves as well as drought stress. In Arctic freshwater ecosystems, Heim et al. (2015) demonstrated the importance of seasonal cues for fish migration, which can be impacted by climate change due to reduced stream connectivity and fragmentation, earlier peak flows and increased evapotranspiration.

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The regions containing MTEs all show high confidence in projected increases in the intensity and frequency of hot extremes and decreases in the intensity and frequency of cold extremes, and medium confidence in increasing ecological drought due to increased evapotranspiration (in all regions) and reduced rainfall (excluding California, USA, where model agreement is low) (see WGI Chapter 11). Projections also show a robust increase in the intensity and frequency of heavy precipitation in the event of ≥2°C warming for MTEs in South Africa, the Mediterranean Basin and California, USA, but are less clear for Australia and Chile (Seneviratne et al., 2021).

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In Amazon rainforests, the relatively lower buffering capacity for plant moisture during drought increases the risk of tree mortality and, combined with increased heat from climate change and fire from deforestation, the possibility of a tipping point of extensive forest dieback and a biome shift to grassland (Oyama and Nobre, 2003; Sampaio et al., 2007; Lenton et al., 2008; Nepstad et al., 2008; Malhi et al., 2009; Salazar and Nobre, 2010; Settele et al., 2014; Lyra et al., 2016; Zemp et al., 2017b; Brando et al., 2020). This could occur at a 4°C–5°C temperature increase above that of the pre-industrial period (Salazar and Nobre, 2010). Under RCP8.5, half the Amazon tropical evergreen forest could turn into grassland through drought-induced tree mortality and wildfire, but lower emissions (RCP4.5) could limit this loss to ~5% (Lyra et al., 2016). The decline in precipitation due to reduced evapotranspiration inputs after forest loss could cause additional Amazon forest loss of one-quarter to one-third (Zemp et al., 2017a). Similarly, in Guinean tropical deciduous forest in Africa, climate change under RCP8.5 could increase mortality 700% by 2100 or 400% under lower emissions (RCP4.5; (Claeys et al., 2019). These projections indicate risks of climate change-induced tree mortality reducing tropical forest areas in Africa and South America by up to half under a 4°C increase above the pre-industrial period, but a lower projection of a 2°C increase could limit the projected increases in tree mortality (robust evidence, high agreement ).

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Temperate and boreal forests possess greater diversity of physiological traits related to plant hydraulics, so they are more buffered against drought than tropical forests (Anderegg et al., 2018). Nevertheless, in temperate forests, drought-induced tree mortality under RCP8.5 could cause the loss of half the Northern Hemisphere conifer forest area by 2100 (McDowell et al., 2016). In the western USA, under RCP8.5, one-tenth of forest area is highly vulnerable to drought-induced mortality by 2050 (Buotte et al., 2019). In California, increased evapotranspiration in Sierra Nevada conifer forests increases the potential fraction of the area at risk of tree mortality by 15–20% per degree Celsius (Goulden and Bales, 2019). In Alaska, fire-induced tree mortality from climate change under RCP8.5 could reduce the extent of spruce forest (Picea sp.) by 8–44% by 2100 (Pastick et al., 2017). Under RCP8.5, tree mortality from drought, wildfire and bark beetles could reduce the timber productivity of boreal forests in Canada by 2100 below the current levels (Boucher et al., 2018; Chaste et al., 2019; Brecka et al., 2020). In Tasmania, projected increases in wildfire (Fox-Hughes et al., 2014) increase the risk of mortality of mesic vegetation (Harris et al., 2018b) and threaten the disappearance of the long-lived endemic pencil pine (Athrotaxis cupressoides) (Holz et al., 2015; Worth et al., 2016) and temperate montane rainforest (Mariani et al., 2019). These projections indicate risks of climate change-induced tree mortality reducing some temperate forest areas by half under emissions scenarios of 2.5°C–4°C above the pre-industrial period (medium evidence, high agreement ).

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Since biophysical feedbacks can contribute to both surface temperature warming or cooling, analyses so far suggest that, on a global scale, the net impact on climate change is small (Perugini et al., 2017; Jia et al., 2019), unless these feedbacks also accelerate vegetation mortality and lead to substantive carbon losses (Zemp et al., 2017a; Lemordant and Gentine, 2019). More than one-third of the Earth’s land surface has at least 50% of its evapotranspiration regulated by vegetation, and in some regions between 40 and >80% of the land’s evaporated water is returned to land as precipitation. Locally, both directly human-mediated and climate change-mediated changes in vegetation cover can therefore notably affect annual average freshwater availability to human societies, especially if negative feedbacks amplify the reduction of vegetation cover, evapotranspiration and precipitation (medium confidence) (Keys et al., 2016; Keys and Wang-Erlandsson, 2018).

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Protecting and restoring natural processes is a general principle for maintaining and building resilience to climate change for biodiversity (Timpane-Padgham et al., 2017). One element of this is ensuring naturally functioning hydrology for wetlands and river systems (Table 2.6), which is particularly important in a context of changing rainfall patterns and increased evapotranspiration. An important development in approaches to conservation over recent decades has been the concept of re-wilding (Schulte To Bühne et al., 2021); this encompasses a number of elements of restoring natural processes, including the reintroduction of top predators, larger conservation areas, and less prescriptive outcomes than many previous conservation measures. There are elements of re-wilding which may well contribute to building resilience to climate change, but it will be increasingly important to factor climate change adaptation into the planning of re-wilding schemes (Carroll and Noss, 2021).

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EbA can operate on a range of different scales, from local to catchment to region. On the local scale, there is a variety of circumstances in which microclimates can be managed and local temperatures lowered by the presence of vegetation (Table 2.7), and these EbA techniques are now being used more widely. In both urban and agricultural situations, shade trees are a traditional technique which can be applied to contemporary climate change adaptation. As reported in Section 2.6.2 above, shading of watercourses can lower temperatures, which can allow species to survive locally; as well as supporting diversity, it can help to maintain important fisheries, including those of salmonid fish (O’Briain et al., 2020). Within cities, green spaces, including parks, local nature reserves and green roofs and walls can also provide cooling as a result of evapotranspiration (Bowler et al., 2010a; Aram et al., 2019; Hobbie and Grimm, 2020), although this may be reduced in drought conditions.

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Helbig, M. et al., 2020: Increasing contribution of peatlands to boreal evapotranspiration in a warming climate. Nature Climate Change, 10 (6), 555–560, doi:10.1038/s41558-020-0763-7.

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Hirano, T., K. Kusin, S. Limin and M. Osaki, 2015: Evapotranspiration of tropical peat swamp forests. Global change biology, 21 (5), 1914–1927.

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Lafleur, P. M., R. A. Hember, S. W. Admiral and N. T. Roulet, 2005: Annual and seasonal variability in evapotranspiration and water table at a shrub-covered bog in southern Ontario, Canada. Hydrological Processes, 19 (18), 3533–3550, doi:10.1002/hyp.5842.

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Water demand is projected to change as a direct result of socioeconomic changes. For example, the global water demand for domestic, industrial and agricultural uses, at present about 4600 km³ yr –1, is projected to increase by 20–30% by 2050 (Greve et al., 2018), depending on the socioeconomic scenario. Changes in water availability and demand have been projected in several studies using climate models and socioeconomic scenarios (e.g., Arnell and Lloyd-Hughes, 2014; Gosling and Arnell, 2016; Greve et al., 2018; Koutroulis et al., 2019). In such studies, the projected changes in water availability arise from differences in precipitation and evapotranspiration (ET). However, both precipitation and evapotranspiration are also subject to very high uncertainty in key processes such as regional climate change patterns (Uhe et al., 2021) and the influence of vegetation responses to elevated CO2 on transpiration (Betts et al., 2015).

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Table 4.1 | Trends in global evapotranspiration for different periods between 1981–1982 and 2009–2013.

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The processes that lead to droughts include lack of or less frequent precipitation, increased evapotranspiration and decreased soil moisture, snow cover, runoff and streamflow. For example, warming temperatures may result in higher evapotranspiration, in turn leading to drier soils. In addition, reduced soil moisture diminishes the amount of water filtering into rivers in both the short and long term while also increasing the aridity that can foster the conditions for fire. Moreover, decreased snow cover represents less runoff supply to downstream areas during warmer seasons. Depending on this process and the propagation of a meteorological drought onto further systems, a drought can be defined as hydrological, agricultural or ecological. Agricultural drought threatens food production through crop damage and yield decreases, and consequent economic impacts, and therefore, can be the most impactful to humans. Geographically, the likelihood of agricultural drought is projected to increase across most of southern Africa, Australia, the majority of Europe, the southern and western USA, Central America and the Caribbean, northwest China, parts of South America, and the Russian Federation; but due to increased precipitation, it is projected to decline in southeastern South America, central Africa, central Canada, western India and the south of the Arabian Peninsula.

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Berg, A. and J. Sheffield, 2019: Evapotranspiration partitioning in CMIP5 models: uncertainties and future projections. J. Clim. , 32 (10), 2653–2671, doi:10.1175/jcli-d-18-0583.1.

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Condon, L.E., A.L. Atchley and R.M. Maxwell, 2020: Evapotranspiration depletes groundwater under warming over the contiguous United States. Nat. Commun. , 11 (1), 873, doi:10.1038/s41467-020-14688-0.

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Halladay, K. and P. Good, 2017: Non-linear interactions between CO2 radiative and physiological effects on Amazonian evapotranspiration in an Earth system model. Clim. Dyn. , 49 (7), 2471–2490, doi:10.1007/s00382-016-3449-0.

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Kimball, B.A., et al., 2019: Simulation of maize evapotranspiration: an inter-comparison among 29 maize models. Agric. For. Meteorol. , 271, 264–284, doi:10.1016/j.agrformet.2019.02.037.

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Milly, P.C.D. and K. A. Dunne, 2016: Potential evapotranspiration and continental drying. Nat. Clim. Chang. , 6, 946, doi:10.1038/nclimate3046.

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Zeng, Z., L. Peng and S. Piao, 2018: Response of terrestrial evapotranspiration to Earth’s greening. Curr. Opin. Environ. Sustain. , 33, 9–25, doi:10.1016/j.cosust.2018.03.001.

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Zeng, Z., et al., 2014: A worldwide analysis of spatiotemporal changes in water balance-based evapotranspiration from 1982 to 2009. J. Geophys. Res. Atmos. , 119 (3), 1186–1202, doi:10.1002/2013jd020941.

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Zhang, K., et al., 2015: Vegetation greening and climate change promote multidecadal rises of global land evapotranspiration. Sci. Rep. , 5, 15956, doi:10.1038/srep15956.

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Zhang, Y., et al., 2016: Multi-decadal trends in global terrestrial evapotranspiration and its components. Sci. Rep. , 6, 19124, doi:10.1038/srep19124.

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4.2.1 Observed Changes in Precipitation, Evapotranspiration and Soil Moisture 567

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4.4.1 Projected Changes in Precipitation, Evapotranspiration and Soil Moisture 596

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Under regional projections for North Asia, warmer climate will increase forest fire severity by the late 21st century (medium confidence). For the southern taiga in Tuva Republic, Central Siberia, in a warmer climate, both the annual area burned and fire intensity will increase by 2100. For the central taiga in the Irkutsk region, the annual area burned as well as crown fire-to-ground fire ratiowill increase by the late 21st century compared with the historical (1960–1990) estimate. This moves forest composition towards greater contribution of hardwoods (e.g., Betula spp., Populus spp.) (Brazhnik et al., 2017). This shifting was also proved by observations in northern Mongolia, where boreal forest fires likely promote the relative dominance of B. platyphylla and threaten the existence of the evergreen conifers, Picea obovata and Pinus sibirica (Otoda et al., 2013). For Tuva Republic, warming ambient temperatures increase the potential evapotranspiration demands on vegetation, but if no concurrent increase in precipitation occurs, vegetation becomes stressed and either dies from temperature-based drought stress or more easily succumbs to insects, fire, pathogens or wind throw (Brazhnik et al., 2017). Although Torzhkov et al. (2019) also projected fire risk (FR) increase in Tuva Republic, they expect FR decrease in the Irkutsk region and Yakutia under RCP8.5, and FR decrease in major parts of Central and East Siberia under RCP4.5 for 2090–2099. This discrepancy is due to differences in models, climate projections, fire severity metrics and other assumptions. According to global projections, FR will increase in Central Asia, Russia, China and India under a range of scenarios (Sun et al., 2019).

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Dahal, N.M., et al., 2021: Spatiotemporal analysis of drought variability based on the standardized precipitation evapotranspiration index in the Koshi River Basin, Nepal. J. Arid Land, 13, 433–454.

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Lim, C.-H., et al., 2017b: Estimation of the Virtual Water Content of Main Crops on the Korean Peninsula Using Multiple Regional Climate Models and Evapotranspiration Methods. Sustainability, 9, 1172, doi:10.3390/su9071172.

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There is a recognised need to adapt and chose development pathways that are resilient to climate change while addressing key developmental challenges within dryland regions, notably, poverty, water and food insecurity, and a highly dispersed population with poor access to services (Government of Kenya, 2012; Bizikova et al., 2015; Herrero et al., 2016). The current vision for development of dryland regions comes with both opportunities and threats to achieve a more climate-resilient future. For example, the growth in and exploitation of renewable energy resources, made possible through increased connectivity, brings climate mitigation gains but also risks. These risks include the uneven distribution of costs in terms of where the industry is sited compared with where benefits primarily accrue, and may exacerbate issues around water and food insecurity as strategic areas of land become harder to access (Opiyo et al., 2016; Cormack and Kurewa, 2018; Enns, 2018; Lind, 2018). While LAPSSET will bring greater freedom of movement for commodities, benefitting investors, improving access to markets and urban centres, supporting trade or ease of movement for tourists supporting economic goals, it can also result in the relocation of people and impede access to certain locations for the resident populations. Mobility is a key adaptation behaviour employed in the short and long term to address issues linked with climatic variability (Opiyo et al., 2014; Muricho et al., 2019). With modelled changes in the climate suggesting decreases in income associated with agricultural staples and livestock-dependent livelihoods, development that constrains mobility of local populations could retard resilience gains (Ochieng et al., 2017; ASSAR, 2018; Enns, 2018; Nkemelang et al., 2018). The likely increase in urban populations and the growth in tourism and agriculture may lead to increases in water demand at a time when water availability could become more constrained owing to the reliance on surface water sources and the modelled increases in evapotranspiration due to rising mean temperature, more heatwave days and greater percentage of precipitation falling as storms (ASSAR, 2018; Nkemelang et al., 2018; USAID, 2018). These pressures could make it harder to meet basic health and sanitation goals for rural and poorer urban populations, issues compounded further by likely increases in child malnutrition and diarrheal deaths linked to climate change (WHO, 2016; ASSAR, 2018; Hirpa et al., 2018; Nkemelang et al., 2018; Lesutis, 2020). Development must pay adequate attention to these interconnections to ensure that costs and benefits of achieving climate mitigation and adaptation goals are distributed fairly within a population.

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The runoff decline in southern Australia is projected to be further accentuated by higher temperature and potential evapotranspiration (Potter and Chiew, 2011; Chiew et al., 2014), transpiration from tree regrowth following more frequent and severe wildfires (Brookhouse et al., 2013) (Box 11.1), interceptions from farm dams (Fowler et al., 2015) and reduced surface–groundwater connectivity (limiting groundwater discharge to rivers) in long dry spells (high confidence) (Petrone et al., 2010; Hughes et al., 2012; Chiew et al., 2014). In the longer term, runoff will also be affected by changes in vegetation and surface–atmosphere feedback in a warmer and higher CO2 environment, but the impact is uncertain because of the complex interactions, including changes in climate inputs, fire patterns (Box 11.1) and nutrient availability (Raupach et al., 2013; Ukkola et al., 2016; Cheng et al., 2017).

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Risks associated with water scarcity have the potential to become severe based on projections of large numbers of people becoming exposed to low levels of water availability per person, where ‘water availability’ includes fresh water in the landscape, including soil moisture and streamflows, available for all uses including agriculture as a dominant sector. Approximately 1.6 billion people currently experience ‘chronic’ water scarcity, defined as the availability of less than 1000 m 3 of renewable sources of fresh water per person per year (Gosling and Arnell, 2016). In this context, we define a severe outcome as an additional 1 billion people experiencing ‘chronic’ water scarcity, relating to all uses of water, representing an increase of a magnitude comparable to current levels. The global number of people experiencing chronic water scarcity is projected to increase by approximately 800 million to 3 billion for 2°C global warming, and up to approximately 4 billion for 4°C global warming, considering the effects of climate change alone, with a 9 billion population (Gosling and Arnell, 2016). Severe outcomes are projected to occur even with no changes in exposure: present-day exposure is defined here as ‘medium’ since either an increase or decrease in exposure could be possible. Vulnerability is not quantified in the literature assessed here, so in this assessment it is considered that severe outcomes could occur with present-day levels of vulnerability, again defined here as ‘medium’. Particularly severe outcomes (i.e., the high end of these ranges) are driven by regional patterns of climate change bringing severe reductions in precipitation and/or high levels of evapotranspiration in the most highly populated regions, leading to very substantial reductions in water availability compared with demand. There is strong consensus across models that water scarcity is projected to increase across substantial parts of the world even though projections disagree on which specific areas would see this impact. Moreover, a projected decrease in water scarcity in some regions does not prevent the increase in water scarcity in other regions becoming severe. Hence there is high confidence that risks to water scarcity have the potential to become severe due to climate change. Consequences of water scarcity include potential competition and conflicts between water users (Vanham et al., 2018), damaging livelihoods, hindering socioeconomic development and reducing human well-being, for example through malnutrition resulting from inadequate water supplies leading to long-term health impacts such as child stunting (Cooper et al., 2019). The avoidance of these consequences at high levels of water scarcity would require transformational adaptations including large-scale interventions such as dams and water transfer infrastructure (Greve et al., 2018). Since these require many years or even decades for planning and construction, and are also costly and irreversible and can potentially lead to lock-in and maladaptation, the potential for inadequate policy decisions made in the context of high uncertainties in regional climate changes brings the risk of a shortfall in adaptation. Around 2050, at approximately 2°C global warming, the risk of a substantial adaptation shortfall and hence severe outcomes for water scarcity have a relatively high likelihood across large parts of the southern USA and Mexico, northern Africa, parts of the Middle-East, northern China, and southern Australia, as well as many parts of Northwest India and Pakistan (Greve et al., 2018).

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Nature-based strategies, including street trees, green roofs, green walls and other urban vegetation, can reduce heat and extreme heat by cooling private and public spaces (robust evidence, high agreement ). Shading and evapotranspiration are the primary mechanisms for vegetation-induced urban cooling (Coutts et al., 2016). Shading reduces mean radiant temperature, which is the dominant influence on outdoor human thermal comfort under warm, sunny conditions (Thorsson et al., 2014; Viguié et al., 2020). Outdoor green space and parks may also slightly reduce indoor heat hazard (Viguié et al., 2020). Apart from lowering temperature, NBS may also contribute to lower energy costs by reducing extra demand for conventional sources of cooling (e.g., air conditioning) (Viguié et al., 2020; Foustalieraki et al., 2017), especially during peak demand periods. Homes with shade trees that are located in cities where air conditioning systems are common can save over 30% of residential peak cooling demand (Zardo et al., 2017; Wang et al., 2015). Green roofs have been shown to significantly lower surface temperatures on buildings (Bevilacqua et al., 2017) and modelling suggests that green roofs, if employed widely throughout urban areas, have the potential to impact the regional heat profile of cities (Bevilacqua et al., 2017; Rosenzweig, Gaffin and Parshall, 2006). Community or allotment gardens, backyard greening and other types of low vegetation, as well as lakes, ponds, rivers and streams, can also provide local cooling benefits to nearby residents (Gunawardena, Wells and Kershaw, 2017; Larondelle et al., 2014; Santamouris, 2020).

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Urban climate models show that increased vegetation cover results in reducing both mean air temperatures and extreme temperatures during heatwaves (Heaviside, Cai and Vardoulakis, 2015; Ferreira and Duarte, 2019; Schubert and Grossman-Clarke, 2013). Greater density and more canopy coverage relative to other built and paved surfaces increases shade provision and evapotranspiration (Hamstead et al., 2016; Grilo et al., 2020; Herath, Halwatura and Jayasinghe, 2018; Knight et al., 2021). However, local cooling by vegetation depends on regional climate context, geographic setting of the city, urban form, the density and placement of the trees, in addition to a variety of other ecological, technical, and social factors, such as local stewardship (Salmond et al., 2016). Green spaces less than 0.5–2.0 ha may have negligible cooling effects at regional scales, but impacts of shading can have microscale cooling benefits (Gunawardena, Wells and Kershaw, 2017; Zardo et al., 2017). Vegetation impacts on day versus night-time cooling varies (Imran et al., 2019) as does cooling potential in temperate versus tropical climates. The supply of cooler air from surrounding peri-urban and rural areas can impact cooling in the urban core suggesting that regional adaptation planning for NBS is important to maintain or extend ventilation paths from the urban fringe into the city centre (Schau-Noppel, Kossmann and Buchholz, 2020).

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To maximize the adaptation benefits of NBS for regulating urban heat, it can be helpful to prioritise tree planting and other urban greening investments in areas where heat vulnerability and risk are the highest, especially communities that lack urban tree canopy or accessibility to parks to cool off during hot days or heatwaves (Ziter et al., 2019). Planting trees closely together or in partly permeable vegetated barriers along streets can improve local cooling benefits. Additionally, choosing tree species with leaves that have the greatest leaf area index or the largest leaves can improve cooling performance, as those trees have the greatest shading and evapotranspiration benefits that, in turn, provide the greatest cooling effects (Keeler et al., 2019). Drought-resistant trees, often native trees, are ideal to avoid high watering costs, though dry or water scarce areas may limit adoption of urban vegetation as an NBS strategy (Coutts et al., 2013). Native trees and permaculture can provide additional benefits for local biodiversity as shown in study in Melbourne, Australia which found that increasing vegetation from 10% to 30% increased occupancy of bats, birds, bees, beetles and bugs by up to 130% (Threlfall et al., 2017), with particularly high impact on native species.. Additionally, planting fruit or nut trees can provide co-benefits for local food production, and yet choice of species and placement is important to consider with respect to local cultural needs and norms (Adegun, 2018; Adegun, 2017).

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The role of NBS has been increasingly recognised for improving urban water management, emphasising it’s contribution for climate-adapted development and sustainable urbanisation (robust evidence, high agreement ) (Wong and Brown, 2009). NBS that protect or restore the natural infiltration capacity of a watershed can increase the water supply service to various extents, improving drought protection and providing resilient water supply (Drosou et al., 2019; Krauze and Wagner, 2019), although different forms of NBS (e.g., street trees, parks and open space, community gardens, and engineered devices such as rain gardens, bioswales or retention ponds) contribute in different ways to increasing stormwater infiltration. Additional sources of water may be available to replace the water supplied by NBS, such as rainwater harvesting, inter-basin transfers or desalination plants. Reliance on naturally sourced, locally available surface water and groundwater is more energy efficient and economical than desalination or water reuse for potable use (Boelee et al., 2017), while rainwater harvesting is even more economical. Increasing the amount of green space in urban areas can secure and regulate water supplies, improving water security (Liu and Jensen, 2018; Bichai and Cabrera Flamini, 2018). However, Bhaskar et al. (2016) reviewed the effect of urbanisation and NBS on baseflow and suggest that the confounded effects of infiltration and evapotranspiration losses, combined with the subsurface infrastructure (sewer systems) and geology, makes it difficult to predict the magnitude of baseflow enhancement resulting from the implementation of NBS in cities.

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To maximise the adaptation benefits of NBS for urban water supply research suggests that managers and planners consider NBS as alternatives to traditional stormwater management techniques, where possible, since these solutions can promote groundwater recharge. As green infrastructure is increasingly being used for stormwater absorption in cities (McPhillips et al., 2020), rain gardens, wetlands, or engineered infiltration ponds and bioswales are the NBS most likely to promote recharge, reduce evapotranspiration and contribute to water provisioning.

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In contrast to a ‘fail-safe’ approach to design which emphasises strengthening infrastructure against more intense environmental conditions, ‘safe-to-fail’ flood strategies allow infrastructure to fail in its ability to carry out its primary function but control the consequences of the failure. Examples include the use of a bioretention basin in Scottsdale (Arizona, USA) to accommodate excess runoff and help drain the city; a subsidy for affected farmers for lost crop production as part of the Netherlands’ Room for the River programme; targeted destruction of a levee to control flooding in the Mississippi River Valley in 2011 (Kim et al., 2019). Water-sensitive urban design, low-impact development, sponge cities, sustainable urban drainage and natural flood management involve deployment of systems and practices that use or mimic natural processes that result in the infiltration, evapotranspiration or use of stormwater to protect water quality and associated aquatic habitat. These are being designed and implemented at increasingly ambitious scales. For example, China’s Sponge City initiative sets a goal of 80% of urban land able to absorb or reuse 70% of stormwater through underground storage tanks and tunnels, and use of pervious pavements, in addition to NBS (Chan et al., 2018; Muggah, 2019). Similarly, several thousand water-sensitive urban design interventions have been implemented across the city of Melbourne (Kuller et al., 2018).

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Urban agriculture and forestry can improve nutrition and food security, household income and mental health of urban farmers while mitigating against some of the impacts of climate change, like flooding and landslides (by stabilising the soil and reducing runoff, for example), heat (by providing shade and through evapotranspiration) and diversifying food sources in case of drought (Zezza and Tasciotti, 2010; Lwasa et al., 2014; Battersby and Hunter-Adams, 2020).

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Climate trends are affecting riverine, lake and reservoir water quality (medium confidence). Droughts and increased evapotranspiration have impaired water quality by concentrating pollutants in diminished water volumes (Paul et al., 2019a). Cyanobacterial blooms and pathogen exposure events are increasing in frequency, intensity and duration in North America (Taranu et al., 2015). They are closely associated with observed changes in precipitation intensity and associated nutrient loading (e.g., agricultural runoff, sanitary sewer overflows), elevated water temperatures and eutrophication (Michalak et al., 2013; Michalak, 2016; Trtanj et al., 2016; Chapra et al., 2017; IBWC, 2017; Williamson et al., 2017; Olds et al., 2018; Coffey et al., 2019). These events endanger human and animal health, recreational and drinking water uses and aquatic ecosystem functioning, and cause economic losses (Michalak et al., 2013; Bullerjahn et al., 2016; Chapra et al., 2017; Huisman et al., 2018). Households and communities dependent on substandard wells, unimproved water sources or deficient water provision systems are more exposed than others to experience climate-related impairment of drinking water quality (Section 14.5.6.5; Allaire et al., 2018; Baeza et al., 2018; California State Water Resources Control Board, 2021; Navarro-Espinoza et al., 2021; Water and Tribes Initiative, 2021).

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Tam, B.Y., et al., 2019: CMIP5 drought projections in Canada based on the Standardized Precipitation Evapotranspiration Index. Can. Water Resour. J. , 44 (1), 90–107, doi:10.1080/07011784.2018.1537812.

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Table 12.3 | Change in percentage of land area affected by extreme drought in 2010–2019, in relation to 1950–1959 using Standardised Precipitation-Evapotranspiration Index (SPEI); extreme drought is defined as SPEI ≤ −1.6 (Federal Office of Meteorology and Climatology MeteoSwiss, 2021). Data were derived from Romanello et al. (2021).

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Grasslands make a significant contribution to food security in Patagonia by providing part of the feed requirements of ruminants used for meat, wool and milk production. There is a lack of information regarding the combined effects of climate change and overgrazing and the consequences for pastoral livelihoods that depend on rangelands. Temperature and the amount and seasonal distribution of precipitation were important controls of vegetation structure in Patagonian rangelands (Gaitán et al., 2014). They found that over two-thirds of the total effect of precipitation on above-ground net primary production (ANPP) was direct, and the other third was indirect (via the effects of precipitation on vegetation structure). Thus, if evapotranspiration and drought stress increase as temperature increases and rainfall decreases in water-limited ecosystems, a greater exposure of ranchers to a reduction in stocking rate and, therefore, family income would be expected (medium confidence). The number of farmers (mainly family enterprises) exposed to climatic hazards (drought) is approximately 70,000–80,000, who have 14–15 million sheep in Argentina (Peri et al., 2021).

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Heerspink, B.P., A.D. Kendall, M.T. Coe and D.W. Hyndman, 2020: Trends in streamflow, evapotranspiration, and groundwater storage across the Amazon Basin linked to changing precipitation and land cover. J. Hydrol. Reg. Stud. , 32, 100755, doi:10.1016/j.ejrh.2020.100755.

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Global precipitation, evapotranspiration, runoff and water availability increase with warming (Hanasaki et al. 2013; Greve et al. 2018) (AR6 WGII Chapter 4). Climate change also affects the occurrence of and exposure to hydrological extremes ( high confidence) (Arnell and Lloyd-Hughes 2014; Asadieh and Krakauer 2017; Dottori et al. 2018; Naumann et al. 2018; IPCC 2019a; Do et al. 2020) (AR6 WGII Chapter 4). Climate models project increases in precipitation intensity ( high confidence), local flooding (medium confidence), and drought risk (very high confidence) (Arnell and Lloyd-Hughes 2014; Asadieh and Krakauer 2017; Dottori et al. 2018; IPCC 2019a) (AR6 WGII Chapter 4).

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Global precipitation, evapotranspiration, runoff and water availability increase with warming (Hanasaki et al. 2013; Greve et al. 2018) (AR6 WGII Chapter 4). Climate change also affects the occurrence of and exposure to hydrological extremes ( high confidence) (Arnell and Lloyd-Hughes 2014; Asadieh and Krakauer 2017; Dottori et al. 2018; Naumann et al. 2018; IPCC 2019a; Do et al. 2020) (AR6 WGII Chapter 4). Climate models project increases in precipitation intensity ( high confidence), local flooding (medium confidence), and drought risk (very high confidence) (Arnell and Lloyd-Hughes 2014; Asadieh and Krakauer 2017; Dottori et al. 2018; IPCC 2019a) (AR6 WGII Chapter 4).

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Since AR5 (Ciais et al., 2013), a number of improvements have led to the more constrained carbon budget presented here. Some new additions include: (i) the use of independent estimates for the residual carbon sink on natural terrestrial ecosystems (Le Quéré et al., 2018a); (ii) improvements in the estimates of emissions from cement production (Andrew, 2019) and the sink associated with cement carbonation (Cao et al., 2020); (iii) improved and new emissions estimates from forestry and other land use (Hansis et al., 2015; Gasser et al., 2020); (iv) the use of ocean observation-based sink estimates and a revised river flux partition between hemispheres (Friedlingstein et al., 2020); and (v) the expansion of constraints from atmospheric inversions, based on surface networks and the use of satellite retrievals.

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Xi, F. et al., 2016: Substantial global carbon uptake by cement carbonation. Nat. Geosci. , 9(12) , 880–883, doi:10.1038/ngeo2840.

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The physical and biogeochemical controls of greenhouse gases (GHGs) is a central motivation for this chapter, which identifies biogeochemical feedbacks that have led or could lead to a future acceleration, slowdown or abrupt transitions in the rate of GHG accumulation in the atmosphere, and therefore of climate change. A characterization of the trends and feedbacks lead to improved quantification for the remaining carbon budgets for climate stabilization, and the responses of the carbon cycle to atmospheric carbon dioxide removal (CDR), which is embedded in many of the mitigation scenarios, to achieve the goals of the Paris Agreement.

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Carbon dioxide removal (CDR) options based on terrestrial carbon sinks will require the appropriation of significant amounts of water at the landscape level. Most mitigation pathways that seek to limit global warming to 1.5°C or less than 2°C require the removal of about 30 to 300 GtC from the atmosphere by 2100 (Rogelj et al., 2018b). Bioenergy with carbon capture and storage (BECCS), and afforestation/reforestation are the dominant CDR options used in climate stabilization scenarios, implying large requirements for land and water (Section 5.6; Beringer et al., 2011; Boysen et al., 2017b; Fajardy and Mac Dowell, 2017; Jans et al., 2018; Séférian et al., 2018b; Yamagata et al., 2018; Stenzel et al., 2019). A review of freshwater requirements for irrigating biomass plantations shows a range between 15 and 1250 km3 per GtC of biomass harvest. This is equivalent to a water requirement of 99–8250 km3 for the median BECCS deployment of around 3.3 GtC yr−1 (Smith et al., 2016) in <2°C-scenarios (Stenzel et al., 2021), assuming that biomass is converted to electricity, which is substantially less efficient than converting biomass to heat. These large ranges are the result of different assumptions about the type of biomass and yield improvements, management, and land availability. The use of alternative feedstocks, such as wastes, residues and algae, would lead to smaller water requirements (Smith et al., 2019).

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This subsection assesses evidence about the response of the global carbon cycle to CDR from idealized model simulations which assume that CO2 is removed from the atmosphere directly and stored permanently in the geologic reservoir, which is analogous to direct air carbon capture with carbon storage (DACCS) (Table 5.9). The carbon cycle response to specific land and ocean-based CDR methods is assessed in Section 5.6.2.2.2. At the time of AR5 there were very few studies about the global carbon cycle response to CDR. Based on these studies and general understanding of the carbon cycle, AR5 WGI Chapter 6 assessed that it is virtually certain that deliberate removal of CO2 from the atmosphere will be partially offset by outgassing of CO2 from the ocean and land carbon sinks. Low confidence was placed on any quantification of effects. Since AR5 WGI Chapter 6, several studies have investigated the carbon cycle response to CDR in idealized ‘pulse’ removal simulations, whereby a specified amount of CO2 is removed instantly from the atmosphere, and scenario simulations with CO2 emissions and removals following a plausible trajectory. In addition, a dedicated carbon dioxide removal model intercomparison project was initiated (CDRMIP; Keller et al., 2018b) which includes a range of CDR experiments from idealized simulations to simulations of deployment of specific CDR methods (afforestation and ocean alkalinization).

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During the industrial era, CO2 emitted by the combustion of fossil fuels and land-use change has been redistributed between atmosphere, land, and ocean carbon reservoirs due to carbon cycle processes (Box 5.3, Figure 1b and Figure 5.13). Over the past decade (2010–2019), 46% of the emitted CO2 remained in the atmosphere, 23% was taken up by the ocean, and 31% by the terrestrial biosphere (Section 5.2.1.5). When carbon dioxide removal (CDR) is applied during periods in which human activities are net CO2 sources to the atmosphere, and the amount of emissions removed by CDR is smaller than the net source (net positive CO2 emissions), CDR acts to reduce the net emissions (Box 5.3 Figure 1c). In this scenario, part of the CO2 emissions in the atmosphere are removed by the land and ocean sinks, as has been the case historically.

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Boysen, L.R., W. Lucht, and D. Gerten, 2017a: Trade-offs for food production, nature conservation and climate limit the terrestrial carbon dioxide removal potential. Global Change Biology, 23(10), 4303–4317, doi: 10. 1111/gcb.13745.

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Heck, V., D. Gerten, W. Lucht, and L.R. Boysen, 2016: Is extensive terrestrial carbon dioxide removal a ‘green’ form of geoengineering? A global modelling study. Global and Planetary Change, 137, 123–130, doi: 10.1016/j.gloplacha.2015.12.008.

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Keller, D.P. et al., 2018a: The Effects of Carbon Dioxide Removal on the Carbon Cycle. Current Climate Change Reports, 4(3), 250–265, doi: 10.1007/s40641-018-0104-3.

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Keller, D.P. et al., 2018b: The Carbon Dioxide Removal Model Intercomparison Project (CDRMIP): rationale and experimental protocol for CMIP6. Geoscientific Model Development, 11(3), 1133–1160, doi: 10.5194/gmd-11-1133-2018.

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Strefler, J., T. Amann, N. Bauer, E. Kriegler, and J. Hartmann, 2018: Potential and costs of carbon dioxide removal by enhanced weathering of rocks. Environmental Research Letters, 13(3), 34010, doi: 10.1088/1748-9326/aaa9c4.

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The expanded treatment of uncertainty allows this chapter a more comprehensive assessment of the benefits from mitigation than in previous IPCC reports, as well as the climate response to carbon dioxide removal (CDR) and solar radiation modification (SRM), and how to detect them against the backdrop of internal variability. Important advances have been made in the detection and attribution of mitigation, CDR, and SRM (Bürger and Cubasch, 2015; Lo et al., 2016; Ciavarella et al., 2017); exploring the ‘time of emergence’ (ToE; see Annex VII: Glossary) of responses to assumed emissions reductions (Tebaldi and Friedlingstein, 2013; Samset et al., 2020)and the attribution of decadal events to forcing changes that reflect emissions reductions (Marotzke, 2019; Spring et al., 2020; McKenna et al., 2021).

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Global temperature is expected to remain approximately constant if emissions of CO2 were to cease (Section 4.7.1.1), and so reductions in GSAT are only possible in the event of net negative global CO2 emissions. We assess here results from an overshoot scenario (SSP5-3.4-OS; O’Neill et al., 2016), which explores the implications of a peak and decline in forcing during the 21st century. Reversibility under more extreme and idealized carbon dioxide removal (CDR) scenarios is assessed in Section 4.6.3. In SSP5-3.4-OS, CO2 peaks at 571 ppm in the year 2062 and reverts to 497 ppm by 2100 – approximately the same level as in 2040. SSP5-3.4-OS has strong net negative emissions of CO2, exceeding those in SSP1-2.6 and SSP1-1.9 from 2070 onwards and reaching –5.5 PgC yr–1 (–20 GtCO2 yr–1) by 2100. While this causes global mean temperature to decline, changes in climate have not fully reversed by 2100 under this reversal of CO2 concentration (Figure 4.34). Quantities are compared for 2081–2100 relative to a 20-year period (2034–2053) of the same average CO2. Differences between these two periods of the same CO2 are: GSAT: 0.28 ± 0.30°C (mean ± standard deviation); global land precipitation: 0.026 ± 0.011 mm day–1; September Arctic sea ice area: –0.32 ± 0.53 million km2; thermosteric sea level: 12 ± 0.8 cm. As assessed in Section 9.3.1.1, Arctic sea ice area is linearly reversible with GSAT. Although these climate quantities are not fully reversible, the overshoot scenario results in reduced climate change compared with stabilisation or continued increase in greenhouse gases (Tsutsui et al., 2006; Palter et al., 2018; Tachiiri et al., 2019) (high confidence).

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The climate response to CDR-caused net negative CO2 emissions has been studied in Earth system models by prescribing idealized ramp-down of CO2 concentrations (MacDougall,2013; Zickfeld et al., 2016; Schwinger and Tjiputra, 2018), CO2 concentrations of RCP scenarios that have net negative CO2 emissions (C.D. Jones et al., 2016b), and idealized net negative CO2 emissions scenarios (Tokarska and Zickfeld, 2015). The Carbon Dioxide Removal Model Intercomparison Project (CDRMIP) uses multiple ESMs to explore the climate response, effectiveness of CO2 removal, and challenges of CDR options (Keller et al., 2018). Idealized CDRMIP simulations increase CO2 concentrations at 1% per year from the level in the pre-industrial control run (piControl) to 4×CO2 and subsequently decrease at the same rate to the piControl level. This section assesses the lag in climate response to CDR-caused negative emissions; climate ‘reversibility’ is assessed in Section 4.7.2. The ramp-down phase, though unrealistic, represents the ‘net negative CO2 emissions’ phase.

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The Carbon Dioxide Removal Model Intercomparison Project (CDR-MIP; Keller et al., 2018) comprises a set of 1% ramp-up, ramp-down simulations aimed at establishing a multi-model assessment of reversibility of Earth system components. Preliminary results from CDRMIP are presented in Section 4.6.3. Results from the SSP5-3.4-Overshoot scenario and other quantities of climate change at the same CO2 level before and after overshoot are assessed in Section 4.6.2. Forcing reversal is followed by reversal of ocean surface and land temperature along with land and ocean precipitation, snow cover, and Arctic sea ice with a lag of a few years to decades (Table 4.10). Other tipping elements have much longer time scales of reversibility from decades to millennia. Drijfhout et al. (2015) provided an assessment of 13 regional mechanisms of abrupt change, finding abrupt changes in sea ice, oceanic flows, land ice, and terrestrial ecosystem response, although with little consistency among the models. The potential for abrupt changes in ice sheets, the AMOC, tropical forests, and ecosystem responses to ocean acidification were also recently reviewed by (Good et al., 2018). They found that some degree of irreversible loss of the West Antarctic Ice Sheet (WAIS) may have already begun, that tropical forests are adversely affected by drought, and rapid development of aragonite undersaturation at high latitudes affecting calcifying organisms.

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Ehlert, D. and K. Zickfeld, 2018: Irreversible ocean thermal expansion under carbon dioxide removal. Earth System Dynamics, 9(1), 197–210, doi: 10.5194/esd-9-197-2018.

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Keller, D.P. et al., 2018: The Carbon Dioxide Removal Model Intercomparison Project (CDRMIP): rationale and experimental protocol for CMIP6. Geoscientific Model Development, 11(3), 1133–1160, doi: 10.5194/gmd-11-1133-2018.

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Rickels, W., F. Reith, D. Keller, A. Oschlies, and M.F. Quaas, 2018: Integrated Assessment of Carbon Dioxide Removal. Earth’s Future, 6(3), 565–582, doi: 10.1002/2017ef000724.

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Keller, D.P. et al., 2018: The Carbon Dioxide Removal Model Intercomparison Project (CDRMIP): rationale and experimental protocol for CMIP6. Geoscientific Model Development, 11(3), 1133–1160, doi: 10.5194/gmd-11-1133-2018.

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Since AR5, a small number of modelling studies have examined the reversibility of the multimillennial sea level commitment under carbon dioxide (CO2) removal, solar radiation modification or local ice shelf engineering. The slow response of the deep ocean to forcing leads to global-mean thermosteric sea level fall occurring long afterward, even if CO2 levels are restored after a transient increase: global mean thermosteric sea level rise takes more than a millennium to reverse (Ehlert and Zickfeld, 2018). Rapid reversion to pre-industrial CO2 concentrations has been found to be ineffective at fostering regrowth of the AIS (DeConto et al., 2021) but may reduce the multimillennial sea level commitment (DeConto and Pollard, 2016). Altering sub-ice-shelf bathymetry (Wolovick and Moore, 2018) or triggering ice shelf advance through massive snow deposition (Feldmann et al., 2019) might interrupt marine ice sheet instability (Section 9.4.2.4) and thus reduce sea level commitment. A reversion to pre-industrial Greenland Ice Sheet temperatures with solar radiation modification is projected to stop mass loss in Greenland but leads to minimal regrowth (Applegate and Keller, 2015). Based on limited evidence, carbon dioxide removal, solar radiation modification, and local ice-shelf engineering may be effective at reducing the yet-to-be-realized sea level commitment, but ineffective at reversing GMSL rise (low confidence).

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Ehlert, D. and K. Zickfeld, 2018: Irreversible ocean thermal expansion under carbon dioxide removal. Earth System Dynamics, 9, 197–210, doi: 10.5194/esd-9-197-2018.

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Global net zero CO2 or GHG emissions can be achieved even if some sectors and regions are net emitters, provided that others reach net negative emissions (see Figure 4.1). The potential and cost of achieving net zero or even net negative emissions vary by sector and region. If and when net zero emissions for a given sector or region are reached depends on multiple factors, including the potential to reduce GHG emissions and undertake carbon dioxide removal, the associated costs, and the availability of policy mechanisms to balance emissions and removals between sectors and countries. (high confidence). {WGIII Box TS.6, WGIII Cross-Chapter Box 3}

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With every increment of warming, climate change impacts and risks will become increasingly complex and more difficult to manage. Many regions are projected to experience an increase in the probability of compound events with higher global warming, such as concurrent heatwaves and droughts, compound flooding and fire weather. In addition, multiple climatic and non-climatic risk drivers such as biodiversity loss or violent conflict will interact, resulting in compounding overall risk and risks cascading across sectors and regions. Furthermore, risks can arise from some responses that are intended to reduce the risks of climate change, e.g., adverse side effects of some emission reduction and carbon dioxide removal (CDR) measures (see 3.4.1). (high confidence) {WGI SPM C.2.7, WGI Figure SPM.6, WGI TS.4.3; WGII SPM B.1.7, WGII B.2.2, WGII SPM B.5, WGII SPM B.5.4, WGII SPM C.4.2, WGII SPM B.5, WGII CCB2}

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Multiple climate change risks will increasingly compound and cascade in the near term (high confidence). Many regions are projected to experience an increase in the probability of compound events with higher global warming (high confidence) including concurrent heatwaves and drought. Risks to health and food production will be made more severe from the interaction of sudden food production losses from heat and drought, exacerbated by heat-induced labour productivity losses (high confidence) (Figure 4.3). These interacting impacts will increase food prices, reduce household incomes, and lead to health risks of malnutrition and climate-related mortality with no or low levels of adaptation, especially in tropical regions (high confidence). Concurrent and cascading risks from climate change to food systems, human settlements, infrastructure and health will make these risks more severe and more difficult to manage, including when interacting with non-climatic risk drivers such as competition for land between urban expansion and food production, and pandemics (high confidence). Loss of ecosystems and their services has cascading and long-term impacts on people globally, especially for Indigenous Peoples and local communities who are directly dependent on ecosystems, to meet basic needs (high confidence). Increasing transboundary risks are projected across the food, energy and water sectors as impacts from weather and climate extremes propagate through supply-chains, markets, and natural resource flows (high confidence) and may interact with impacts from other crises such as pandemics. Risks also arise from some responses intended to reduce the risks of climate change, including risks from maladaptation and adverse side effects of some emissions reduction and carbon dioxide removal measures, such as afforestation of naturally unforested land or poorly implemented bioenergy compounding climate-related risks to biodiversity, food and water security, and livelihoods (high confidence) (see Section 3.4.1 and 4.5). {WGI SPM.2.7; WGII SPM B.2.1, WGII SPM B.5, WGII SPM B.5.1, WGII SPM B.5.2, WGII SPM B.5.3, WGII SPM B.5.4, . WGII Cross-Chapter Box COVID in Chapter 7; WGIII SPM C.11.2; SRCCL SPM A.5, SRCCL SPM A.6.5} (Figure 4.3)

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Li, X., et al., 2020c: Irreversibility of marine climate change impacts under carbon dioxide removal. Geophys. Res. Lett. , 47 (17), e2020GL088507, doi:10.1029/2020GL088507.

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Sustainable development considerations could be used to prioritise mitigation options, but as noted earlier, there are trade-offs, with a potentially significant impact on the economic cost of mitigation, as well as a potential trade-off in terms of the climate outcomes that are still viable (Riahi et al., 2022). For instance, all of the 1.5°C scenarios used in IPCC (2018a) deploy carbon dioxide removal technologies (Rogelj et al., 2018). Without these technologies, most models cannot generate pathways that limit warming to 1.5°C, and those that are able to adopt strong assumptions about global policy development and socioeconomic changes. Sustainable development might also affect the design of policies by prioritising specific sustainable development objectives. However, there are trade-offs here as well, with costs and the distribution of costs varying with alternative policy designs. For instance, prioritising air quality has climate co-benefits but does not ensure the lowest cost climate strategy (Arneth et al., 2009; Kandlikar et al., 2009). Similarly, prioritising land protection has a variety of co-benefits but could increase food prices significantly, as well as the overall cost of climate mitigation (IPCC, 2019b). In this context, with lower climate risk and adaptation levels and larger mitigation effort, managing mitigation trade-offs could be a sustainable development priority. Furthermore, sustainable development could also be tailored to facilitate adaptation and manage mitigation costs.

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Schenuit, F. et al., 2021: Carbon Dioxide Removal Policy in the Making: Assessing Developments in 9 OECD Cases. Frontiers in Climate, 3, 7, doi:10.3389/fclim.2021.638805.

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Second, demand-side solutions support staying within planetary boundaries (Haberl et al. 2014; Matson et al. 2016; Hillebrand et al. 2018; Andersen and Quinn 2020; UNDESA 2020; Hickel et al. 2021; Keyßer and Lenzen 2021). Demand side solutions entail fewer environmental risks than many supply-side technologies (Von Stechow et al. 2016). Additionally they make carbon dioxide removal technologies, such as bioenergy with carbon capture and storage (BECCS) less relevant (Van Vuuren et al. 2018) but modelling studies (Grubler et al. 2018; Hickel et al. 2021; Keyßer and Lenzen 2021) still require ecosystem-based carbon dioxide removal. In the IPCC’s Special Report on Global Warming of 1.5°C (SR1.5) (IPCC 2018), four stylised scenarios have explored possible pathways towards stabilising global warming at 1.5°C (IPCC 2014a, Figure SPM.3a) (Figure 5.1) One of these scenarios, LED-19, investigates the scope of demand-side solutions (Figure 5.1). The comparison of scenarios reveals that such low energy demand pathways eliminate the need for technologies with high uncertainty, such as BECCS. Third, interrogating demand for services from the well-being perspective also opens new avenues for assessing mitigation potentials (Brand-Correa and Steinberger 2017; Mastrucci and Rao 2017; Rao and Min 2018a; Mastrucci and Rao 2019; Baltruszewicz et al. 2021). Arguably, demand-side interventions often operate institutionally or in terms of restoring natural functioning and have so far been politically sidelined but COVID-19 revealed interesting perspectives (Box 5.2). Such demand-side solutions also support near-term goals towards climate change mitigation and reduce the need for politically challenging high global carbon prices (Méjean et al. 2019) (Box 5.11). The well-being focus emphasises equity and universal need satisfaction, compatible with progress towards meeting the Sustainable Development Goals (SDGs) (Lamb and Steinberger 2017).

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Long-term mitigation scenarios play a crucial role in climate policy design in the near term, by illuminating transition pathways, interactions between supply-side and demand-side interventions, their timing, and the scales of required investments needed to achieve mitigation goals (Chapter 3). Historically, most long-term mitigation scenarios have taken technology-centric approaches with heavy reliance on supply-side solutions and the use of carbon dioxide removal, particularly in 1.5°C scenarios (Rogelj et al. 2018). Comparatively less attention has been paid to deep demand-side reductions incorporating socio-cultural change and the cascade effects (Section 5.3.2) associated with ASI strategies, primarily due to limited past representation of such service-oriented interventions in long-term integrated assessment models (IAMs) and energy systems models (ESMs) (Grubler et al. 2018; van de Ven et al. 2018; Napp et al. 2019). There is ample evidence of savings from sector- or issue-specific bottom-up studies (Section 5.3.1.2). However, these savings typically get lost in the dominant narrative provided by IAMs and ESMs and in their aggregate-level evaluations of combinations of ASI and efficiency strategies. As a result, their interaction effects do not typically get equal focus alongside supply-side and carbon dioxide removal options (Samadi et al. 2017; Van Vuuren et al. 2018; Van den Berg et al. 2019).

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In response to 1.5°C ambitions, and a growing desire to identify participatory pathways with less reliance on carbon dioxide removal which has high uncertainty, some recent IAM and ESM mitigation scenarios have explored the role of deep demand-side energy and resource use reduction potentials at global and regional levels. Table 5.2 summarises long-term scenarios that aimed to: minimise service-level energy and resource demand as a central mitigation tenet; specifically evaluate the role of behavioural change and ASI strategies; and/or achieve a carbon budget with limited or no carbon dioxide removal. From assessment of this emerging body of literature, several general observations arise and are presented below.

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Third, low demand scenarios can reduce both supply-side capacity additions and the need for carbon capture and removal technologies to reach emissions targets. Of the scenarios listed in Table 5.2, one (LED-MESSAGE) reaches 2050 emissions targets with no carbon capture or removal technologies (Grubler et al. 2018), whereas others report significant reductions in reliance on bioenergy with carbon capture and storage (BECCS) compared to traditional technology-centric mitigation pathways (Liu et al. 2018; Van Vuuren et al. 2018; Napp et al. 2019), with the IEA’s NZE notably requiring the least carbon dioxide removal (1.8 Gt in 2050) and primary bioenergy (100 EJ in 2050) compared to IPCC net zero SR1.5 scenarios (IEA 2021).

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Cumulative CO2 emissions since 1850 reached 2400 ± 240 GtCO2 in 2019 ( high confidence). 7 More than half (62%) of total emissions from 1850 to 2019 occurred since 1970 (1500 ± 140 GtCO2), about 42% since 1990 (1000 ± 90 GtCO2) and about 17% since 2010 (410 ± 30 GtCO2) (Friedlingstein et al. 2019; Friedlingstein et al. 2020; Canadell et al. 2021) (Figure 2.7). Emissions in the last decade are about the same size as the remaining carbon budget of 400 ± 220 (500, 650) GtCO2 for limiting global warming to 1.5°C and between one-third and half the 1150 ± 220 (1350, 1700) GtCO2 for limiting global warming below 2°C with a 67% (50%, 33%) probability, respectively (medium confidence) (Canadell et al. 2021). At current (2019) levels of emissions, it would only take 8 (2–15) and 25 (18–35) years to emit the equivalent amount of CO2 for a 67th percentile 1.5°C and 2°C remaining carbon budget, respectively. Related discussions of carbon budgets, short-term ambition in the context of Nationally Determined Contributions (NDCs), pathways to limiting warming to well below 2°C and carbon dioxide removals are mainly discussed in Chapters 3, 4, and 12, but also Section 2.7 of this chapter.

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In accounting studies, estimates of future CO2 emissions from current fossil fuel infrastructures are dominated by the power sector with its large fossil fuel capacities. In contrast, scenario studies highlight residual emissions from non-electric energy – particularly in the transport and industry sectors. Fossil-fuel infrastructure in the power sector can be much more easily retired than in those sectors, where there are fewer and more costly alternatives. IAMs therefore account for continued investments into fossil-based energy technologies in areas with limited decarbonisation potential, such as some areas of transportation (in particular aviation, shipping and road-based freight) or some industrial processes (such as cement production or feedstocks for chemicals). This explains the key discrepancies observable in Table 2.7. Therefore, our overall assessment of these available lines of evidence strongly emphasises the importance of decommissioning, reduced utilisation of existing power sector infrastructure, as well as continued cancellation of new power sector infrastructures in order to limit warming to well below 2°C ( high confidence) (Kriegler et al. 2018b; Luderer et al. 2018; Chen et al. 2019; Cui et al. 2019; Fofrich et al. 2020). This is important as the power sector is comparatively easy to decarbonise (IPCC 2014a; Krey et al. 2014; Davis et al. 2018; Méjean et al. 2019) and it is crucial to make space for residual emissions from non-electric energy end uses that are more difficult to mitigate ( high confidence). Any further delay in climate policy substantially increases carbon lock-in and mitigation challenges as well as a dependence on carbon dioxide removal technologies for meeting the Paris climate goals (Kriegler et al. 2018b; Luderer et al. 2018).

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Table 2.7 | Residual (gross) fossil fuel emissions (GtCO2) in climate change mitigation scenarios strengthening mitigation action after 2020 (‘early strengthening’), compared to scenarios that keep Nationally Determined Contribution (NDC) ambition level until 2030 and only strengthen thereafter. Cumulative gross CO2 emissions from fossil fuel and industry until reaching net zero CO2 emissions are given in terms of the mean as well as minimum and maximum (in parentheses) across seven participating models: AIM/CGE, GCAM, IMAGE, MESSAGES, POLES, REMIND, WITCH. Scenario design prescribes a harmonised, global carbon price in line with long-term carbon budget. Delay scenarios follow the same price trajectory, but 10 years later. Carbon dioxide removal requirements represent ex-post calculations that subtract gross fossil fuel emissions from the carbon budget associated with the respective long-term warming limit. We take the carbon budget for limiting warming to 1.5°C with a 50% probability and to 2°C with a 67% probability (Canadell et al. 2021). Hence, carbon dioxide removal (CDR) requirements reflect a minimum amount of CDR for a given mitigation trajectory. Results are reported at two significant digits. Sources: Luderer et al. (2018); Tong et al. (2019).

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Chapter 3 assesses the emissions pathways literature in order to identify their key characteristics (both in commonalities and differences) and to understand how societal choices may steer the system into a particular direction (high confidence) . More than 2000 quantitative emissions pathways were submitted to the IPCC’s Sixth Assessment Report AR6 scenarios database, out of which 1202 scenarios included sufficient information for assessing the associated warming consistent with WGI. Five Illustrative Mitigation Pathways (IMPs) were selected, each emphasising a different scenario element as its defining feature: heavy reliance on renewables (IMP-Ren), strong emphasis on energy demand reductions (IMP-LD), extensive use of carbon dioxide removal (CDR) in the energy and the industry sectors to achieve net negative emissions (IMP-Neg), mitigation in the context of broader sustainable development (IMP-SP), and the implications of a less rapid and gradual strengthening of near-term mitigation actions (IMP-GS). {3.2, 3.3}

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Pathways following Nationally Determined Contributions (NDCs) announced prior to COP262 until 2030 reach annual emissions of 47–57GtCO2-eq by 2030, thereby making it impossible to limit warming to 1.5°C with no or limited overshoot and strongly increasing the challenge to limit warming to 2°C (>67%) (high confidence). A high overshoot of 1.5°C increases the risks from climate impacts and increases the dependence on large-scale carbon dioxide removal from the atmosphere. A future consistent with NDCs announced prior to COP26 implies higher fossil fuel deployment and lower reliance on low-carbon alternatives until 2030, compared to mitigation pathways with immediate action to limit warming to 2°C (>67%) or lower. To limit warming to 2°C (>67%) after following the NDCs to 2030, the pace of global GHG emission reductions would need to accelerate rapidly from 2030 onward: to an average of 1.4–2.0 GtCO2-eq yr –1 between 2030 and 2050, which is around two-thirds of the global CO2 emission reductions in 2020 due to the COVID-19 pandemic, and around 70% faster than in immediate action pathways that limit warming to 2°C (>67%). Accelerating emission reductions after following an NDC pathway to 2030 would be particularly challenging because of the continued buildup of fossil fuel infrastructure that would be expected to take place between now and 2030. {3.5, 4.2}

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aThe Illustrative Mitigation Pathway ‘Neg’ has extensive use of carbon dioxide removal (CDR) in the AFOLU, energy and the industry sectors to achieve net negative emissions. Warming peaks around 2060 and declines to below 1.5°C (50% likelihood) shortly after 2100. Whilst technically classified as C3, it strongly exhibits the characteristics of C2 high-overshoot pathways, hence it has been placed in the C2 category. See Box SPM.1 for an introduction of the IPs and IMPs.

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The Illustrative Mitigation Pathways (IMPs) properly explore different pathways consistent with meeting the long-term temperature goals of the Paris Agreement. They represent five different pathways that emerge from the overall assessment. The IMPs differ in terms of their focus, for example, placing greater emphasis on renewables (IMP-Ren), deployment of carbon dioxide removal that results in net negative global GHG emissions (IMP-Neg), and efficient resource use and shifts in consumption patterns, leading to low demand for resources, while ensuring a high level of services (IMP-LD). Other IMPs illustrate the implications of a less rapid introduction of mitigation measures followed by a subsequent gradual strengthening (IMP-GS), and how shifting global pathways towards sustainable development, including by reducing inequality, can lead to mitigation (IMP-SP) In the IMP framework, IMP-GS is consistent with limiting warming to 2°C (>67%) (C3), IMP-Neg shows a strategy that also limits warming to 2°C (>67%) but returns to nearly 1.5°C (>50%) by the end of the century (hence indicated as C2*). The other variants that can limit warming to 1.5°C (>50%) (C1) were selected. In addition to these IMPs, sensitivity cases that explore alternative warming levels (C3) for IMP-Neg and IMP-Ren are assessed (IMP-Neg-2.0 and IMP-Ren-2.0).

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The trajectory of future CO2 emissions plays a critical role in mitigation, given CO2 long-term impact and dominance in total greenhouse gas forcing. As shown in Figure 3.12, CO2 dominates total greenhouse gas emissions in the high-emissions scenarios but is also reduced most, going from scenarios in the highest to lower categories. In C4 and below, most scenarios exhibit net negative CO2 emissions in the second half of the century compensating for some of the residual emissions of non-CO2 gases as well as reducing overall warming from an intermediate peak. Still, early emission reductions and further reductions in non-CO2 emissions can also lead to scenarios without net negative emissions in 2100, even in C1 and C3 (shown for the 85–95th percentile). In C1, avoidance of significant overshoot implies that immediate gross reductions are more relevant than long-term net negative emissions (explaining the lower number than in C2) but carbon dioxide removal (CDR) is still playing a role in compensating for remaining positive emissions in hard-to-abate sectors.

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The timing of net zero CO2or GHG emissions may differ across regions and sectors. Achieving net zero emissions globally implies that some sectors and regions must reach net zero CO2or GHG ahead of the time of global net zero CO2or GHG if others reach it later. Similarly, some sectors and regions would need to achieve net negative CO2 or GHG emissions to compensate for continued emissions by other sectors and regions after the global net zero year. Differences in the timing to reach net zero emissions between sectors and regions depend on multiple factors, including the potential of countries and sectors to reduce GHG emissions and undertake carbon dioxide removal (CDR), the associated costs, and the availability of policy mechanisms to balance emissions and removals between sectors and countries (Fyson et al. 2020; Strefler et al. 2021a; van Soest et al. 2021b). A lack of such mechanisms could lead to higher global costs to reach net zero emissions globally, but less interdependencies and institutional needs (Fajardy and Mac Dowell 2020). Sectors will reach net zero CO2 and GHG emissions at different times if they are aiming for such targets with sector-specific policies or as part of an economy-wide net zero emissions strategy integrating emissions reductions and removals across sectors. In the latter case, sectors with large potential for achieving net negative emissions would go beyond net zero to balance residual emissions from sectors with low potential, which in turn would take more time compared to the case of sector-specific action. Global pathways project global AFOLU emissions to reach global net zero CO2 the earliest, around 2030 to 2035 in pathways to limit warming to 2°C (>67%) or lower, by rapid reduction of deforestation and enhancing carbon sinks on land, although net zero GHG emissions from global AFOLU are typically reached 30 years later, if at all. The ability of global AFOLU CO2 emissions to reach net zero as early as in the 2030s in modelled pathways hinges on optimistic assumptions about the ability to establish global cost-effective mechanisms to balance emissions reductions and removals across regions and sectors. These assumptions have been challenged in the literature and the Special Report on Climate Change and Land (IPCC SRCCL).

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f The Illustrative Mitigation Pathway ‘Neg’ has extensive use of carbon dioxide removal (CDR) in the AFOLU, energy and the industry sectors to achieve net negative emissions. Warming peaks around 2060 and declines to below 1.5°C (50% likelihood) shortly after 2100. Whilst technically classified as C3, it strongly exhibits the characteristics of C2 high-overshoot pathways, hence it has been placed in the C2 category. See Box SPM.1 for an introduction of the IPs and IMPs.

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Returning warming to lower levels requires net negative CO2 emissions in the second half of the century (Clarke et al. 2014; Fuss et al. 2014; Rogelj et al. 2018 a). The amount of net negative CO2 emissions in pathways limiting warming to 1.5°C–2°C climate goals varies widely, with some pathways not deploying net negative CO2 emissions at all and others deploying up to –600 to –800 GtCO2. The amount of net negative CO2 emissions tends to increase with 2030 emissions levels (Figure 3.30e and Table 3.6). Studies confirmed the ability of net negative CO2 emissions to reduce warming, but pointed to path dependencies in the storage of carbon and heat in the Earth System and the need for further research particularly for cases of high overshoot (Zickfeld et al. 2016, 2021; Keller et al. 2018a,b; Tokarska et al. 2019). The AR6 WGI assessed the reduction in global surface temperature to be approximately linearly related to cumulative CO2 removal and, with lower confidence, that the amount of cooling per unit CO2 removed is approximately independent of the rate and amount of removal (AR6 WGI TS.3.3.2). Still there remains large uncertainty about a potential asymmetry between the warming response to CO2 emissions and the cooling response to net negative CO2 emissions (Zickfeld et al. 2021). It was also shown that warming can adversely affect the efficacy of carbon dioxide removal measures and hence the ability to achieve net negative CO2 emissions (Boysen et al. 2016).

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Obtaining net negative CO2 emissions requires massive deployment of carbon dioxide removal (CDR) in the second half of the century, on the order of 220 (160–370) GtCO2 for each 0.1°C degree of cooling (based on the assessment of the likely range of the transient response to cumulative CO2 emissions in AR6 WGI Section 5.5 in Chapter 5, not taking into account potential asymmetries in the temperature response to CO2 emissions and removals). CDR is assessed in detail in Section 12.3 of this report (see also Cross-Chapter Box 8 in Chapter 12). Here we only point to the finding that CDR ramp-up rates and absolute deployment levels are tightly limited by techno-economic, social, political, institutional and sustainability constraints (Smith et al. 2016; Boysen et al. 2017; Fuss et al. 2018, 2020; Nemet et al. 2018; Hilaire et al. 2019; Jia et al. 2019) (Section 12.3). CDR therefore cannot be deployed arbitrarily to compensate any degree of overshoot. A fraction of models was not able to compute pathways that would follow the mitigation ambition in unconditional and conditional NDCs until 2030 and return warming to below 1.5°C by 2100 (Luderer et al. 2018; Roelfsema et al. 2020; Riahi et al. 2021). There exists a three-way trade-off between near-term emissions developments until 2030, transitional challenges during 2030–50, and long-term CDR deployment post-2050 (Sanderson et al. 2016; Holz et al. 2018; Strefler et al. 2018). For example, Strefler et al. (2018) find that if CO2 emission levels stay at around 40 GtCO2 until 2030, within the range of what is projected for NDCs announced prior to COP26, rather than being halved to 20 GtCO2 until 2030, CDR deployment in the second half of the century would have to increase by 50–100%, depending on whether the 2030–2050 CO2 emissions reduction rate is doubled from 6% to 12% or kept at 6% yr –1. This three-way trade-off has also been identified at the national level (Pan et al. 2020).

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Yes. Achieving net zero CO2 emissions and sustaining them into the future is sufficient to stabilise the CO2-induced warming signal which scales with the cumulative net amount of CO2 emissions. At the same time, the warming signal of non-CO2GHGs can be stabilised or reduced by declining emissions that lead to stable or slightly declining concentrations in the atmosphere. For short-lived GHGs with atmospheric lifetimes of less than 20 years, this is achieved when residual emissions are reduced to levels that are lower than the natural removal of these gases in the atmosphere. Taken together, mitigation pathways that bring CO2 emissions to net zero and sustain it, while strongly reducing non-CO2GHGs to levels that stabilise or decline their aggregate warming contribution, will stabilise warming without using net negative CO2 emissions and with positive overall GHG emissions when aggregated using GWP-100. A considerable fraction of pathways that limit warming to 1.5°C (>50%) with no or limited overshoot and limit warming to 2°C (>67%), respectively, do not or only marginally (<10 GtCO2 cumulative until 2100) deploy net negative CO2 emissions (26% and 46%, respectively) and do not reach net zero GHG emissions by the end of the century (48% and 70%, respectively). This is no longer the case in pathways that return warming to 1.5°C (>50%) after a high overshoot (typically >0.1°C). All of these pathways deploy net negative emissions on the order of 360 (60–680) GtCO2 (median and 5–95th percentile) and 87% achieve net negative GHGs emissions in AR6 GWP-100 before the end of the century. Hence, global net negative CO2 emissions, and net zero or net negative GHG emissions, are only needed to decline, not to stabilise global warming. The deployment of carbon dioxide removal (CDR) is distinct from the deployment of net negative CO2 emissions, because it is also used to neutralise residual CO2 emissions to achieve and sustain net zero CO2 emissions. CDR deployment can be considerable in pathways without net negative emissions and all pathways limiting warming to 1.5°C use it to some extent.

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Not all regions and sectors must reach net zero CO2 or GHG emissions individually to achieve global net zero CO2 or GHG emissions, respectively; instead, positive emissions in one sector or region can be compensated by net negative emissions from another sector or region. The time each sector or region reaches net zero CO2 or GHG emissions depends on the mitigation options available, the cost of those options, and the policies implemented (including any consideration of equity or fairness). Most modelled pathways that likely limit warming to 2°C (>67%) above pre-industrial levels and below use land-based CO2 removal such as afforestation/reforestation and BECCS to achieve net zero CO2 and net zero GHG emissions even while some CO2 and non-CO2 emissions continue to occur. Pathways with more demand-side interventions that limit the amount of energy we use, or where the diet that we consume is changed, can achieve net zero CO2, or net zero GHG emissions with less carbon dioxide removal (CDR). All available studies require at least some kind of carbon dioxide removal to reach net zero; that is, there are no studies where absolute zero GHG or even CO2 emissions are reached by deep emissions reductions alone.

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Bistline, J.E.T. and G.J. Blanford, 2021: Impact of carbon dioxide removal technologies on deep decarbonization of the electric power sector. Nat. Commun. , 12(1) , 3732, doi:10.1038/s41467-021-23554-6.

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Fajardy, M. and N. Mac Dowell, 2020: Recognizing the Value of Collaboration in Delivering Carbon Dioxide Removal. One Earth, 3(2) , 214–225, doi.org/10.1016/j.oneear.2020.07.014.

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Fuss, S. et al., 2020: Moving toward Net-Zero Emissions Requires New Alliances for Carbon Dioxide Removal. One Earth, 3(2) , 145–149, doi:10.1016/j.oneear.2020.08.002.

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Fyson, C.L., S. Baur, M. Gidden, and C.F. Schleussner, 2020: Fair-share carbon dioxide removal increases major emitter responsibility. Nat. Clim. Change, 10(9) , 836–841, doi:10.1038/s41558-020-0857-2.

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Giannousakis, A. et al., 2021: How uncertainty in technology costs and carbon dioxide removal availability affect climate mitigation pathways. Energy, 216, 119253, doi.org/10.1016/j.energy.2020.119253.

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Grant, N., A. Hawkes, S. Mittal, and A. Gambhir, 2021: The policy implications of an uncertain carbon dioxide removal potential. Joule, 5, doi:10.1016/j.joule.2021.09.004.

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Hofmann, M., S. Mathesius, E. Kriegler, D.P. va. van Vuuren, and H.J. Schellnhuber, 2019: Strong time dependence of ocean acidification mitigation by atmospheric carbon dioxide removal. Nat. Commun. , 10(1) , 5592, doi:10.1038/s41467-019-13586-4.

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Holz, C., L.S. Siegel, E. Johnston, A.P. Jones, and J. Sterman, 2018: Ratcheting ambition to limit warming to 1.5°C trade-offs between emission reductions and carbon dioxide removal. Environ. Res. Lett. , 13(6) , doi:10.1088/1748-9326/aac0c1.

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Honegger, M., A. Michaelowa, and J. Roy, 2021: Potential implications of carbon dioxide removal for the sustainable development goals. Clim. Policy, 21(5) , 678–698, doi:10.1080/14693062.2020.1843388.

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Keller, D.P. et al., 2018a: The Carbon Dioxide Removal Model Intercomparison Project (CDRMIP): Rationale and experimental protocol for CMIP6. Geosci. Model Dev. , 11(3) , 1133–1160, doi:10.5194/gmd-11-1133-2018.

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Keller, D.P. et al., 2018b: The Effects of Carbon Dioxide Removal on the Carbon Cycle. Curr. Clim. Change Reports, 4(3) , 250–265, doi:10.1007/s40641-018-0104-3.

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Kriegler, E., O. Edenhofer, L. Reuster, G. Luderer, and D. Klein, 2013b: Is atmospheric carbon dioxide removal a game changer for climate change mitigation?Clim. Change, 118(1) , 45–57, doi:10.1007/s10584-012-0681-4.

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Rickels, W., F. Reith, D. Keller, A. Oschlies, and M.F. Quaas, 2018: Integrated Assessment of Carbon Dioxide Removal. Earth’s Future, 6(3) , 565–582, doi:10.1002/2017EF000724.

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Strefler, J. et al., 2021a: Carbon dioxide removal technologies are not born equal. Environ. Res. Lett. , 16(7) , 74021, doi:10.1088/1748-9326/ac0a11.

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Various assessment frameworks have been proposed to analyse fair share ranges for NDCs. The literature on equity frameworks including quantification of national emissions allocation is assessed in section 4.5 (Sections 13.4.2, 14.3.2 and 14.5.3). Recent literature has assessed equity, analysing how fairness is expressed in NDCs in a bottom-up manner (Mbeva and Pauw 2016; Cunliffe et al. 2019; Winkler et al. 2018). Some studies compare NDC ambition level with different effort sharing regimes and which principles are applied to various countries and regions (Peters et al. 2015; Pan et al. 2017; Robiou Du Pont et al. 2017; Holz et al. 2018; Robiou du Pont and Meinshausen 2018; van den Berg et al. 2019). Others propose multi-dimensional evaluation schemes for NDCs that combine a range of indicators, including the NDC targets, cost-effectiveness compared to global models, recent trends and policy implementation into consideration (Aldy et al. 2017; Höhne et al. 2018). Yet other literature evaluates NDC ambition against factors such as technological progress of energy efficiency and low-carbon technologies (Jiang et al. 2017; Kuramochi et al. 2017; Wakiyama and Kuramochi 2017), synergies with adaptation plans (Fridahl and Johansson 2017), the obligations to deploy carbon dioxide removal technologies like bioenergy with carbon capture and storage (BECCS) in the future implied by their near-term emission reductions where they are not reflected on in the first NDCs (Peters and Geden 2017; Fyson et al. 2020; Pozo et al. 2020; Mace et al. 2021). Others identify possible risks of unfairness when applying GWP* as emissions metric at national scale (Rogelj and Schleussner 2019). A recent study on national fair shares draws on principles of international environmental law, excludes approaches based on cost and grandfathering, thus narrowing the range of national fair shares previously assessed, and apply this to the quantification of national fair share emissions targets (Rajamani et al. 2021).

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Studies have identified socio-technological pathways to help achieve net zero CO2 and GHG targets at national scale, that in aggregate are crucial to keeping global temperature below agreed limits. However, most of the literature focuses on supply-side options, including carbon dioxide removal mechanisms (BECCS, afforestation, and others) that are not fully commercialised (Cross-Chapter Box 8 in Chapter 12). Costs to research, deploy, and scale up these technologies are often high. Recent studies have addressed lowering demand through energy conversion efficiency improvements, but few studies have considered demand reduction through efficiency (Grubler et al. 2018) and the related supply implications and mitigation measures.

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Fyson, C.L., S. Baur, M. Gidden, and C.-F. Schleussner, 2020: Fair-share carbon dioxide removal increases major emitter responsibility. Nat. Clim. Change, 10(9) , 836–841, doi:10.1038/s41558-020-0857-2.

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Giannousakis, A. et al., 2020: How uncertainty in technology costs and carbon dioxide removal availability affect climate mitigation pathways. Energy, 216, 119253, doi:10.1016/j.energy.2020.119253.

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Mace, M.J., C.L. Fyson, M. Schaeffer, and W.L. Hare, 2021: Large‐Scale Carbon Dioxide Removal to Meet the 1.5°C Limit: Key Governance Gaps, Challenges and Priority Responses. Glob. Policy, 12(S1) , 67–81, doi:10.1111/1758-5899.12921.

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Pozo, C., Á. Galán-Martín, D.M. Reiner, N. Mac Dowell, and G. Guillén-Gosálbez, 2020: Equity in allocating carbon dioxide removal quotas. Nat. Clim. Change, 10(7) , 640–646, doi:10.1038/s41558-020-0802-4.

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Climate change has negative impacts on agricultural productivity in general, including unequal geographical distribution (Chapter 3). On top of that, there is also a risk that climate change mitigation aimed at achieving stringent climate goals could negatively affect food access and food security (Akimoto et al. 2012; Fujimori et al. 2019; Hasegawa et al. 2018). If not managed properly, the risk of hunger due to climate policies such as large-scale bioenergy production increases remarkably if the 2°C and 1.5°C targets are implemented (Section 3.7.1). Taking the highest median values from different IAMs for given classes of scenarios, up to 14.9 GtCO2 yr –1 carbon dioxide removal (CDR) from BECCS is required in 2100, and 2.4 GtCO2 yr –1 for afforestation. Across the different scenarios, median changes in global forest area throughout the 21st century reach the required 7.2 Mkm 2 increases between 2010 and 2100, and agricultural land used for second-generation bioenergy crop production may require up to 6.6 Mkm 2 in 2100, increasing the competition for land and potentially affecting sustainable development (AR6 scenarios database).

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As pointed out in Chapter 6, the achievement of long-term temperature goals in line with the Paris Agreement requires the rapid penetration of renewable energy and a timely phasing out of fossil fuels, especially coal, from the global energy system. Limiting warming to 1.5°C (>50%) with no or limited overshoot means that global CO2 emissions must reach ‘net zero’ in 2050/2060 (IPCC 2018). Net zero emissions imply that fossil fuel use is minimised and replaced by renewables and other low-carbon primary forms of energy, or that the residual emissions from fossil fuels are offset by carbon dioxide removal (CDR). The 1.5°C scenario requires a 2–3% annual improvement rate in carbon intensities till 2050. The historical record only shows a slight improvement in the carbon intensity rate of global energy supplies, far from what is required to limit global warming to 2°C (>67%), or limit warming to 1.5°C (>50%) with no or limited overshoot.

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GTEMs show broad ranges for future travel demand, particularly for the freight sector. These results show more dependency on models than on baseline or policy scenarios. According to ITF Transport Outlook (ITF 2019), global passenger transport and freight demand could more than double by 2050 in a business-as-usual scenario. Mulholland et al. (2018) suggest the freight sector could grow 2.4-fold over 2015–2050 in the reference scenario, with the majority of growth attributable to developing countries. The IEA suggests a more modest increase in passenger transport, from 51 trillion pkm in 2014 to 110 trillion pkm in 2060, in a reference scenario without climate policies and a climate scenario that would limit emissions below 2°C. The demand for land-based freight transport in 2060 is, however, slightly lower in the climate scenario (116 trillion tkm) compared to the reference scenario (130 trillion tkm) (IEA 2017b). The ITF, however, suggests that ambitious decarbonisation policies could reduce global demand for passenger transport by 13–20% in 2050, compared to the business-as-usual scenario (ITF 2019; ITF 2021). The reduction in vehicle travel through shared mobility could reduce emissions from urban passenger transport by 30% compared to the business-as-usual scenario. Others suggest that reductions larger than 25%, on average, for both passenger and freight in 2030 and 2050 may be needed to achieve very low carbon emissions pathways (Fisch-Romito and Guivarch 2019). In the absence of large-scale carbon dioxide removal, few global studies highlight the need for significant demand reduction in critical sectors (aviation, shipping and road freight) in well below 2°C scenarios (van Vuuren et al. 2018; Grant et al. 2021; Sharmina et al. 2021).

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The pulp and paper industry has significant biogenic carbon emissions but relatively small fossil carbon emissions. Pulp mills have access to biomass residues and by-products and in paper mills the use of process heat at low to medium temperatures allows for electrification (high confidence). Competition for feedstock will increase if wood substitutes for building materials and petrochemicals feedstock. The pulp and paper industry can also be a source of biogenic carbon dioxide and carbon for organic chemicals feedstock and carbon dioxide removal (CDR) using CCS. {11.4.1.4}

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While CCS and CCU share common capture technologies, what happens to the CO2 and therefore the strategies that will employ them can be very different. CCS can help maintain near-CO2 neutrality for fossil CO2 that passes through the process, with highly varying partially negative emissions if the source is biogenic (Hepburn et al. 2019), and fully negative emissions if the source is air capture, all not considering the energy used to drive the above processes. CCS has been covered in other IPCC publications at length, for example, IPCC (2005), and in most mitigation-oriented assessments since, for example, the IEA’s Energy Technology Perspectives (ETP) 2020 and Net Zero scenario reports (IEA 2021a, 2020a). The potentials and costs for CCS in industry vary considerably due to the diversity of industrial processes (Leeson et al. 2017), as well as the volume and purity of different flows of CO2 (Naims 2016); Kearns et al. (2021) provide a recent review. As a general rule it is not possible to capture all the CO2 emissions from an industrial plant. To achieve zero or negative emissions, CCS would need to be combined with some use of sustainably sourced biofuel or feedstock, or the remaining emissions would need to be offset by carbon dioxide removal (CDR) elsewhere.

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Care is required to clarify what is optimised (Dietz and Venmans 2019). Optimising a path towards a given temperature goal by a fixed date (e.g., 2100) gives time-inconsistent results backloaded to large, last-minute investment in carbon dioxide removal (CDR). ‘Cost-effective’ optimisations generate less initial effort than equivalent cost-benefit models (Dietz and Venmans 2019; Gollier 2021) as they do not incorporate benefits of reducing impacts earlier.

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Chapters 6 to 12 examine sectoral contributions and possibilities for mitigation. Chapter 6 summarises characteristics and trends in the energy sector, specifically supply, including the remarkable changes in the cost of some key technologies since AR5. Chapter 7 examines the roles of AFOLU, drawing upon and updating the recent Special Report, including the potential tensions between the multiple uses of land. Chapter 8 presents a holistic view of the trends and pressures of urban systems, as both a challenge and an opportunity for mitigation. Chapters 9 and 10 then examine two sectors which entwine with, but go well beyond, urban systems: buildings (Chapter 9) including construction materials and zero-carbon buildings; and transport (Chapter 10), including shipping and aviation and a wider look at mobility as a general service. Chapter 11 explores the contribution of industry, including supply chain developments, resource efficiency/circular economy, and the cross-system implications of decarbonisation for industrial systems. Finally, Chapter 12 takes a cross-sectoral perspective and explores cross-cutting issues like the interactions of biomass energy, food and land, and carbon dioxide removal.

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Bioenergy has the potential to be a high-value and large-scale mitigation option to support many different parts of the energy system. Bioenergy could be particularly valuable for sectors with limited alternatives to fossil fuels (e.g., aviation, heavy industry), production of chemicals and products, and, potentially, in carbon dioxide removal (CDR) via BECCS or biochar. While traditional biomass and first-generation biofuels are widely used today, the technology for large-scale production from advanced processes is not competitive, and growing dedicated bioenergy crops raises a broad set of sustainability concerns. Its long-term role in low-carbon energy systems is therefore uncertain ( high confidence). (Note that this section focuses on the key technological developments for deployment of commercial bioenergy.)

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Alleviating these issues would require some combination of increasing crop yields, improving conversion efficiencies, and developing advanced biotechnologies for increasing the fuel yield per tonne of feedstock (Henry et al. 2018). Policy structures would be necessary to retain biodiversity, manage water use, limit deforestation and land-use change emissions, and ultimately optimally integrate bioenergy with transforming ecosystems. Large-scale international trade of biomass might be required to support a global bioeconomy, raising questions about infrastructure, logistics, financing options, and global standards for bioenergy production and trade (Box 6.10). Additional institutional and economic barriers are associated with accounting of carbon dioxide removal, including BECCS (Fuss et al. 2014; Muratori et al. 2016; Fridahl and Lehtveer 2018).

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CO2 emissions from fuel combustion are the bottom line on energy system progress. Beyond CO2 emissions, primary energy demand by energy sources, final energy consumption by sectors, and total electricity demand provide a first order assessment of energy system transitions. The year at which CO2 emissions peak is also important. The Kaya Identity can be used to decompose energy system CO2 emissions into carbon intensity of the energy system (CO2 emissions from fossil-fuel combustion and industry divided by energy use), energy intensity (energy use divided by economic output), and economic output. The impacts of energy and climate policy are reflected in the changes of carbon intensity and energy intensity. Carbon intensity captures decarbonisation of energy supply systems, for example, through fuel switching from fossil fuels to non-fossil fuels, upscaling of low-carbon energy sources, and deploying carbon dioxide removal technologies. The carbon intensity of electricity is specifically important, given the role of the electricity sector in near-term mitigation. Economy-wide energy intensity represents efforts of demand-side energy, such as energy conservation, increase of energy performance of technologies, structural change of economy, and development of efficient urban infrastructure.

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Bistline, J.E.T. and G.J. Blanford, 2021a: Impact of carbon dioxide removal technologies on deep decarbonization of the electric power sector. Nat. Commun. , 12(1) , 3732, doi:10.1038/s41467-021-23554-6.

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Fajardy, M. and N. Mac Dowell, 2020: Recognizing the Value of Collaboration in Delivering Carbon Dioxide Removal. One Earth, 3(2) , 214–225, doi:10.1016/j.oneear.2020.07.014.

carbon dioxide removalresources/ipcc/cleaned_content/wg3/Chapter06/html_with_ids.html#references_p451

Field, C.B. and K.J. Mach, 2017: Rightsizing carbon dioxide removal. Science, 356(6339) , 706 LP – 707, doi:10.1126/science.aam9726.

carbon dioxide removalresources/ipcc/cleaned_content/wg3/Chapter06/html_with_ids.html#references_p701

Iyer, G. et al., 2021: The role of carbon dioxide removal in net-zero emissions pledges. Energy Clim. Change, 2, 100043, doi:10.1016/J.EGYCC.2021.100043.

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TheAgriculture, Forestry and Other Land Use1 (AFOLU) sector encompasses managed ecosystems and offers significant mitigation opportunities while delivering food, wood and other renewable resources as well as biodiversity conservation, provided the sector adapts to climate change. Land-based mitigation measures represent some of the most important options currently available. They can both deliver carbon dioxide removal (CDR) and substitute for fossil fuels, thereby enabling emissions reductions in other sectors. The rapid deployment of AFOLU measures is essential in all pathways staying within the limits of the remaining budget for a 1.5°C target ( high confidence). Where carefully and appropriately implemented, AFOLU mitigation measures are uniquely positioned to deliver substantial co-benefits and help address many of the wider challenges associated with land management. If AFOLU measures are deployed badly then, when taken together with the increasing need to produce sufficient food, feed, fuel and wood, they may exacerbate trade-offs with the conservation of habitats, adaptation, biodiversity and other services. At the same time the capacity of the land to support these functions may be threatened by climate change itself ( high confidence). {IPCC AR6 WGI, Figure SPM.7; IPCC AR6 WGII, 7.1, 7.6}

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None the less, the importance of mitigation within AFOLU was highlighted in all IPCC reports, with modelled scenarios demonstrating the considerable potential role and land-based mitigation forming an important component of pledged mitigation in Nationally Determined Contributions (NDCs) under the Paris Agreement. The sector was identified as the only one in which large-scale carbon dioxide removal (CDR) may currently and at short term be possible (e.g., through afforestation/reforestation or soil organic carbon management). This CDR component was deemed crucial to limit climate change and its impacts, which would otherwise lead to enhanced release of carbon from land. However, the SRCCL emphasised that mitigation cannot be pursued in isolation. The need for integrated response options, that mitigate and adapt to climate change, but also deal with land degradation and desertification, while enhancing food and fibre security, biodiversity and contributing to other SDGs has been made clear (IPCC 2019; IPBES 2019a; IPBES-IPCC 2021).

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Regreening in the Sahel and the consequent transformation of the landscape has resulted from the actions of hundreds of thousands of individuals responding to social and biophysical signals (Hanan 2018). This is an example for climate change mitigation, where eliminating regulations – versus increasing them – has led to carbon dioxide removal.

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Busch, J. et al., 2019: Potential for low-cost carbon dioxide removal through tropical reforestation. Nat. Clim. Change, 9(6) , 463–466, doi:10.1038/s41558-019-0485-x.

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Lenton, T.M., 2014: The global potential for carbon dioxide removal. In: Geoengineering of the Climate System[Harrison, R.M. and R.E. Hester (eds.)]. Royal Society of Chemistry, Cambridge, UK, pp. 52–79.

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Powell, T.W.R. and T.M. Lenton, 2012: Future carbon dioxide removal via biomass energy constrained by agricultural efficiency and dietary trends. Energy Environ. Sci. , 5(8) , 8116, doi:10.1039/c2ee21592f.

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Strefler, J. et al., 2021: Carbon dioxide removal technologies are not born equal. Environ. Res. Lett. , 16(7) , 074021, doi:10.1088/1748-9326/ac0a11.

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Other opportunities exist, such as building light-weighting or more efficient material production, use and disposal (Hertwich et al. 2020), fast-growing biomass sources such as hemp, straw or flax as insulation in renovation processes (Pittau et al. 2019), bamboo-based construction systems as an alternative to conventional high-impact systems in tropical and subtropical climates (Zea Escamilla et al. 2018). Earth architecture is still limited to a niche (Morel and Charef 2019). See also Cross-Chapter Box 9 in Chapter 13 for carbon dioxide removal and its role in mitigation strategies.

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Geden, O., G.P. Peters, and V. Scott, 2019: Targeting carbon dioxide removal in the European Union. Clim. Policy, 19(4) , 487–494, doi:10.1080/14693062.2018.1536600.

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Vonhedemann, N., Z. Wurtzebach, T.J. Timberlake, E. Sinkular, and C.A. Schultz, 2020: Forest policy and management approaches for carbon dioxide removal: Forest Policy and Management for CDR. Interface Focus, 10(5) , doi:10.1098/rsfs.2020.0001.

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It is worth noting that Article 4.1 recognises that ‘peaking will take longer for developing countries’ and that the balance between emissions and removals needs to be on the ‘basis of equity, and in the context of sustainable development and efforts to eradicate poverty’. This suggests that not all countries are expected to reach net zero GHG emissions at the same time, or in the same manner. If global cost-effective 1.5°C and 2°C scenarios from integrated assessment models are taken, without applying an equity principle, the results suggest that domestic net zero GHG and CO2 emissions would be reached a decade earlier than the global average in Brazil and the USA and later in India and Indonesia (van Soest et al. 2021). By contrast, if equity principles are taken into account countries like Canada and the EU would be expected to phase out earlier than the cost-optimal scenarios indicate, and countries like China and Brazil could phase out emissions later, as well as other countries with lower per-capita emissions (van Soest et al. 2021). Some suggest that the application of such fairness considerations could bring forward the net zero GHG date for big emitting countries by up to 15 to 35 years as compared to the global least-cost scenarios (Lee et al. 2021b). In any case, reaching net zero GHG emissions requires to some extent the use of carbon dioxide removal (CDR) methods as there are important sources of non-CO2GHGs, such as methane and nitrous oxide, that cannot be fully eliminated (IPCC 2018b). However, there are divergent views on different CDR methods, policy choices determine the degree to which and the type of CDR methods that are considered and there is a patchwork of applicable regulatory instruments. There are also uncertainties and governance challenges associated with CDR methods which render tracking progress against net zero GHG emissions challenging (Mace et al. 2021). Researchers have noted that given the key role of CDR in net zero targets and 1.5°C compatible pathways, and the fact that it presents ‘significant costs to current and future generations’, it is important to consider what an equitable distribution of CDR might look like (UNFCCC 2019c; Day et al. 2020; Lee et al. 2021b).

solar radiation modificationresources/ipcc/cleaned_content/wg3/Chapter14/html_with_ids.html#14.4.5_p1

While Solar Radiation Modification (SRM) and carbon dioxide removal (CDR) were often referred to as ‘geoengineering’ in earlier IPCC reports and in the literature, IPCC SR1.5 started to explore SRM and CDR more thoroughly and to highlight the differences between – but also within – both approaches more clearly. This section assesses international governance of both SRM and CDR, recognising that CDR, as a mitigation option, is covered elsewhere in this report, whereas SRM is not. Chapter 12 of this report covers the emerging national, sub-national and non-state governance of CDR, while Chapters 6, 7 and 12 also assess the mitigation potential, risks and co-benefits of some CDR options. Chapters 4 and 5 of AR6 WGI assess the physical climate system and biogeochemical responses to different SRM and CDR methods. Cross-Working Group Box 4 on SRM (AR6 WGII, Chapter 16; and Cross-Working Group Box 4 in this chapter) gives a brief overview of Solar Radiation Modification methods, risks, benefits, ethics and governance.

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Specific regulations on CDR options have been limited to those posing transboundary risks, namely the use of ocean fertilisation. In a series of separate decisions from 2008 to 2013, Parties to the London Convention and Protocol limited ocean fertilisation activities to only those of a research character, and in 2012 the CBD made a non-legally-binding decision to do the same, further requiring such research activities to be limited scale, and carried out under controlled conditions, until more knowledge is gained to be able to assess the risks (GESAMP 2019; Burns and Corbett 2020). In doing so they have taken a precautionary approach (Sands and Peel, 2018). The London Convention and Protocol has also developed an Assessment Framework for Scientific Research Involving Ocean Fertilisation (London Convention/Protocol 2010) and in 2013 adopted amendments (which are not yet in force) to regulate marine carbon dioxide removal activities, including ocean fertilisation.

carbon dioxide removalresources/ipcc/cleaned_content/wg3/Chapter14/html_with_ids.html#FAQ 14.3 | Are there any important gaps in international cooperation, which will need to be filled in order for countries to achieve the objectives of the Paris Agreement, such as holding temperature increase to well below 2°C and pursuing efforts towards 1.5°C above pre-industrial levels?_p1

While international cooperation is contributing to global mitigation efforts, its effects are far from uniform. Cooperation has contributed to setting a global direction of travel, and to falling greenhouse gas emissions in many countries and avoided emissions in others. It remains to be seen whether it can achieve the kind of transformational changes needed to achieve the Paris Agreement’s long-term global goals. There appears to be a large potential role for international cooperation to better address sector-specific technical and infrastructure challenges that are associated with such transformational changes. Finalising the rules to pursue voluntary cooperation, such as through international carbon market mechanisms and public climate finance in the implementation of NDCs, without compromising environmental integrity, may play an important role in accelerating mitigation efforts in developing countries. Finally, there is room for international cooperation to more explicitly address transboundary issues associated with carbon dioxide removal and solar radiation management.

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Fyson, C.L., S. Baur, M. Gidden, and C.-F. Schleussner, 2020: Fair-share carbon dioxide removal increases major emitter responsibility. Nat. Clim. Change, 10(9) , 836–841, doi:10.1038/s41558-020-0857-2.

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Honegger, M., M. Poralla, A. Michaelowa, and H.-M. Ahonen, 2021b: Who Is Paying for Carbon Dioxide Removal? Designing Policy Instruments for Mobilizing Negative Emissions Technologies. Front. Clim. , 3 (June), 1–15, doi:10.3389/fclim.2021.672996.

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Lee, K., C. Fyson, and C.-F. Schleussner, 2021b: Fair distributions of carbon dioxide removal obligations and implications for effective national net-zero targets. Environ. Res. Lett. , 16(9) , 94001, doi:10.1088/1748-9326/ac1970.

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Mace, M.J., C. Fyson, M. Schaeffer, and B. Hare, 2018: Governing large-scale carbon dioxide removal: are we ready?Carnegie Climate Geoengineering Governance Initiative, New York, NY, USA, 46 pp. www. c2g2.net/ wp-content/uploads/C2G2-2018-CDR-Governance-1.pdf (Accessed October 31, 2021).

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Mace, M.J., C.L. Fyson, M. Schaeffer, and W.L. Hare, 2021: Large-Scale Carbon Dioxide Removal to Meet the 1.5°C Limit: Key Governance Gaps, Challenges and Priority Responses. Glob. Policy, 12 (S1), 67–81, doi:10.1111/1758-5899.12921.

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Pozo, C., Á. Galán-Martín, D.M. Reiner, N. Mac Dowell, and G. Guillén-Gosálbez, 2020: Equity in allocating carbon dioxide removal quotas. Nat. Clim. Change, 10, 640–646, doi:10.1038/s41558-020-0802-4.

bioenergy with carbon capture and storageresources/ipcc/cleaned_content/wg3/Chapter12/html_with_ids.html#executive-summary_p2

Carbon dioxide removal (CDR) is a necessary element to achieve net zero CO2and greenhouse gas (GHG) emissions both globally and nationally, counterbalancing residual emissions from hard-to-transition sectors. It is a key element in scenarios that limit warming to 2°C (>67%) or lower by 2100 (robust evidence, high agreement). Implementation strategies need to reflect that CDR methods differ in terms of removal process, timescale of carbon storage, technological maturity, mitigation potential, cost, co-benefits, adverse side effects, and governance requirements. All Illustrative Mitigation Pathways (IMPs) use land-based biological CDR (primarily afforestation/reforestation (A/R)) and/or bioenergy with carbon capture and storage (BECCS) and some include direct air carbon capture and storage (DACCS). As a median value (5–95% range) across the scenarios that limit warming to 2°C (>67%) or lower, cumulative volumes of BECCS, CO2 removal from AFOLU (mainly A/R), and DACCS reach 328 (168–763) gigatonnes of CO2 equivalent (GtCO2), 252 (20–418) GtCO2, and 29 (0–339) GtCO2 for the 2020–2100 period, with annual volumes at 2.75 (0.52–9.45) GtCO2 yr –1 for BECCS, 2.98 (0.23–6.38) GtCO2 yr –1 for the CO2 removal from AFOLU (mainly A/R), and 0.02 (0–1.74) GtCO2 yr –1 for DACCS, in 2050. {12.3, Cross-Chapter Box 8 in this chapter}

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The scope of this chapter was motivated by the need for a succinct bottom-up cross-sectoral view of greenhouse gas (GHG) emissions mitigation coupled with the desire to provide systemic perspectives on critical mitigation potentials and options that go beyond individual sectors and cover cross-sectoral topics such as food systems, land systems, and carbon dioxide removal (CDR) methods. Driven by this motivation, Chapter 12 provides a focused thematic assessment of CDR methods and food systems, followed by consideration of land-related impacts of mitigation options (land-based CDR and other mitigation options that occupy land) and other cross-sectoral impacts of mitigation, with emphasis on synergies and trade-offs between mitigation options, and between mitigation and other environmental and socio-economic objectives. The systems focus is unique to the Sixth Assessment Report (AR6) of the IPCC and is of critical policy relevance as it informs coordinated approaches to planning interventions that deliver multiple benefits and minimise trade-offs, and coordinated policy approaches to support such planning, to tap relatively under-explored areas for the strengthening and acceleration of mitigation efforts in the short to medium term, and for dealing with residual emissions in hard-to-transition sectors in the medium to long term.

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Carbon dioxide removal (CDR) refers to a cluster of technologies, practices, and approaches that remove and sequester carbon dioxide from the atmosphere and durably store the carbon in geological, terrestrial, or ocean reservoirs, or in products. Despite the common feature of removing carbon dioxide, CDR methods can be very different (Smith et al. 2017). There are proposed methods for removal of non-CO2 greenhouse gases such as methane (Jackson et al. 2019; Jackson et al. 2021) but scarcity of literature on these methods prevents assessment here.

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Trade-offs and spillover effects: Air quality could be adversely affected by the spreading of rock dust (Edwards et al. 2017), though this can partly be ameliorated by water-spraying (Grundnig et al. 2006). As noted above, any significant expansion of the mining industry would require careful assessment to avoid possible detrimental effects on biodiversity (Amundson et al. 2015). The processing of an additional 10 billion tonnes of rock would require up to 3000 Terawatt-hours of energy, which could represent approximately 0.1–6 % of global electricity use in 2100. The emissions associated with this additional energy generation may reduce the net carbon dioxide removal by up to 30% with present-day grid average emissions, but this efficiency loss would decrease with low-carbon power (Beerling et al. 2020).

carbon dioxide removalresources/ipcc/cleaned_content/wg3/Chapter12/html_with_ids.html#FAQ 12.1 | How could new technologies to remove carbon dioxide from the atmosphere contribute to climate change mitigation?_p2

The carbon dioxide removal (CDR) methods studied so far have different removal potentials, costs, co-benefits and side effects. Some biological methods for achieving CDR, like afforestation/reforestation or wetland restoration, have long been practised. If implemented well, these practices can provide a range of co-benefits, but they can also have adverse side effects such as biodiversity loss or food price increases. Other chemical and geochemical approaches to CDR include direct air carbon capture and storage (DACCS), enhanced weathering or ocean alkalinity enhancement. They are generally less vulnerable to reversal than biological methods.

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Bertram, C. and C. Merk, 2020: Public Perceptions of Ocean-Based Carbon Dioxide Removal: The Nature-Engineering Divide?Front. Clim. , 2, 31, doi:10.3389/fclim.2020.594194.

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Bistline, J.E.T. and G.J. Blanford, 2021: Impact of carbon dioxide removal technologies on deep decarbonization of the electric power sector. Nat. Commun. , 12(1) , 3732, doi:10.1038/s41467-021-23554-6.

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Buylova, A., M. Fridahl, N. Nasiritousi, and G. Reischl, 2021: Cancel (Out) Emissions? The Envisaged Role of Carbon Dioxide Removal Technologies in Long-Term National Climate Strategies. Front. Clim. , 3, 63, doi:10.3389/fclim.2021.675499.

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Cox, E., E. Spence, and N. Pidgeon, 2020a: Public perceptions of carbon dioxide removal in the United States and the United Kingdom. Nat. Clim. Change, 10(8) , 744–749, doi:10.1038/s41558-020-0823-z.

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Fajardy, M. and N. Mac Dowell, 2020: Recognizing the Value of Collaboration in Delivering Carbon Dioxide Removal. One Earth, 3(2) , 214–225, doi:10.1016/j.oneear.2020.07.014.

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Field, C.B., and K.J. Mach, 2017: Rightsizing carbon dioxide removal. Science, 356(6339) , 706–707, doi:10.1126/science.aam9726.

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Fuss, S. et al., 2020: Moving toward Net-Zero Emissions Requires New Alliances for Carbon Dioxide Removal. One Earth, 3, 145–149, doi:10.1016/j.oneear.2020.08.002.

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Geden, O., G.P. Peters, and V. Scott, 2019: Targeting carbon dioxide removal in the European Union. Clim. Policy, 19(4) , 487–494, doi:10.1080/14693062.2018.1536600.

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Geden, O., V. Scott, and J. Palmer, 2018: Integrating carbon dioxide removal into EU climate policy: Prospects for a paradigm shift. Wiley Interdiscip. Rev. Clim. Change, 9, 1–10, doi:10.1002/wcc.521.

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Grant, N., A. Hawkes, S. Mittal, and A. Gambhir, 2021: The policy implications of an uncertain carbon dioxide removal potential. Joule, 5(10) , 2593–2605, doi:10.1016/j.joule.2021.09.004.

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Holz, C., L.S. Siegel, E. Johnston, A.P. Jones, and J. Sterman, 2018: Ratcheting ambition to limit warming to 1.5 °C–trade-offs between emission reductions and carbon dioxide removal. Environ. Res. Lett. , 13(6) , 64028, doi:10.1088/1748-9326/aac0c1.

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Honegger, M., A. Michaelowa, and J. Roy, 2020: Potential implications of carbon dioxide removal for the sustainable development goals. Clim. Policy, 21(5) , 678–698, doi:10.1080/14693062.2020.1843388.

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Honegger, M., W. Burns, and D.R. Morrow, 2021a: Is carbon dioxide removal ‘mitigation of climate change’?Rev. Eur. Comp. Int. Environ. Law, 00, 1–9, doi:10.1111/reel.12401.

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Honegger, M., M. Poralla, A. Michaelowa, and H.-M. Ahonen, 2021b: Who Is Paying for Carbon Dioxide Removal? Designing Policy Instruments for Mobilizing Negative Emissions Technologies. Front. Clim. , 3, 50, doi:10.3389/fclim.2021.672996.

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Iyer, G. et al., 2021: The role of carbon dioxide removal in net-zero emissions pledges. Energy Clim. Change, 2, 100043, doi:10.1016/j.egycc.2021.100043.

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Lee, K., C. Fyson, and C.-F. Schleussner, 2021: Fair distributions of carbon dioxide removal obligations and implications for effective national net-zero targets. Environ. Res. Lett. , 16(9) , 094001, doi:10.1088/1748-9326/ac1970.

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Lenton, T.M., 2014: The Global Potential for Carbon Dioxide Removal. In: Geoengineering of the Climate System[Harrison, R.M. and R.E. Hester, (eds.)], The Royal Society of Chemistry, Cambridge, UK, pp. 52–79.

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Lezaun, J., P. Healey, T. Kruger, and S.M. Smith, 2021: Governing Carbon Dioxide Removal in the UK: Lessons Learned and Challenges Ahead. Front. Clim. , 3, 89, doi:10.3389/fclim.2021.673859.

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Mace, M.J., C.L. Fyson, M. Schaeffer, and W.L. Hare, 2021: Large‐Scale Carbon Dioxide Removal to Meet the 1.5°C Limit: Key Governance Gaps, Challenges and Priority Responses. Glob. Policy, 12 (S1), 67–81, doi:10.1111/1758-5899.12921.

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Pozo, C., Á. Galán-Martín, D.M. Reiner, N. Mac Dowell, and G. Guillén-Gosálbez, 2020: Equity in allocating carbon dioxide removal quotas. Nat. Clim. Change, 10(7) , 640–646, doi:10.1038/s41558-020-0802-4.

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Rickels, W., A. Proelß, O. Geden, J. Burhenne, and M. Fridahl, 2021: Integrating Carbon Dioxide Removal Into European Emissions Trading. Front. Clim. , 3, 62, doi:10.3389/fclim.2021.690023.

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Schenuit, F. et al., 2021: Carbon Dioxide Removal Policy in the Making: Assessing Developments in 9 OECD Cases. Front. Clim. , 3, 638805, doi:10.3389/fclim.2021.638805.

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Shrum, T.R. et al., 2020: Behavioural frameworks to understand public perceptions of and risk response to carbon dioxide removal. Interface Focus, 10(5) , 20200002, doi:10.1098/rsfs.2020.0002.

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Smith, P. et al., 2017: Bridging the gap – Carbon dioxide removal. In: The UNEP Emissions Gap Report , United Nations Environment Programme, Nairobi, Kenya, pp. 58–66.

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Spence, E., E. Cox, and N. Pidgeon, 2021: Exploring cross-national public support for the use of enhanced weathering as a land-based carbon dioxide removal strategy. Clim. Change, 165(1) , 23, doi:10.1007/s10584-021-03050-y.

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Strefler, J., T. Amann, N. Bauer, E. Kriegler, and J. Hartmann, 2018: Potential and costs of carbon dioxide removal by enhanced weathering of rocks. Environ. Res. Lett. , 13(3) , 034010, doi:10.1088/1748-9326/aaa9c4.

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Strefler, J. et al., 2021: Carbon dioxide removal technologies are not born equal. Environ. Res. Lett. , 16(7) , 074021, doi:10.1088/1748-9326/ac0a11.

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VonHedemann, N., Z. Wurtzebach, T.J. Timberlake, E. Sinkular, and C.A. Schultz, 2020: Forest policy and management approaches for carbon dioxide removal. Interface Focus, 10(5) , 20200001, doi:10.1098/rsfs.2020.0001.

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Global net zero CO2 or GHG emissions can be achieved even if some sectors and regions are net emitters, provided that others reach net negative emissions (see Figure 4.1). The potential and cost of achieving net zero or even net negative emissions vary by sector and region. If and when net zero emissions for a given sector or region are reached depends on multiple factors, including the potential to reduce GHG emissions and undertake carbon dioxide removal, the associated costs, and the availability of policy mechanisms to balance emissions and removals between sectors and countries. (high confidence). {WGIII Box TS.6, WGIII Cross-Chapter Box 3}

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With every increment of warming, climate change impacts and risks will become increasingly complex and more difficult to manage. Many regions are projected to experience an increase in the probability of compound events with higher global warming, such as concurrent heatwaves and droughts, compound flooding and fire weather. In addition, multiple climatic and non-climatic risk drivers such as biodiversity loss or violent conflict will interact, resulting in compounding overall risk and risks cascading across sectors and regions. Furthermore, risks can arise from some responses that are intended to reduce the risks of climate change, e.g., adverse side effects of some emission reduction and carbon dioxide removal (CDR) measures (see 3.4.1). (high confidence) {WGI SPM C.2.7, WGI Figure SPM.6, WGI TS.4.3; WGII SPM B.1.7, WGII B.2.2, WGII SPM B.5, WGII SPM B.5.4, WGII SPM C.4.2, WGII SPM B.5, WGII CCB2}

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Multiple climate change risks will increasingly compound and cascade in the near term (high confidence). Many regions are projected to experience an increase in the probability of compound events with higher global warming (high confidence) including concurrent heatwaves and drought. Risks to health and food production will be made more severe from the interaction of sudden food production losses from heat and drought, exacerbated by heat-induced labour productivity losses (high confidence) (Figure 4.3). These interacting impacts will increase food prices, reduce household incomes, and lead to health risks of malnutrition and climate-related mortality with no or low levels of adaptation, especially in tropical regions (high confidence). Concurrent and cascading risks from climate change to food systems, human settlements, infrastructure and health will make these risks more severe and more difficult to manage, including when interacting with non-climatic risk drivers such as competition for land between urban expansion and food production, and pandemics (high confidence). Loss of ecosystems and their services has cascading and long-term impacts on people globally, especially for Indigenous Peoples and local communities who are directly dependent on ecosystems, to meet basic needs (high confidence). Increasing transboundary risks are projected across the food, energy and water sectors as impacts from weather and climate extremes propagate through supply-chains, markets, and natural resource flows (high confidence) and may interact with impacts from other crises such as pandemics. Risks also arise from some responses intended to reduce the risks of climate change, including risks from maladaptation and adverse side effects of some emissions reduction and carbon dioxide removal measures, such as afforestation of naturally unforested land or poorly implemented bioenergy compounding climate-related risks to biodiversity, food and water security, and livelihoods (high confidence) (see Section 3.4.1 and 4.5). {WGI SPM.2.7; WGII SPM B.2.1, WGII SPM B.5, WGII SPM B.5.1, WGII SPM B.5.2, WGII SPM B.5.3, WGII SPM B.5.4, . WGII Cross-Chapter Box COVID in Chapter 7; WGIII SPM C.11.2; SRCCL SPM A.5, SRCCL SPM A.6.5} (Figure 4.3)

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Chapter 3 assesses the emissions pathways literature in order to identify their key characteristics (both in commonalities and differences) and to understand how societal choices may steer the system into a particular direction (high confidence) . More than 2000 quantitative emissions pathways were submitted to the IPCC’s Sixth Assessment Report AR6 scenarios database, out of which 1202 scenarios included sufficient information for assessing the associated warming consistent with WGI. Five Illustrative Mitigation Pathways (IMPs) were selected, each emphasising a different scenario element as its defining feature: heavy reliance on renewables (IMP-Ren), strong emphasis on energy demand reductions (IMP-LD), extensive use of carbon dioxide removal (CDR) in the energy and the industry sectors to achieve net negative emissions (IMP-Neg), mitigation in the context of broader sustainable development (IMP-SP), and the implications of a less rapid and gradual strengthening of near-term mitigation actions (IMP-GS). {3.2, 3.3}

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Pathways following Nationally Determined Contributions (NDCs) announced prior to COP262 until 2030 reach annual emissions of 47–57GtCO2-eq by 2030, thereby making it impossible to limit warming to 1.5°C with no or limited overshoot and strongly increasing the challenge to limit warming to 2°C (>67%) (high confidence). A high overshoot of 1.5°C increases the risks from climate impacts and increases the dependence on large-scale carbon dioxide removal from the atmosphere. A future consistent with NDCs announced prior to COP26 implies higher fossil fuel deployment and lower reliance on low-carbon alternatives until 2030, compared to mitigation pathways with immediate action to limit warming to 2°C (>67%) or lower. To limit warming to 2°C (>67%) after following the NDCs to 2030, the pace of global GHG emission reductions would need to accelerate rapidly from 2030 onward: to an average of 1.4–2.0 GtCO2-eq yr –1 between 2030 and 2050, which is around two-thirds of the global CO2 emission reductions in 2020 due to the COVID-19 pandemic, and around 70% faster than in immediate action pathways that limit warming to 2°C (>67%). Accelerating emission reductions after following an NDC pathway to 2030 would be particularly challenging because of the continued buildup of fossil fuel infrastructure that would be expected to take place between now and 2030. {3.5, 4.2}

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aThe Illustrative Mitigation Pathway ‘Neg’ has extensive use of carbon dioxide removal (CDR) in the AFOLU, energy and the industry sectors to achieve net negative emissions. Warming peaks around 2060 and declines to below 1.5°C (50% likelihood) shortly after 2100. Whilst technically classified as C3, it strongly exhibits the characteristics of C2 high-overshoot pathways, hence it has been placed in the C2 category. See Box SPM.1 for an introduction of the IPs and IMPs.

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The Illustrative Mitigation Pathways (IMPs) properly explore different pathways consistent with meeting the long-term temperature goals of the Paris Agreement. They represent five different pathways that emerge from the overall assessment. The IMPs differ in terms of their focus, for example, placing greater emphasis on renewables (IMP-Ren), deployment of carbon dioxide removal that results in net negative global GHG emissions (IMP-Neg), and efficient resource use and shifts in consumption patterns, leading to low demand for resources, while ensuring a high level of services (IMP-LD). Other IMPs illustrate the implications of a less rapid introduction of mitigation measures followed by a subsequent gradual strengthening (IMP-GS), and how shifting global pathways towards sustainable development, including by reducing inequality, can lead to mitigation (IMP-SP) In the IMP framework, IMP-GS is consistent with limiting warming to 2°C (>67%) (C3), IMP-Neg shows a strategy that also limits warming to 2°C (>67%) but returns to nearly 1.5°C (>50%) by the end of the century (hence indicated as C2*). The other variants that can limit warming to 1.5°C (>50%) (C1) were selected. In addition to these IMPs, sensitivity cases that explore alternative warming levels (C3) for IMP-Neg and IMP-Ren are assessed (IMP-Neg-2.0 and IMP-Ren-2.0).

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The trajectory of future CO2 emissions plays a critical role in mitigation, given CO2 long-term impact and dominance in total greenhouse gas forcing. As shown in Figure 3.12, CO2 dominates total greenhouse gas emissions in the high-emissions scenarios but is also reduced most, going from scenarios in the highest to lower categories. In C4 and below, most scenarios exhibit net negative CO2 emissions in the second half of the century compensating for some of the residual emissions of non-CO2 gases as well as reducing overall warming from an intermediate peak. Still, early emission reductions and further reductions in non-CO2 emissions can also lead to scenarios without net negative emissions in 2100, even in C1 and C3 (shown for the 85–95th percentile). In C1, avoidance of significant overshoot implies that immediate gross reductions are more relevant than long-term net negative emissions (explaining the lower number than in C2) but carbon dioxide removal (CDR) is still playing a role in compensating for remaining positive emissions in hard-to-abate sectors.

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The timing of net zero CO2or GHG emissions may differ across regions and sectors. Achieving net zero emissions globally implies that some sectors and regions must reach net zero CO2or GHG ahead of the time of global net zero CO2or GHG if others reach it later. Similarly, some sectors and regions would need to achieve net negative CO2 or GHG emissions to compensate for continued emissions by other sectors and regions after the global net zero year. Differences in the timing to reach net zero emissions between sectors and regions depend on multiple factors, including the potential of countries and sectors to reduce GHG emissions and undertake carbon dioxide removal (CDR), the associated costs, and the availability of policy mechanisms to balance emissions and removals between sectors and countries (Fyson et al. 2020; Strefler et al. 2021a; van Soest et al. 2021b). A lack of such mechanisms could lead to higher global costs to reach net zero emissions globally, but less interdependencies and institutional needs (Fajardy and Mac Dowell 2020). Sectors will reach net zero CO2 and GHG emissions at different times if they are aiming for such targets with sector-specific policies or as part of an economy-wide net zero emissions strategy integrating emissions reductions and removals across sectors. In the latter case, sectors with large potential for achieving net negative emissions would go beyond net zero to balance residual emissions from sectors with low potential, which in turn would take more time compared to the case of sector-specific action. Global pathways project global AFOLU emissions to reach global net zero CO2 the earliest, around 2030 to 2035 in pathways to limit warming to 2°C (>67%) or lower, by rapid reduction of deforestation and enhancing carbon sinks on land, although net zero GHG emissions from global AFOLU are typically reached 30 years later, if at all. The ability of global AFOLU CO2 emissions to reach net zero as early as in the 2030s in modelled pathways hinges on optimistic assumptions about the ability to establish global cost-effective mechanisms to balance emissions reductions and removals across regions and sectors. These assumptions have been challenged in the literature and the Special Report on Climate Change and Land (IPCC SRCCL).

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f The Illustrative Mitigation Pathway ‘Neg’ has extensive use of carbon dioxide removal (CDR) in the AFOLU, energy and the industry sectors to achieve net negative emissions. Warming peaks around 2060 and declines to below 1.5°C (50% likelihood) shortly after 2100. Whilst technically classified as C3, it strongly exhibits the characteristics of C2 high-overshoot pathways, hence it has been placed in the C2 category. See Box SPM.1 for an introduction of the IPs and IMPs.

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Returning warming to lower levels requires net negative CO2 emissions in the second half of the century (Clarke et al. 2014; Fuss et al. 2014; Rogelj et al. 2018 a). The amount of net negative CO2 emissions in pathways limiting warming to 1.5°C–2°C climate goals varies widely, with some pathways not deploying net negative CO2 emissions at all and others deploying up to –600 to –800 GtCO2. The amount of net negative CO2 emissions tends to increase with 2030 emissions levels (Figure 3.30e and Table 3.6). Studies confirmed the ability of net negative CO2 emissions to reduce warming, but pointed to path dependencies in the storage of carbon and heat in the Earth System and the need for further research particularly for cases of high overshoot (Zickfeld et al. 2016, 2021; Keller et al. 2018a,b; Tokarska et al. 2019). The AR6 WGI assessed the reduction in global surface temperature to be approximately linearly related to cumulative CO2 removal and, with lower confidence, that the amount of cooling per unit CO2 removed is approximately independent of the rate and amount of removal (AR6 WGI TS.3.3.2). Still there remains large uncertainty about a potential asymmetry between the warming response to CO2 emissions and the cooling response to net negative CO2 emissions (Zickfeld et al. 2021). It was also shown that warming can adversely affect the efficacy of carbon dioxide removal measures and hence the ability to achieve net negative CO2 emissions (Boysen et al. 2016).

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Obtaining net negative CO2 emissions requires massive deployment of carbon dioxide removal (CDR) in the second half of the century, on the order of 220 (160–370) GtCO2 for each 0.1°C degree of cooling (based on the assessment of the likely range of the transient response to cumulative CO2 emissions in AR6 WGI Section 5.5 in Chapter 5, not taking into account potential asymmetries in the temperature response to CO2 emissions and removals). CDR is assessed in detail in Section 12.3 of this report (see also Cross-Chapter Box 8 in Chapter 12). Here we only point to the finding that CDR ramp-up rates and absolute deployment levels are tightly limited by techno-economic, social, political, institutional and sustainability constraints (Smith et al. 2016; Boysen et al. 2017; Fuss et al. 2018, 2020; Nemet et al. 2018; Hilaire et al. 2019; Jia et al. 2019) (Section 12.3). CDR therefore cannot be deployed arbitrarily to compensate any degree of overshoot. A fraction of models was not able to compute pathways that would follow the mitigation ambition in unconditional and conditional NDCs until 2030 and return warming to below 1.5°C by 2100 (Luderer et al. 2018; Roelfsema et al. 2020; Riahi et al. 2021). There exists a three-way trade-off between near-term emissions developments until 2030, transitional challenges during 2030–50, and long-term CDR deployment post-2050 (Sanderson et al. 2016; Holz et al. 2018; Strefler et al. 2018). For example, Strefler et al. (2018) find that if CO2 emission levels stay at around 40 GtCO2 until 2030, within the range of what is projected for NDCs announced prior to COP26, rather than being halved to 20 GtCO2 until 2030, CDR deployment in the second half of the century would have to increase by 50–100%, depending on whether the 2030–2050 CO2 emissions reduction rate is doubled from 6% to 12% or kept at 6% yr –1. This three-way trade-off has also been identified at the national level (Pan et al. 2020).

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Yes. Achieving net zero CO2 emissions and sustaining them into the future is sufficient to stabilise the CO2-induced warming signal which scales with the cumulative net amount of CO2 emissions. At the same time, the warming signal of non-CO2GHGs can be stabilised or reduced by declining emissions that lead to stable or slightly declining concentrations in the atmosphere. For short-lived GHGs with atmospheric lifetimes of less than 20 years, this is achieved when residual emissions are reduced to levels that are lower than the natural removal of these gases in the atmosphere. Taken together, mitigation pathways that bring CO2 emissions to net zero and sustain it, while strongly reducing non-CO2GHGs to levels that stabilise or decline their aggregate warming contribution, will stabilise warming without using net negative CO2 emissions and with positive overall GHG emissions when aggregated using GWP-100. A considerable fraction of pathways that limit warming to 1.5°C (>50%) with no or limited overshoot and limit warming to 2°C (>67%), respectively, do not or only marginally (<10 GtCO2 cumulative until 2100) deploy net negative CO2 emissions (26% and 46%, respectively) and do not reach net zero GHG emissions by the end of the century (48% and 70%, respectively). This is no longer the case in pathways that return warming to 1.5°C (>50%) after a high overshoot (typically >0.1°C). All of these pathways deploy net negative emissions on the order of 360 (60–680) GtCO2 (median and 5–95th percentile) and 87% achieve net negative GHGs emissions in AR6 GWP-100 before the end of the century. Hence, global net negative CO2 emissions, and net zero or net negative GHG emissions, are only needed to decline, not to stabilise global warming. The deployment of carbon dioxide removal (CDR) is distinct from the deployment of net negative CO2 emissions, because it is also used to neutralise residual CO2 emissions to achieve and sustain net zero CO2 emissions. CDR deployment can be considerable in pathways without net negative emissions and all pathways limiting warming to 1.5°C use it to some extent.

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Not all regions and sectors must reach net zero CO2 or GHG emissions individually to achieve global net zero CO2 or GHG emissions, respectively; instead, positive emissions in one sector or region can be compensated by net negative emissions from another sector or region. The time each sector or region reaches net zero CO2 or GHG emissions depends on the mitigation options available, the cost of those options, and the policies implemented (including any consideration of equity or fairness). Most modelled pathways that likely limit warming to 2°C (>67%) above pre-industrial levels and below use land-based CO2 removal such as afforestation/reforestation and BECCS to achieve net zero CO2 and net zero GHG emissions even while some CO2 and non-CO2 emissions continue to occur. Pathways with more demand-side interventions that limit the amount of energy we use, or where the diet that we consume is changed, can achieve net zero CO2, or net zero GHG emissions with less carbon dioxide removal (CDR). All available studies require at least some kind of carbon dioxide removal to reach net zero; that is, there are no studies where absolute zero GHG or even CO2 emissions are reached by deep emissions reductions alone.

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Bistline, J.E.T. and G.J. Blanford, 2021: Impact of carbon dioxide removal technologies on deep decarbonization of the electric power sector. Nat. Commun. , 12(1) , 3732, doi:10.1038/s41467-021-23554-6.

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Fajardy, M. and N. Mac Dowell, 2020: Recognizing the Value of Collaboration in Delivering Carbon Dioxide Removal. One Earth, 3(2) , 214–225, doi.org/10.1016/j.oneear.2020.07.014.

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Fuss, S. et al., 2020: Moving toward Net-Zero Emissions Requires New Alliances for Carbon Dioxide Removal. One Earth, 3(2) , 145–149, doi:10.1016/j.oneear.2020.08.002.

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Fyson, C.L., S. Baur, M. Gidden, and C.F. Schleussner, 2020: Fair-share carbon dioxide removal increases major emitter responsibility. Nat. Clim. Change, 10(9) , 836–841, doi:10.1038/s41558-020-0857-2.

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Giannousakis, A. et al., 2021: How uncertainty in technology costs and carbon dioxide removal availability affect climate mitigation pathways. Energy, 216, 119253, doi.org/10.1016/j.energy.2020.119253.

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Grant, N., A. Hawkes, S. Mittal, and A. Gambhir, 2021: The policy implications of an uncertain carbon dioxide removal potential. Joule, 5, doi:10.1016/j.joule.2021.09.004.

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Hofmann, M., S. Mathesius, E. Kriegler, D.P. va. van Vuuren, and H.J. Schellnhuber, 2019: Strong time dependence of ocean acidification mitigation by atmospheric carbon dioxide removal. Nat. Commun. , 10(1) , 5592, doi:10.1038/s41467-019-13586-4.

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Holz, C., L.S. Siegel, E. Johnston, A.P. Jones, and J. Sterman, 2018: Ratcheting ambition to limit warming to 1.5°C trade-offs between emission reductions and carbon dioxide removal. Environ. Res. Lett. , 13(6) , doi:10.1088/1748-9326/aac0c1.

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Honegger, M., A. Michaelowa, and J. Roy, 2021: Potential implications of carbon dioxide removal for the sustainable development goals. Clim. Policy, 21(5) , 678–698, doi:10.1080/14693062.2020.1843388.

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Keller, D.P. et al., 2018a: The Carbon Dioxide Removal Model Intercomparison Project (CDRMIP): Rationale and experimental protocol for CMIP6. Geosci. Model Dev. , 11(3) , 1133–1160, doi:10.5194/gmd-11-1133-2018.

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Keller, D.P. et al., 2018b: The Effects of Carbon Dioxide Removal on the Carbon Cycle. Curr. Clim. Change Reports, 4(3) , 250–265, doi:10.1007/s40641-018-0104-3.

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Kriegler, E., O. Edenhofer, L. Reuster, G. Luderer, and D. Klein, 2013b: Is atmospheric carbon dioxide removal a game changer for climate change mitigation?Clim. Change, 118(1) , 45–57, doi:10.1007/s10584-012-0681-4.

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Rickels, W., F. Reith, D. Keller, A. Oschlies, and M.F. Quaas, 2018: Integrated Assessment of Carbon Dioxide Removal. Earth’s Future, 6(3) , 565–582, doi:10.1002/2017EF000724.

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Strefler, J. et al., 2021a: Carbon dioxide removal technologies are not born equal. Environ. Res. Lett. , 16(7) , 74021, doi:10.1088/1748-9326/ac0a11.

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(Section 5.6 assesses the impacts of CDR and solar radiation modification for the purpose of climate change mitigation on the global carbon cycle, building from the assessment in the IPCC Special Report on Climate Change and Land (SRCCL). It includes an overview of the major CDR options and potential collateral biogeochemical effects beyond the intended climate change mitigation strategies. The potential capacity to deliver atmospheric reductions and the socio-economic feasibility of such options are assessed in detail in AR6 working group III (WGIII).

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This section assesses the possible consequences of solar radiation modification (SRM) on the biosphere and global biogeochemical cycles. The SRM options and the physical climate response to SRM is assessed in detail in Section 4.6.3 and Table 4.7. Section 6.3.6 assesses the potential effective radiative forcing of aerosol-based SRM options and Section 8.6.3 assesses the abrupt water cycle changes in response to initiation or termination of SRM. Most literature on the biogeochemical responses to SRM focuses on stratospheric aerosol injection (SAI), and only a few studies have investigated the biogeochemical responses to marine cloud brightening (MCB) and cirrus cloud thinning (CCT). At the time of AR5, there were only a few studies on the biogeochemical responses to SRM. The main assessment of AR5 (Ciais et al., 2013) was that SRM will not interfere with the direct biogeochemical effects of increased CO2, such as ocean acidification and CO2 fertilization, but could affect the carbon cycle through climate–carbon feedbacks. Overall, AR5 concluded that the level of confidence on the effects of SRM on carbon and other biogeochemical cycles is very low (Ciais et al., 2013). Since AR5, more modelling work has been conducted to examine various aspects of the global biogeochemical cycle responses to SRM.

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Relative to a high-greenhouse gas (GHG) world without solar radiation modification (SRM), SRM would affect the carbon cycles through changes in sunlight, climate (e.g., temperature, precipitation, soil moisture, ocean circulation), and atmospheric chemistry (e.g., ozone; Section 4.6.3.3; Cao, 2018). Net SRM effects on the carbon cycle, relative to a world without SRM, depend on the change of individual factors, and interactions among them.

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Plazzotta, M., R. Séférian, and H. Douville, 2019: Impact of solar radiation modification on allowable CO2 emissions: what can we learn from multimodel simulations?Earth’s Future, 7(6), 664–676, doi: 10.1029/2019ef001165.

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Climate change can be also offset by solar radiation modification (SRM) measures that modify the Earth’s radiation budget to reduce global warming (see Glossary). CDR and SRM approaches have been together referred to as ‘geoengineering’ or ‘climate engineering’ in the literature (The Royal Society, 2009; NRC, 2015a, b; Schäfer et al., 2015). However, following SR1.5 (de Coninck et al., 2018), these terms are inconsistently used in the literature, so that CDR and SRM are explicitly differentiated here. SRM contrasts with climate change mitigation because it introduces a ‘mask’ to the climate change problem by altering the Earth’s radiation budget, rather than attempting to address the root cause of the problem, which is the increase in GHGs in the atmosphere.

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Plazzotta, M., R. Séférian, and H. Douville, 2019: Impact of Solar Radiation Modification on Allowable CO2 Emissions: What Can We Learn From Multimodel Simulations?Earth’s Future, 7(6), 664–676, doi: 10.1029/2019ef001165.

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In this Report, abrupt change is defined as a regional-to-global scale change in the climate system that occurs faster than the typical rate of changes in its history, implying non-linearity in the climate response (see Glossary). Often, abrupt change arises from positive feedbacks in the climate system that cause the current state to become unstable, and cross a ‘tipping point’ (Lenton et al., 2008); that is, a rapid shift from one climate state to another. The water cycle has several attributes with potential to produce abrupt change. Non-linear interactions between the ocean, atmosphere, and land surface can result in rapid shifts between wet and dry states (Sections 8.6.1 and 8.6.2). Cessation of solar radiation modification could also result in abrupt changes in the water cycle (Section 8.6.3). This section reviews these types of abrupt shifts and assesses the likelihood that they will occur by 2100.

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Solar radiation modification (SRM; Sections 4.6.3.3 and 8.6.3) has the potential to exert a significant ERF on the climate, mainly by affecting the SW component of the radiation budget (e.g., Caldeira et al., 2013; NRC, 2015; Lawrence et al., 2018). The possible ways and the extent to which the most commonly discussed options may affect radiative forcing is addressed in this section. Side effects of SRM on stratospheric ozone and changes in atmospheric transport due to radiative heating of the lower stratosphere are discussed in Section 4.6.3.3.

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Honegger, M., A. Michaelowa and J. Pan, 2021: Potential Implications of Solar Radiation Modification for Achievement of the Sustainable Development Goals. 21 pp.

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Cross-Working Group Box SRM | Solar Radiation Modification 2473

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In the context of mitigation pathways, only a few studies have examined solar radiation modification (SRM), typically focusing on Stratospheric Aerosol Injection (Arinoa et al. 2016; Emmerling and Tavoni 2018a,b; Heutel et al. 2018; Helwegen et al. 2019; Rickels et al. 2020; Belaia et al. 2021). These studies find that substantial mitigation is required to limit warming to a given level, even if SRM is available (Moreno-Cruz and Smulders 2017; Emmerling and Tavoni 2018b; Belaia et al. 2021). SRM may reduce some climate impacts, reduce peak temperatures, lower mitigation costs, and extend the time available to achieve mitigation; however, SRM does not address ocean acidification and may involve risks to crop yields, economies, human health, or ecosystems (AR6 WGII Chapter 16; AR6 WGI TS and Chapter 5; SR1.5 SPM; and Cross-Working Group Box 4 in Chapter 14 of this report). There are also significant uncertainties surrounding SRM, including uncertainties on the costs and risks, which can substantially alter the amount of SRM used in modelled pathways (Tavoni et al. 2017; Heutel et al. 2018; IPCC 2018; Helwegen et al. 2019; NASEM 2021). Furthermore, the degree of international cooperation can influence the amount of SRM deployed in scenarios, with uncoordinated action resulting in larger SRM deployment and consequently larger risks/impacts from SRM (Emmerling and Tavoni 2018a). Bridging research and governance involves consideration of the full range of societal choices and ramifications (Sugiyama et al. 2018). More information on SRM, including the caveats, risks, uncertainties, and governance issues is found in AR6 WGI Chapter 4; AR6 WGIII Chapter 14; and Cross-Working Group Box 4 in Chapter 14 of this report.

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A broad class of more speculative technologies propose to counteract effects of climate change by removing CO2 from the atmosphere (CDR), or by directly modifying the Earth’s energy balance at a large scale (solar radiation modification or SRM). CDR technologies include ocean iron fertilisation, enhanced weathering and ocean alkalinisation (Council 2015a), along with direct air carbon capture and storage (DACCS). They could potentially draw down atmospheric CO2 much faster than the Earth’s natural carbon cycle, and reduce reliance on biomass-based removal (Köberle 2019; Realmonte et al. 2019), but some present novel risks to the environment and DACCS is currently more expensive than most other forms of mitigation (Fuss et al. 2018) (Cross-Chapter Box 8 in Chapter 12). Solar radiation modification (SRM) could potentially cool the planet rapidly at low estimated direct costs by reflecting incoming sunlight (Council 2015b), but entails uncertain side effects and thorny international equity and governance challenges (Netra et al. 2018; Florin et al. 2020; National Academies of Sciences 2021) (Chapter 14). Understanding the climate response to SRM remains subject to large uncertainties (AR6 WGI). Some literature uses the term ‘geoengineering’ for both CDR or SRM when applied at a planetary scale (Shepherd 2009; GESAMP 2019). In this report, CDR and SRM are discussed separately, reflecting their very different geophysical characteristics.

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Four chapters then review thematic issues in implementation and governance of mitigation. Chapter 13 explores national and sub-national policies and institutions, bringing together lessons of policies examined in the sectoral chapters, as well as insights from service and demand-side perspectives (Chapter 5), along with governance approaches and capacity-building, and the role and relationships of sub-national actors. Chapter 14 then considers the roles and status of international cooperation, including the UNFCCC agreements and international institutions, sectoral agreements and multiple forms of international partnerships, and the ethics and governance challenges of solar radiation modification. Chapter 15 explores investment and finance, including current trends, the investment needs for deep decarbonisation, and the complementary roles of public and private finance. This includes climate-related investment opportunities and risks (e.g., ‘stranded assets’), linkages between finance and investments in adaptation and mitigation; and the impact of COVID-19. A new chapter on innovation (Chapter 16) looks at technology development, accelerated deployment and global diffusion as systemic issues that hold potential for transformative changes, and the challenges of managing such changes at multiple levels including the role of international cooperation.

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International cooperation will need to be strengthened in several key respects in order to support mitigation action consistent with limiting temperature rise to well below 2°C in the context of sustainable development and equity (high confidence). Many developing countries’ NDCs have components or additional actions that are conditional on receiving assistance with respect to finance, technology development and transfer, and capacity building, greater than what has been provided to date ( high confidence). Sectoral and sub-global cooperation is providing critical support, and yet there is room for further progress. In some cases, notably with respect to aviation and shipping, sectoral agreements have adopted climate mitigation goals that fall far short of what would be required to achieve the temperature goal of the Paris Agreement ( high confidence). Moreover, there are cases where international cooperation may be hindering mitigation efforts, namely evidence that trade and investment agreements, as well as agreements within the energy sector, impede national mitigation efforts (medium confidence). International cooperation is emerging but so far fails to fully address transboundary issues associated with Solar Radiation Modification and CO2 removal ( high confidence). {14.2, 14.3, 14.4, 14.5, 14.6}

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Honegger, M., A. Michaelowa, and J. Pan, 2021a: Potential implications of solar radiation modification for achievement of the Sustainable Development Goals. Mitig. Adapt. Strateg. Glob. Change, 26(5) , 1–20, doi:10.1007/s11027-021-09958-1.

solar radiation modificationresources/ipcc/wg3/Chapter03/html_with_ids.html#3.4.1.2_p14

In the context of mitigation pathways, only a few studies have examined solar radiation modification (SRM), typically focusing on Stratospheric Aerosol Injection (Arinoa et al. 2016; Emmerling and Tavoni 2018a,b; Heutel et al. 2018; Helwegen et al. 2019; Rickels et al. 2020; Belaia et al. 2021). These studies find that substantial mitigation is required to limit warming to a given level, even if SRM is available (Moreno-Cruz and Smulders 2017; Emmerling and Tavoni 2018b; Belaia et al. 2021). SRM may reduce some climate impacts, reduce peak temperatures, lower mitigation costs, and extend the time available to achieve mitigation; however, SRM does not address ocean acidification and may involve risks to crop yields, economies, human health, or ecosystems (AR6 WGII Chapter 16; AR6 WGI TS and Chapter 5; SR1.5 SPM; and Cross-Working Group Box 4 in Chapter 14 of this report). There are also significant uncertainties surrounding SRM, including uncertainties on the costs and risks, which can substantially alter the amount of SRM used in modelled pathways (Tavoni et al. 2017; Heutel et al. 2018; IPCC 2018; Helwegen et al. 2019; NASEM 2021). Furthermore, the degree of international cooperation can influence the amount of SRM deployed in scenarios, with uncoordinated action resulting in larger SRM deployment and consequently larger risks/impacts from SRM (Emmerling and Tavoni 2018a). Bridging research and governance involves consideration of the full range of societal choices and ramifications (Sugiyama et al. 2018). More information on SRM, including the caveats, risks, uncertainties, and governance issues is found in AR6 WGI Chapter 4; AR6 WGIII Chapter 14; and Cross-Working Group Box 4 in Chapter 14 of this report.

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Zhao, Y. et al., 2019: Inter-model comparison of global hydroxyl radical (OH) distributions and their impact on atmospheric methane over the 2000–2016 period. Atmospheric Chemistry and Physics, 19(21), 13701–13723, doi: 10.5194/acp-19-13701-2019.

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Fuchs, H. et al., 2013: Experimental evidence for efficient hydroxyl radical regeneration in isoprene oxidation. Nature Geoscience, 6(12), 1023–1026, doi: 10.1038/ngeo1964.

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Peeters, J., J.F. Müller, T. Stavrakou, and V.S. Nguyen, 2014: Hydroxyl radical recycling in isoprene oxidation driven by hydrogen bonding and hydrogen tunneling: The upgraded LIM1 mechanism. Journal of Physical Chemistry A, 118(38), 8625–8643, doi: 10.1021/jp5033146.

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Zhao, Y. et al., 2019: Inter-model comparison of global hydroxyl radical (OH) distributions and their impact on atmospheric methane over the 2000–2016 period. Atmospheric Chemistry and Physics, 19(21), 13701–13723, doi: 10.5194/acp-19-13701-2019.

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Zhao, Y. et al., 2020a: Influences of hydroxyl radicals (OH) on top-down estimates of the global and regional methane budgets. Atmospheric Chemistry and Physics, 20(15), 9525–9546, doi: 10.5194/acp-20-9525-2020.

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Bindoff, N.L. et al., 2019: Changing Ocean, Marine Ecosystems, and Dependent Communities. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate[Pörtner, H.-O., D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama, and N.M. Weyer (eds.)]. In Press, pp. 447–588, www.ipcc.ch/srocc/chapter/chapter-5.

special report on the ocean and cryosphereresources/ipcc/cleaned_content/wg1/Chapter05/html_with_ids.html#references_p512

IPCC, 2019b: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate [Pörtner, H.-O., D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama, and N.M. Weyer (eds.)]. In Press, 755 pp., www.ipcc.ch/report/srocc.

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IPCC, 2019c: Summary for Policymakers. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate[Pörtner, H.-O., D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, M. Nicolai, A. Okem, J. Petzold, B. Rama, and N. Weyer (eds.)]. In Press, pp. 3–35, www.ipcc.ch/srocc/chapter/summary-for-policymakers.

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Meredith, M. et al., 2019: Polar Regions. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate[Pörtner, H.-O., D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama, and N.M. Weyer (eds.)]. In Press, pp. 203–320, www.ipcc.ch/srocc/chapt er/chapter-3-2.

special report on the ocean and cryosphereresources/ipcc/cleaned_content/wg1/Chapter02/html_with_ids.html#references_p107

Bindoff, N.L. et al., 2019: Changing Ocean, Marine Ecosystems, and Dependent Communities. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate[Pörtner, H.-O., D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, and A. Okem (eds.)]. In Press, pp. 447–587, www.ipcc.ch/srocc/chapter/chapter-5/.

special report on the ocean and cryosphereresources/ipcc/cleaned_content/wg1/Chapter02/html_with_ids.html#references_p245

Collins, M., M. Sutherland, L. Bower, and S.-M. Cheong, 2019: Extremes, Abrupt Changes and Managing Risks. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate[Pörtner, H.-O., D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, and A. Okem (eds.)]. In Press, pp. 589–656, www.ipcc.ch/srocc/chapter/chapter-6/.

special report on the ocean and cryosphereresources/ipcc/cleaned_content/wg1/Chapter02/html_with_ids.html#references_p616

IPCC, 2019: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate [Pörtner, H.-O., D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, and A. Okem (eds.)]. In Press, 755 pp., www.ipcc.ch/srocc.

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Meredith, M. et al., 2019: Polar Regions. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate[Pörtner, H.-O., D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama, and N.M. Weyer (eds.)]. In Press, pp. 203–320, www.ipcc.ch/srocc/chapter/chapter-3-2/.

ipcc special report on climate change and landresources/ipcc/cleaned_content/wg1/Chapter03/html_with_ids.html#3.1_p2

The evidence of human influence on the climate system has strengthened progressively over the course of the previous five IPCC assessments, from the Second Assessment Report that concluded ‘the balance of evidence suggests a discernible human influence on climate’ through to the Fifth Assessment Report (AR5) which concluded that ‘it is extremely likely that human influence caused more than half of the observed increase in global mean surface temperature (GMST) from 1951 to 2010’ (see also Sections 1.3.4 and 3.3.1.1). The AR5 concluded that climate models had been developed and improved since the Fourth Assessment Report (AR4) and were able to reproduce many features of observed climate. Nonetheless, several systematic biases were identified (Flato et al., 2013). This chapter additionally builds on the assessment of attribution of global temperatures contained in the IPCC Special Report on Global Warming of 1.5°C (SR1.5; IPCC, 2018), assessments of attribution of changes in the ocean and cryosphere in the IPCC Special Report on the Ocean and Cryosphere in a Changing Climate (SROCC; IPCC, 2019b), and assessments of attribution of changes in the terrestrial carbon cycle in the IPCC Special Report on Climate Change and Land (SRCCL, IPCC, 2019a).

special report on the ocean and cryosphereresources/ipcc/cleaned_content/wg1/Chapter03/html_with_ids.html#references_p68

Bindoff, N.L. et al., 2019: Changing Ocean, Marine Ecosystems, and Dependent Communities. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate[Pörtner, H.-O., D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama, and N.M. Weyer (eds.)]. In Press, pp. 447–588, www.ipcc.ch/srocc/chapter/chapter-5.

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Hock, R. et al., 2019b: High Mountain Areas. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate[Pörtner, H.-O., D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama, and N.M. Weyer (eds.)]. In Press, pp. 131–202, www.ipcc.ch/srocc/chapter/chapter-2.

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IPCC, 2019b: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate [Pörtner, H.-O., D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama, and N.M. Weyer (eds.)]. In Press, 755 pp., www.ipcc.ch/report/srocc.

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Meredith, M. et al., 2019: Polar Regions. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate[Pörtner, H.-O., D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama, and N.M. Weyer (eds.)]. In Press, pp. 203–320, www.ipcc.ch/srocc/chapter/chapter-3-2.

special report on the ocean and cryosphereresources/ipcc/cleaned_content/wg1/Chapter04/html_with_ids.html#references_p372

IPCC, 2019: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate [Pörtner, H.-O., D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegria, M. Nicolai, A. Okem, J. Petzold, B. Rama, and N.M. Weyer (eds.)]. In Press, 755 pp., www.ipcc.ch/report/srocc.

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In this box, the possible influences of the Arctic warming on the lower latitudes are assessed. This linkage was also the topic of Box 3.2 of the Special Report on the Ocean and Cryosphere in a Changing Climate (SROCC; IPCC, 2019b). It is a topic that has been strongly debated (Ogawa et al., 2018; K. Wang et al., 2018). Separate hypotheses have emerged for winter and summer that describe possible mechanisms of how the Arctic can influence the weather and climate at lower latitudes. They involve changes in the polar vortex, storm tracks, jet stream, planetary waves, stratosphere-troposphere coupling, and eddy-mean flow interactions, thereby affecting the mid-latitude atmospheric circulation, and the frequency, intensity, duration, seasonality and spatial extent of extremes and climatic impact-drivers like cold spells, heatwaves, and floods (Cross-Chapter Box 10.1, Figure 1). However, we note that a decrease in the intensity of cold extremes has been observed in the Northern Hemisphere mid-latitudes in winter since 1950 (Section 11.3.2; van Oldenborgh et al., 2019). Since SROCC, new literature has appeared, and the mechanisms and their criticisms are assessed here as an update and extension to the SROCC box.

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Hock, R. et al., 2019: High Mountain Areas. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate[Pörtner, H.-O., D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegria, M. Nicolai, A. Okem, J. Petzold, B. Rama, and N.M. Weyer (eds.)]. In Press, pp. 131–202, www.ipcc.ch/srocc/chapter/chapter-2.

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IPCC, 2019b: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate [Pörtner, H.-O., D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama, and N.M. Weyer (eds.)]. In Press, 755 pp., www.ipcc.ch/srocc.

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Collins, M. et al., 2019: Extremes, Abrupt Changes and Managing Risks. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate[Pörtner, H.-O., D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama, and N.M. Weyer (eds.)]. In Press, pp. 589–656, www.ipcc.ch/srocc/chapter/chapter-6.

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IPCC, 2019b: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate [Pörtner, H.-O., D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama, and N.M. Weyer (eds.)]. In Press, 755 pp., www.ipcc.ch/srocc.

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Hock, R. et al., 2019a: High Mountain Areas. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate[Pörtner, H.-O., D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama, and N.M. Weyer (eds.)]. In Press, pp. 131–202, www.ipcc.ch/srocc/chapter/chapter-2.

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Meredith, M. et al., 2019: Polar Regions. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate[Pörtner, H.-O., D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama, and N.M. Weyer (eds.)]. In Press, pp. 203–320, www.ipcc.ch/srocc/chapter/chapter-3-2.

special report on the ocean and cryosphereresources/ipcc/cleaned_content/wg1/Chapter01/html_with_ids.html#box-1.2_p1

The Sixth Assessment Cycle started with three Special Reports. The Special Report on Global Warming of 1.5°C (SR1.5, IPCC, 2018), invited by the Parties to the UNFCCC in the context of the Paris Agreement, assessed current knowledge on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas (GHG) emissions pathways. The Special Report on Climate Change and Land (SRCCL, IPCC, 2019a) addressed GHG fluxes in land-based ecosystems, land use and sustainable land management in relation to climate change adaptation and mitigation, desertification, land degradation and food security. The Special Report on the Ocean and Cryosphere in a Changing Climate (SROCC, IPCC, 2019b) assessed new literature on observed and projected changes of the ocean and the cryosphere, and their associated impacts, risks and responses.

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Abram, N. et al., 2019: Framing and Context of the Report. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate[Pörtner, H.-O., D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama, and N.M. Weyer (eds.)]. In Press, pp. 73–129, www.ipcc.ch/srocc/chapter/chapter-1-framing-and-context -of-the-report .

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IPCC, 2019b: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate [Pörtner, H.-O., D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, and A. Okem (eds.)]. In Press, 755 pp., www.ipcc.ch/srocc.

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Finally, the IPCC Special Report on the Ocean and Cryosphere in a Changing Climate (SROCC; IPCC, 2019b) discussed the effects of BC deposition on snow and glaciers, concluding that there is high confidence that darkening of snow through the deposition of BC and other light-absorbing particles enhances snowmelt in the Arctic (Meredith et al., 2019), but that there is limited evidence and low agreement that long-term changes in glacier mass of high mountain areas are linked to light-absorbing particles (Hock et al., 2019).

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Hock, R. et al., 2019: High Mountain Areas. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate[Pörtner, H.-O., D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama, and N.M. Weyer (eds.)]. In Press, pp. 131–202, www.ipcc.ch/srocc/chapter/chapter-2.

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IPCC, 2019b: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate [Pörtner, H.-O., D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama, and N.M. Weyer (eds.)]. In Press, 755 pp., www.ipcc.ch/report/srocc/.

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Meredith, M. et al., 2019: Polar Regions. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate[Pörtner, H.-O., D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama, and N.M. Weyer (eds.)]. In Press, pp. 203–320, www.ipcc.ch/srocc/chapter/chapter-3-2.

special report on the ocean and cryosphereresources/ipcc/cleaned_content/wg1/Chapter07/html_with_ids.html#7.1_p3

This chapter principally builds on the IPCC Fifth Assessment Report (AR5; Boucher, 2012; Church et al., 2013; M. Collins et al., 2013; Flato et al., 2013; Hartmann et al., 2013; Myhre et al., 2013b; Rhein et al., 2013). It also builds on the subsequent IPCC Special Report on Global Warming of 1.5°C (SR1.5; IPCC, 2018), the Special Report on the Ocean and Cryosphere in a Changing Climate (SROCC; IPCC, 2019a) and the Special Report on climate change, desertification, land degradation, sustainable land management, food security, and greenhouse gas fluxes in terrestrial ecosystems (SRCCL; IPCC, 2019b), as well as community-led assessments (e.g., Bellouin et al. (2020) covering aerosol radiative forcing and Sherwood et al. (2020) covering equilibrium climate sensitivity).

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Hock, R. et al., 2019: High Mountain Areas. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate[Pörtner, H.-O., D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama, and N.M. Weyer (eds.)]. In Press, pp. 131–202, www.ipcc.ch/srocc/chapter/chapter-2.

special report on the ocean and cryosphereresources/ipcc/cleaned_content/wg1/Chapter07/html_with_ids.html#references_p393

IPCC, 2019a: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate [Pörtner, H.-O., D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama, and N.M. Weyer (eds.)]. In Press, 755 pp, www.ipcc.ch/srocc.

special report on the ocean and cryosphereresources/ipcc/cleaned_content/wg1/Chapter07/html_with_ids.html#references_p589

Meredith, M. et al., 2019: Polar Regions. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate[Pörtner, H.-O., D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama, and N.M. Weyer (eds.)]. In Press, pp. 203–320, www.ipcc.ch/srocc/chapter/chapter-3-2.

special report on the ocean and cryosphereresources/ipcc/cleaned_content/wg1/Chapter09/html_with_ids.html#box-9.2_p1

Marine heatwaves (MHW) are periods of extreme high sea temperature relative to the long-term mean seasonal cycle (Hobday et al., 2016). Studies since the Special Report on the Ocean and Cryosphere in a Changing Climate (SROCC; Collins et al., 2019) confirm the assessment that MHW can lead to severe and persistent impacts on marine ecosystems – from mass mortality of benthic communities, including coral bleaching, changes in phytoplankton blooms, shifts in species composition and geographical distribution, and toxic algal blooms, to decline in fisheries catch and mariculture (Smale et al., 2019; Cheung and Frölicher, 2020; Hayashida et al., 2020; Piatt et al., 2020). Unlike synoptic atmospheric heatwaves Section 11.3), MHWs can extend for millions of square kilometres, persist for weeks to months, and occur at subsurface (Bond et al., 2015; Schaeffer and Roughan, 2017; Perkins-Kirkpatrick et al., 2019; Laufkötter et al., 2020).

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The IPCC’s Fifth Assessment Report (AR5; Vaughan et al., 2013) assessed glacier changes from studies based on the regions defined in the Randolph Glacier Inventory (RGI; RGI version 2.0): a satellite observation-based, global inventory of glacier outlines for the year 2000. Following Special Report on the Ocean and Cryosphere in a Changing Climate (SROCC; Hock et al., 2019b; Meredith et al., 2019), we report on studies based on RGI version 6.0 (RGI Consortium, 2017). Increased volume of satellite observations and the inclusion of detailed regional glacier inventories has resulted in an improved inventory (RGI Consortium, 2017). A new consensus estimate for the ice thickness distribution of all glaciers in RGI 6.0 was obtained from an ensemble of five numerical models. However, only one out of five models covered all regions (Farinotti et al., 2019), and was, where possible, calibrated and validated with the worldwide Glacier Thickness Database (GlaThiDa 3.0: GlaThiDa Consortium, 2019; Welty et al., 2020). The updated inventory shows decreases in estimated glacier volume in the Arctic, High Mountain Asia and Southern Andes, partially compensated by increases in Antarctica. 15% of the total glacier volume is estimated to be below sea level and would not contribute to sea level rise if melted (Farinotti et al., 2019). Supplementary Material Table 9.SM.2 shows the inventory glacier area and mass for each region in the year 2000.

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Abram, N. et al., 2019: Framing and Context of the Report. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate[Pörtner, H.-O., D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama, and N.M. Weyer (eds.)]. In Press, pp. 73–129, www.ipcc.ch/srocc/chapter/chapter-1-framing-and-context-of-the-report .

special report on the ocean and cryosphereresources/ipcc/cleaned_content/wg1/Chapter09/html_with_ids.html#references_p106

Bindoff, N.L. et al., 2019: Changing Ocean, Marine Ecosystems, and Dependent Communities. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate[Pörtner, H.-O., D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, M. Nicolai, A. Okem, J. Petzold, B. Rama, and N. Weyer (eds.)]. In Press, pp. 447–588, www.ipcc.ch/srocc/chapter/chapter-5.

special report on the ocean and cryosphereresources/ipcc/cleaned_content/wg1/Chapter09/html_with_ids.html#references_p242

Collins, M. et al., 2019: Extremes, Abrupt Changes and Managing Risks. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate[Pörtner, H.-O., D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, M. Nicolai, A. Okem, J. Petzold, B. Rama, and N. Weyer (eds.)]. In Press, pp. 589-655, www.ipcc.ch/srocc/chapter/chapter-6.

special report on the ocean and cryosphereresources/ipcc/cleaned_content/wg1/Chapter09/html_with_ids.html#references_p533

Hock, R. et al., 2019b: High Mountain Areas. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate[Pörtner, H.-O., D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama, and N.M. Weyer (eds.)]. In Press, pp. 131–202, www.ipcc.ch/srocc/chapter/chapter-2.

special report on the ocean and cryosphereresources/ipcc/cleaned_content/wg1/Chapter09/html_with_ids.html#references_p862

Meredith, M. et al., 2019: Polar Regions. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate[Pörtner, H.-O., D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, M. Nicolai, A. Okem, J. Petzold, B. Rama, and N. Weyer (eds.)]. In Press, pp. 203–320, www.ipcc.ch/srocc/chapter/chapter-3-2.

special report on the ocean and cryosphereresources/ipcc/cleaned_content/wg1/Chapter09/html_with_ids.html#references_p973

Oppenheimer, M. et al., 2019: Sea Level Rise and Implications for Low Lying Islands, Coasts and Communities. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate[Pörtner, H.-O., D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, M. Nicolai, A. Okem, J. Petzold, B. Rama, and N. Weyer (eds.)]. In Press, pp. 321–445, www.ipcc.ch/srocc/chapter/chapter-4-sea-level-rise-and-implications-for-low-lying-islands-coasts-and-communities/.

special report on the ocean and cryosphereresources/ipcc/cleaned_content/wg1/Chapter12/html_with_ids.html#12.3.4_p1

Cryospheric changes are a focus of (Chapter 9 and were central to the recent IPCC Special Report on the Ocean and Cryosphere in a Changing Climate (SROCC; IPCC, 2019b). Here we focus on the ways that scientists use snow and ice CIDs to understand current and future societal impacts and risks.

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Bindoff, N. et al., 2019: Changing Ocean, Marine Ecosystems, and Dependent Communities. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate[Pörtner, H.-O., D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama, and N.M. Weyer (eds.)]. In Press, pp. 447–588, www.ipcc.ch/srocc/chapter/chapter-5.

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Collins, M. et al., 2019: Extremes, Abrupt Changes and Managing Risks. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate[Pörtner, H.-O., D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama, and N.M. Weyer (eds.)]. In Press, pp. 3–63, www.ipcc.ch/srocc/chapter/chapter-6.

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Hock, R. et al., 2019: High Mountain Areas. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate[Pörtner, H.-O., D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama, and N.M. Weyer (eds.)]. In Press, pp. 131–202, www.ipcc.ch/srocc/chapter/chapter-2.

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IPCC, 2019b: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate [Pörtner, H.-O., D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama, and N.M. Weyer (eds.)]. In Press, 755 pp., www.ipcc.ch/report/srocc.

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Magnan, A.K. et al., 2019: Cross-Chapter Box 9: Integrative Cross-Chapter Box on Low-Lying Islands and Coasts. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate[Pörtner, H.-O., D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama, and N.M. Weyer (eds.)]. In Press, pp. 657–674, www.ipcc.ch/srocc/chapter/cross-chapter-box-9-integrative-cross-chapter-box-on-low-lying-islands-and-coasts.

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Meredith, M. et al., 2019: Polar Regions. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate[Pörtner, H.-O., D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama, and N.M. Weyer (eds.)]. In Press, pp. 203–320, www.ipcc.ch/srocc/chapter/chapter-3-2.

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Oppenheimer, M. et al., 2019: Sea Level Rise and Implications for Low Lying Islands, Coasts and Communities. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate[Pörtner, H.-O., D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama, and N.M. Weyer (eds.)]. In Press, pp. 321–446, www.ipcc.ch/srocc/chapter/chapter-4-sea-level-rise-and-implications-for-low-lying-islands-coasts-and-communities.

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The IPCC Special Report on the Ocean and Cryosphere in a Changing Climate (SROCC) identified climate change impacts of warming, deoxygenation and acidification of the ocean and reductions in snow, sea ice and glaciers as having major negative impacts on fisheries and crops watered from mountain runoff and agriculture. These impacts affect food provisioning of food and directly threaten livelihoods and food security of vulnerable coastal communities and glacier-fed river basins. Climate change impacts on fisheries will be particularly high in tropical regions, where reductions in catch are expected to be among the largest globally, leading to negative economic and social effects for fishing communities and with implications for the supply of fish and shellfish (high confidence). While specific impacts will depend on the level of global warming and mitigative action to improve fisheries and aquaculture management, some current management practices and extraction levels may not be viable in the future.

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Bindoff, N.L., W.W.L. Cheung, J.G. Kairo, J. Arístegui, V.A. Guinder, R. Hallberg, N. Hilmi, N. Jiao, M.S. Karim, L. Levin, S. O’Donoghue, S.R. Purca Cuicapusa, B. Rinkevich, T. Suga, A. Tagliabue, and P. Williamson, 2019: Changing Ocean, Marine Ecosystems, and Dependent Communities. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate[H.-O. Pörtner, D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama, N.M. Weyer (eds.)]. In press, pp. 447–587.

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IPCC, 2019c: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate[H.-O. Pörtner, D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama, N.M. Weyer (eds.)]. In press.

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Oppenheimer, M., B.C. Glavovic , J. Hinkel, R. van de Wal, A.K. Magnan, A. Abd-Elgawad, R. Cai, M. Cifuentes-Jara, R.M. DeConto, T. Ghosh, J. Hay, F. Isla, B. Marzeion, B. Meyssignac, and Z. Sebesvari, 2019: Sea Level Rise and Implications for Low-Lying Islands, Coasts and Communities. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate[Pörtner, H.-O., D. C. Roberts, V. VMasson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama and N. M. Weyer (eds.)], pp. 321–445.

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Bindoff, N. L. et al., 2019: Changing Ocean, Marine Ecosystems, and Dependent Communities. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate[Pörtner, H. O., D. C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama and N. M. Weyer (eds.)], In press, pp. 447–587.

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Collins, M. et al., 2019: Extremes, Abrupt Changes and Managing Risks. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate[Pörtner, H. O., D. C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama and N. M. Weyer (eds.)], In press.

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Hock, R. et al., 2019: High Mountain Areas. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate[Pörtner, H. O., D. C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama and N. M. Weyer (eds.)].

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IPCC, 2019b: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate[Pörtner, H.-O., D. C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. S. Poloczanska, K. Mintenbeck, M. Nicolai, A. Okem, J. Petzold, B. Rama and N. M. Weyer (eds.)]. In press.

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Abram, N., et al., 2019: Framing and Context of the Report. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate. [Pörtner, H.-O., D. C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. S. Poloczanska, K. Mintenbeck, M. Nicolai, A. Okem, J. Petzold, B. Rama and N. Weyer (eds.)](pp. In press).

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Bindoff, N.L., et al., 2019a: Changing Ocean, Marine Ecosystems, and Dependent Communities. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate. [Pörtner, H.-O., D. C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. S. Poloczanska, K. Mintenbeck, M. Nicolai, A. Okem, J. Petzold, B. Rama and N. Weyer (eds.)](In press).

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Bindoff, N.L., et al., 2019b: Changing Ocean, Marine Ecosystems, and Dependent Communities Supplementary Material. In: IPCC SpecialReport on the Ocean and Cryosphere in a Changing Climate. [Pörtner, H.-O., D. C.Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. S. Poloczanska, K. Mintenbeck, M. Nicolai, A. Okem, J. Petzold, B. Rama and N. Weyer (eds.)](In press).

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Collins, M., et al., 2019a: Extremes, Abrupt Changes and Managing Risks. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate. [Pörtner, H.-O., D. C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. S. Poloczanska, K. Mintenbeck, M. Nicolai, A. Okem, J. Petzold, B. Rama and N. Weyer (eds.)](In press).

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IPCC, 2019a: Annex 1: Glossary [Weyer, N. M. (ed.)]. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate. [Pörtner, H.-O., D. C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. S. Poloczanska, K. Mintenbeck, M. Nicolai, A. Okem, J. Petzold, B. Rama and N. Weyer (eds.)](In press).

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IPCC, 2019b: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate[Pörtner, H.-O., D. C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. S. Poloczanska, K. Mintenbeck, M. Nicolai, A. Okem, J. Petzold, B. Rama and N. Weyer (eds.)]. In press.

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IPCC, 2019c: Summary for Policymakers. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate. [Pörtner, H.-O., D. C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. S. Poloczanska, K. Mintenbeck, M. Nicolai, A. Okem, J. Petzold, B. Rama and N. Weyer (eds.)](In press).

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IPCC, 2019d: Technical Summary. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate. [Pörtner, H.-O., D. C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama and N. M. Weyer (eds.)](In press).

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Meredith, M.P., et al., 2019: Polar Regions. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate. [Pörtner, H.-O., D. C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. S. Poloczanska, K. Mintenbeck, M. Nicolai, A. Okem, J. Petzold, B. Rama and N. Weyer (eds.)](In press).

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Oppenheimer, M., et al., 2019: Sea Level Rise and Implications for Low Lying Islands, Coasts and Communities. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate. [Pörtner, H.-O., D. C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. S. Poloczanska, K. Mintenbeck, M. Nicolai, A. Okem, J. Petzold, B. Rama and N. Weyer (eds.)](In press).

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Prakash, A., et al., 2019: Cross-Chapter Box 3: Governance of the Ocean, Coasts and the Cryosphere under Climate Change. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate. [Pörtner, H.-O., D. C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama and N. M. Weyer (eds.)](In press).

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Abram, N., J.-P. Gattuso, A. Prakash, L. Cheng, M.P. Chidichimo, S. Crate, H. Enomoto, M. Garschagen, N. Gruber, S. Harper, E. Holland, R.M. Kudela, J. Rice, K. Steffen, and K. von Schuckmann, 2019: Framing and Context of the Report. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate[H.-O. Pörtner, D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama, N.M. Weyer (eds.)]. In press. Cambridge University Press, Cambridge

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Hock, R., G. Rasul, C. Adler, B. Cáceres, S. Gruber, Y. Hirabayashi, M. Jackson, A. Kääb, S. Kang, S. Kutuzov, Al. Milner, U. Molau, S. Morin, B. Orlove, and H. Steltzer, 2019b: High Mountain Areas. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate[H.-O. Pörtner, D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama, N.M. Weyer (eds.)]. IPCC, Cambridge University Press, Cambridge

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IPCC, 2019a: Summary for Policymakers. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate. [Pörtner, H.-O., D. C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama and N. M. Weyer (eds.)]. IPCC, Cambridge University Press, Cambridge.

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IPCC, 2019c: Technical Summary. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate. [Pörtner, H. O., D. C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama and N. M. Weyer (eds.)] IPCC, Cambridge University Press, Cambridg.

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Meredith, M., M. Sommerkorn, S. Cassotta, C. Derksen, A. Ekaykin, A. Hollowed, G. Kofinas, A. Mackintosh, J. Melbourne-Thomas, M.M.C. Muelbert, G. Ottersen, H. Pritchard, and E.A.G. Schuur, 2019: Polar Regions. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate[H.-O. Pörtner, D.C. Roberts, V. Masson- Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama, N.M. Weyer (eds.)]. Cambridge University Press, Cambridge, UK.

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Oppenheimer, M., B.C. Glavovic , J. Hinkel, R. van de Wal, A.K. Magnan, A. Abd-Elgawad, R. Cai, M. Cifuentes-Jara, R.M. DeConto, T. Ghosh, J. Hay, F. Isla, B. Marzeion, B. Meyssignac, and Z. Sebesvari, 2019: Sea Level Rise and Implications for Low-Lying Islands, Coasts and Communities. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate. [Pörtner, H. O., D. C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama and N. M. Weyer (eds.)]. Cambridege University Press, Cambridge, UK.

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While a generic understanding of the decision-making process has emerged from the literature, the chapter assesses how these components and their dimensions interact across a range of temporal (short, long term as defined in the IPCC Special Report on the Ocean and Cryosphere in a Changing Climate [SROCC]), scalar (household to global), institutional/governance (formal, informal, bottom-up, top-down) and magnitude (micro adaptation, small scale; macro adaptation, large scale) (Section 17.2). The IPCC SRCCL placed emphasis on acknowledging co-benefits and trade-offs to avoid barriers to implementation, with particular attention to land use decisions. It states that this coordination can be supported by building networks of decision makers across scales and sectors, including local stakeholders from vulnerable groups, and by adopting and implementing policies in a flexible and iterative manner (IPCC, 2019b).

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Abram, N., J.-P. Gattuso, A. Prakash, L. Cheng, M.P. Chidichimo, S. Crate, H. Enomoto, M. Garschagen, N. Gruber, S. Harper, E. Holland, R.M. Kudela, J. Rice, K. Steffen, and K. von Schuckmann, 2019: Framing and Context of the Report Supplementary Material. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate[H.- O. Pörtner, D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama, N.M. Weyer (eds.)]. In press.

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Collins M., M. Sutherland, L. Bouwer, S.-M. Cheong, T. Frölicher, H. Jacot Des Combes, M. Koll Roxy, I. Losada, K. McInnes, B. Ratter, E. Rivera-Arriaga, R.D. Susanto, D. Swingedouw, and L. Tibig, 2019: Extremes, Abrupt Changes and Managing Risk. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate[H.-O. Pörtner, D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama, N.M. Weyer (eds.)]. In press.

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Hock, R., G. Rasul, C. Adler, B. Cáceres, S. Gruber, Y. Hirabayashi, M. Jackson, A. Kääb, S. Kang, S. Kutuzov, Al. Milner, U. Molau, S. Morin, B. Orlove, and H. Steltzer, 2019: High Mountain Areas. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate[H.-O. Pörtner, D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama, N.M. Weyer (eds.)]. In press.

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IPCC, 2019c: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate[H.-O. Pörtner, D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama, N.M. Weyer (eds.)]. In press.

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IPCC, 2019d: Summary for Policymakers. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate[H.-O. Pörtner, D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama, N.M. Weyer (eds.)]. In press.

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Oppenheimer, M., B.C. Glavovic , J. Hinkel, R. van de Wal, A.K. Magnan, A. Abd-Elgawad, R. Cai, M. Cifuentes-Jara, R.M. DeConto, T. Ghosh, J. Hay, F. Isla, B. Marzeion, B. Meyssignac, and Z. Sebesvari, 2019: Sea Level Rise and Implications for Low-Lying Islands, Coasts and Communities. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate[H.-O. Pörtner, D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama, N.M. Weyer (eds.)]. In press.

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Bindoff, N.L., W.W.L. Cheung, J.G. Kairo, J. Arístegui, V.A. Guinder, R. Hallberg, N. Hilmi, N. Jiao, M.S. Karim, L. Levin, S. O’Donoghue, S.R. Purca Cuicapusa, B. Rinkevich, T. Suga, A. Tagliabue, and P. Williamson, 2019: Changing Ocean, Marine Ecosystems, and Dependent Communities. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate[H.-O. Pörtner, D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama, N.M. Weyer (eds.)]. In press.

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Hock, R., G. Rasul, C. Adler, B. Cáceres, S. Gruber, Y. Hirabayashi, M. Jackson, A. Kääb, S. Kang, S. Kutuzov, Al. Milner, U. Molau, S. Morin, B. Orlove, and H. Steltzer, 2019: High Mountain Areas. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate[H.-O. Pörtner, D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama, N.M. Weyer (eds.)]. In press.

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Oppenheimer, M., B.C. Glavovic , J. Hinkel, R. van de Wal, A.K. Magnan, A. Abd-Elgawad, R. Cai, M. Cifuentes-Jara, R.M. DeConto, T. Ghosh, J. Hay, F. Isla, B. Marzeion, B. Meyssignac and Z. Sebesvari, 2019: Sea Level Rise and Implications for Low-Lying Islands, Coasts and Communities. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate[H.-O. Pörtner, D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama, N.M. Weyer (eds.)]. In press.

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Bindoff, N.L., W.W.L. Cheung, J.G. Kairo, J. Arístegui, V.A. Guinder, R. Hallberg, N. Hilmi, N. Jiao, M.S. Karim, L. Levin, S. O’Donoghue, S.R. Purca Cuicapusa, B. Rinkevich, T. Suga, A. Tagliabue, and P. Williamson, 2019: Changing Ocean, Marine Ecosystems, and Dependent Communities. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate[Pörtner, H.-O., D. C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama and N. M. Weyer (eds.)], In press.

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IPCC, 2019a: Annex I: Glossary [Weyer, N.M. (ed.)]. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate[Pörtner, H.-O., D. C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama and N. M. Weyer (eds.)], In press.

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IPCC, 2019e: Special Report on the Ocean and Cryosphere in a Changing Climate[Pörtner, H. O., D. C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama and N. M. Weyer (eds.)]. Available at: https://www.ipcc.ch/srocc/home/ (accessed 2019/10/2).

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IPCC, 2019g: Technical Summary. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate[Pörtner, H.-O., D. C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama and N. M. Weyer (eds.)], In press.

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Oppenheimer, M., B.C. Glavovic , J. Hinkel, R. van de Wal, A.K. Magnan, A. Abd-Elgawad, R. Cai, M. Cifuentes-Jara, R.M. DeConto, T. Ghosh, J. Hay, F. Isla, B. Marzeion, B. Meyssignac, and Z. Sebesvari, 2019: Sea Level Rise and Implications for Low-Lying Islands, Coasts and Communities. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate[Pörtner, H.-O., D. C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama and N. M. Weyer (eds.)]. In press.

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IPCC, 2019b: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate[Pörtner, H. O. (ed.)]. Cambridge University Press,, Cambridge, UK and New York, NY, USA In press pp.

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Bindoff, N.L., W.W.L. Cheung, J.G. Kairo, J. Arístegui, V.A. Guinder, R. Hallberg, N. Hilmi, N. Jiao, M.S. Karim, L. Levin, S. O’Donoghue, S.R. Purca Cuicapusa, B. Rinkevich, T. Suga, A. Tagliabue, and P. Williamson, 2019: Changing Ocean, Marine Ecosystems, and Dependent Communities. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate[H.-O. Pörtner, D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama, N.M. Weyer (eds.)]. In press).

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Garschagen, M., et al., 2019: Key concepts of risk, adaptation, resilience and transformation. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate, pp. 87–90.

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IPCC, 2019b: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate[H.-O. Pörtner, D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama, N.M. Weyer (eds.)]. In press.

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IPCC, 2019c: Annex I: Glossary [Weyer, N.M. (ed.)]. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate[H.-O. Pörtner, D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama, N.M. Weyer (eds.)]. In Press.

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Magnan, A.K., M. Garschagen, J.-P. Gattuso, J.E. Hay, N. Hilmi, E. Holland, F. Isla, G. Kofinas, I.J. Losada, J. Petzold, B. Ratter, T.Schuur, T. Tabe, and R. van de Wal, 2019: Cross-Chapter Box 9: Integrative Cross-Chapter Box on Low-Lying Islands and Coasts. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate[H.-O. Pörtner, D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama, N.M. Weyer (eds.)]. In press.

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Meredith, M., M. Sommerkorn, S. Cassotta, C. Derksen, A. Ekaykin, A. Hollowed, G. Kofinas, A. Mackintosh, J. Melbourne-Thomas, M.M.C. Muelbert, G. Ottersen, H. Pritchard, and E.A.G. Schuur, 2019: Polar Regions. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate[H.-O. Pörtner, D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama, N.M. Weyer (eds.)]. In press).

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Oppenheimer, M., B.C. Glavovic , J. Hinkel, R. van de Wal, A.K. Magnan, A. Abd-Elgawad, R. Cai, M. Cifuentes-Jara, R.M. DeConto, T. Ghosh, J. Hay, F. Isla, B. Marzeion, B. Meyssignac, and Z. Sebesvari, 2019: Sea Level Rise and Implications for Low-Lying Islands, Coasts and Communities. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate[H.-O. Pörtner, D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama, N.M. Weyer (eds.)]. In press.

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Table 8.6, built from SR1.5°C (Roy et al., 2018), illustrates how ecological thresholds and socioeconomic determinants are linked to soft and hard adaptation limits and what the potential and magnitude of livelihoods risks will be in the future. For instance, in the SR1.5°C (IPCC, 2018b) and Special Report on the Ocean and Cryosphere in a Changing Climate (SROCC) (IPCC, 2019b), hard limits are expected with global warming beyond 1.5°C associated with the loss of coral reefs, that will lead to substantial loss of income and livelihoods for coastal communities (Roy et al., 2018; Mechler et al., 2019b; Oppenheimer et al., 2019). The loss of coral reefs around the remote islands of Boigu in Australia is affecting low-lying communities facing financial, institutional (Evans et al., 2016) and cultural place-based attachment adaptation limits (McNamara et al., 2017). Another hard limit to adaptation with implications for income, and culture- and place-based livelihoods is related to the sensitivity of fish to global temperature increase, with losses in fish reproduction expected to be 10% (SSP1–1.9) to about 60% (SSP5–8.5), potentially cascading into severe risks for fisheries livelihoods (Dahlke et al., 2020). In West African fisheries, the loss of coastal ecosystems and productivity are estimated to require 5–10% of countries’ GDP in adaptation costs (Zougmoré et al., 2016), incurring financial limits in poor countries to avoid socioeconomic risks. The SROCC (IPCC, 2019b) showed that scientific knowledge limitations can constrain management of coastlines, mainly in the context of lack of data, affecting most of the vulnerable and poor communities in the Global South (Perkins et al., 2015; Sutton-Grier et al., 2015; Wigand et al., 2017; Romañach et al., 2018). Hard and soft adaptation limits are challenging to define, given the rate and intensity of climate change hazards and the mitigation and adaptation options available, but also the level and rate of non-climatic stresses increasing vulnerabilities and undermining adaptive capacity of poorest members of society and sensitive ecosystems (medium evidence, high agreement ) (Klein et al., 2014; Roy et al., 2018).

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Bindoff, N. et al., 2019: Changing ocean, marine ecosystems, and dependent communities. In: IPCC special report on the ocean and cryosphere in a changing climate. Cambridge University Press.

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IPCC, 2019b: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate[Pörtner, H. O., D. C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama and N. M. Weyer (eds.)]. In press pp.

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Magnan, A. K. et al., 2019: Cross-Chapter Box 9: Integrative Cross-Chapter Box on Low-Lying Islands and Coasts. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate[Pörtner, H. O., D. C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama and N. M. Weyer (eds.)], pp. In press.

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Oppenheimer, M. et al., 2019: Sea Level Rise and Implications for Low-Lying Islands, Coasts and Communities. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate[Pörtner, H. O., D. C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama and N. M. Weyer (eds.)], pp. In press.

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In 2019, a Special Report on the Ocean and Cryosphere in a Changing Climate (SROCC) was published, motivated by the observation that many of the world’s people most exposed to risks caused by climate change live in the mountains or near the coast. Key findings include the following, directly quoted (IPCC, 2019c):

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IPCC, 2019b: Special Report on the Ocean and Cryosphere in a Changing Climate (SROCC).

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IPCC, 2019c: Summary for Policymakers. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate (In press). Cambridge University Press, Cambridge, UK, and New York, NY, US.

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IPCC, 2019b: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate[H.-O. Pörtner, D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama, N.M. Weyer (eds.)]. Cambridge University Press, Cambridge, UK and New York, NY, USA, 755 pp, https://doi.org/10.1017/9781009157964.

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All three post-AR5 IPCC Special Reports considered some of the research that is assessed here in greater detail. The 2018 report on 1.5°C (SR1.5) included a review of climate change and health literature published since AR5 and called for further efforts for protecting health and well-being of vulnerable people and regions (Ebi et al., 2018b) and highlighted links between climate change hazards, poverty, food security, migration and conflict. The 2019 Special Report on Climate Change and Land (SRCCL) (IPCC, 2019b) emphasised the impacts of climate change on food security; highlighted links between reduced resilience of dryland populations, land degradation, migration and conflict; and raised concerns about the impacts of climate extremes. The 2019 Special Report on the Ocean and Cryosphere in a Changing Climate (IPCC, 2019a) detailed how changes in the cryosphere and ocean systems have impacted people and ecosystem services, particularly food security, water resources, water quality, livelihoods, health and well-being, infrastructure, transportation, tourism and recreation as well as the culture of human societies, particularly for Indigenous Peoples. It also noted the risks of future displacements due to rising sea levels and associated coastal hazards.

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IPCC, 2019a: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate[H.-O. Pörtner, D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama, N.M. Weyer (eds.)]. https://report.ipcc.ch/srocc/pdf/SROCC_FinalDraft_FullReport.pdf. (1170 pp).

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Collins, M. et al., 2019: Extremes, Abrupt Changes and Managing Risk. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate[H.-O. Pörtner, D. C. R., V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama, N.M. Weyer (ed.)]. In press. Available at: https://www.ipcc.ch/site/assets/uploads/sites/3/2019/11/10_SROCC_Ch06_FINAL.pdf.

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IPCC, 2019b: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate[Pörtner, H.-O., D. C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama and N. M. Weyer (eds.)]. In press pp. Available at: https://www.ipcc.ch/srocc/download/ (accessed 25/10/2020).

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IPCC, 2019c: Summary for Policymakers. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate[H.-O. Pörtner, D. C. R., V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama, N.M. Weyer (ed.)]. Cambridge University Press, In press pp.

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IPCC, 2019d: Technical Summary[H.-O. Pörtner, D. C. R., V. Masson-Delmotte, P. Zhai, E. Poloczanska, K. Mintenbeck, M. Tignor, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama, N.M. Weyer (ed.)]. IPCC Special Report on the Ocean and Cryosphere in a Changing Climate In press, 39–69 pp. Available at: https://www.ipcc.ch/site/assets/uploads/sites/3/2019/11/04_SROCC_TS_FINAL.pdf (accessed 30/10/2020).

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Oppenheimer, M. et al., 2019: Sea Level Rise and Implications for Low-Lying Islands, Coasts and Communities[Pörtner, H. O., D. C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama and N. M. Weyer (eds.)]. IPCC Special Report on the Ocean and Cryosphere in a Changing Climate, In press pp. Available at: https://www.ipcc.ch/site/assets/uploads/sites/3/2019/12/SROCC_FullReport_FINAL.pdf.

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Hock, R., et al., 2019: High Mountain Areas. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate. [Masson-Delmotte, V., P. Zhai, H.-O. Pörtner, D. Roberts, J. Skea, P. R. Shukla, A. Pirani, W. Moufouma Okia, R. P. C. Péan, S. Connors, J. B. R. Matthews, Y. Chen, X. Zhou, M. I. Gomis, E. Lonnoy, T. Maycock, M. Tignor and T. Waterfield (eds.)], Cambridge University Press, Cambridge, pp. 1–94. ISBN 978-0321267979.

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Meredith, M., M. Sommerkorn, S. Cassotta, C. Derksen, A. Ekaykin, A. Hollowed, G. Kofinas, A. Mackintosh, J. Melbourne-Thomas, M.M.C. Muelbert, G. Ottersen, H. Pritchard, and E.A.G. Schuur, 2019: Polar Regions. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate. [Pörtner, H.-O., D. C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama and N. M. Weyer (eds.)]. Cambridge University Press, Cambridge, pp. 203–320.

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Oppenheimer, M., B.C. Glavovic, J. Hinkel, R. van de Wal, A.K. Magnan, A. Abd-Elgawad, R. Cai, M. Cifuentes-Jara, R.M. DeConto, T. Ghosh, J. Hay, F. Isla, B. Marzeion, B. Meyssignac, and Z. Sebesvari, 2019: Sea Level Rise and Implications for Low-Lying Islands, Coasts and Communities Supplementary Material. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate. [Pörtner, H.-O., D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama, N.M. Weyer (eds.)]. pp. 1–169.

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Evidence from Indigenous knowledge (IK) systems is included in this chapter to assess climate-change risks and solutions in North America following the framing provided in Chapter 1 Special Report on the Ocean and Cryosphere in a Changing Climate (SROCC) (Abram et al., 2019) and Special Report on Climate Change and Land (SRCCL) (IPCC, 2019a). Indigenous contributing authors provided this assessment, reflecting the importance of meaningfully including IK in assessment processes (Ford, 2012; Ford et al., 2016; Hill et al., 2020). This addition represents an important advancement since AR5 (IPCC, 2013; IPCC, 2014).

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Abram, N., J.-P. Gattuso, A. Prakash, L. Cheng, M.P. Chidichimo, S. Crate, H. Enomoto, M. Garschagen, N. Gruber, S. Harper, E. Holland, R.M. Kudela, J. Rice, K. Steffen, and K. von Schuckmann, 2019: Framing and Context of the Report. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate[H.-O. Pörtner, D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama, N.M. Weyer (eds.)]. In press.

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IPCC, 2019b: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate[H.-O. Pörtner, D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama, N.M. Weyer (eds.)]. In press.

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Meredith, M., M. Sommerkorn, S. Cassotta, C. Derksen, A. Ekaykin, A. Hollowed, G. Kofinas, A. Mackintosh, J. Melbourne-Thomas, M.M.C. Muelbert, G. Ottersen, H. Pritchard, and E.A.G. Schuur, 2019: Polar Regions. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate[H.-O. Pörtner, D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama, N.M. Weyer (eds.)]. IPCC.

special report on the ocean and cryosphereresources/ipcc/cleaned_content/wg2/Chapter15/html_with_ids.html#references_p2

Abram, N., J.-P. Gattuso, A. Prakash, L. Cheng, M.P. Chidichimo, S. Crate, H. Enomoto, M. Garschagen, N. Gruber, S. Harper, E. Holland, R.M. Kudela, J. Rice, K. Steffen, and K. von Schuckmann, 2019: Framing and Context of the Report Supplementary Material. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate[H.-O. Pörtner, D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama, N.M. Weyer (eds.)]. In press.

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Bindoff, N.L., W.W.L. Cheung, J.G. Kairo, J. Arístegui, V.A. Guinder, R. Hallberg, N. Hilmi, N. Jiao, M.S. Karim, L. Levin, S. O’Donoghue, S.R. Purca Cuicapusa, B. Rinkevich, T. Suga, A. Tagliabue, and P. Williamson, 2019: Changing Ocean, Marine Ecosystems, and Dependent Communities. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate[H.-O. Pörtner, D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama, N.M. Weyer (eds.)]. In press.

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IPCC, 2019: Special Report on the Ocean and Cryosphere in a Changing Climate[Pörtner, H. O., D. Roberts, V. Masson-Delmotte, P. Zhai, Y. Tignor, E. Poloczanska, K. Mintenbeck, M. Nicolai, A. Okem, J. Petzold, B. Rama and N. Weyer (eds.)] In press.

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Magnan, A.K., M. Garschagen, J.-P. Gattuso, J.E. Hay, N. Hilmi, E. Holland, F. Isla, G. Kofinas, I.J. Losada, J. Petzold, B. Ratter, T.Schuur, T. Tabe, and R. van de Wal, 2019: Cross-Chapter Box 9: Integrative Cross-Chapter Box on Low-Lying Islands and Coasts. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate[H.-O. Pörtner, D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama, N.M. Weyer (eds.)]. In press.

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Oppenheimer, M., B.C. Glavovic , J. Hinkel, R. van de Wal, A.K. Magnan, A. Abd-Elgawad, R. Cai, M. Cifuentes- Jara, R.M. DeConto, T. Ghosh, J. Hay, F. Isla, B. Marzeion, B. Meyssignac, and Z. Sebesvari, 2019: Sea Level Rise and Implications for Low-Lying Islands, Coasts and Communities. In: IPCC Special Report on the Ocean and Cryosphere in a C anging Climate[H.-O. Pörtner, D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama, N.M. Weyer (eds.)]. In press.

special report on the ocean and cryosphereresources/ipcc/cleaned_content/wg2/Chapter12/html_with_ids.html#references_p158

Bindoff, N.L., W.W.L. Cheung, J.G. Kairo, J. Arístegui, V.A. Guinder, R. Hallberg, N. Hilmi, N. Jiao, M.S. Karim, L. Levin, S. O’Donoghue, S.R. Purca Cuicapusa, B. Rinkevich, T. Suga, A. Tagliabue, and P. Williamson, 2019: Changing Ocean, Marine Ecosystems, and Dependent Communities. In: An IPCC Special Report on the Ocean and Cryosphere in a Changing Climate[H.-O. Pörtner, D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama, N.M. Weyer (eds.)]. Cambridge University Press, Cambridge, UK and New York, NY, USA.

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Hock, R., G. Rasul, C. Adler, B. Cáceres, S. Gruber, Y. Hirabayashi, M. Jackson, A. Kääb, S. Kang, S. Kutuzov, Al. Milner, U. Molau, S. Morin, B. Orlove, and H. Steltzer, 2019: High Mountain Areas. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate[H.-O. Pörtner, D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama, N.M. Weyer (eds.)]. Cambridge University Press, Cambridge, UK and New York, NY, USA.

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IPCC, 2019b: Summary for Policymakers. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate[H.-O. Pörtner, D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama, N.M. Weyer (eds.)]. Cambridge University Press, Cambridge, UK and New York, NY, USA.

special report on the ocean and cryosphereresources/ipcc/cleaned_content/wg3/Chapter03/html_with_ids.html#references_p60

Bindoff, N.L., W.W.L. Cheung, J.G. Kairo, J. Arístegui, V.A. Guinder, R. Hallberg, N. Hilmi, N. Jiao, M.S. Karim, L. Levin, S. O’Donoghue, S.R. Purca Cuicapusa, B. Rinkevich, T. Suga, A. Tagliabue, and P. Williamson, 2019: Changing Ocean, Marine Ecosystems, and Dependent Communities. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate[H.-O. Pörtner, D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama, N.M. Weyer (eds.)]. Cambridge University Press, Cambridge, UK and New York, NY, USA, pp. 447–587. https://doi.org/10.1017/9781009157964.007.

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Hock, R., G. Rasul, C. Adler, B. Cáceres, S. Gruber, Y. Hirabayashi, M. Jackson, A. Kääb, S. Kang, S. Kutuzov, Al. Milner, U. Molau, S. Morin, B. Orlove, and H. Steltzer, 2019: High Mountain Areas. In IPCC Special Report on the Ocean and Cryosphere in a Changing Climate[H.-O. Pörtner, D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama, N.M. Weyer (eds.)]. Cambridge University Press, Cambridge, UK and New York, NY, USA, pp. 131–202.

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IPCC, 2019b: IPCCSpecial Report on the Ocean and Cryosphere in a Changing Climate[H.-O. Pörtner, D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama, N.M. Weyer (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.

special report on the ocean and cryosphereresources/ipcc/cleaned_content/wg3/Chapter04/html_with_ids.html#references_p73

Bindoff, N.L., W.W.L. Cheung, J.G. Kairo, J. Arístegui, V.A. Guinder, R. Hallberg, N. Hilmi, N. Jiao, M.S. Karim, L. Levin, S. O’Donoghue, S.R. Purca Cuicapusa, B. Rinkevich, T. Suga, A. Tagliabue, and P. Williamson, 2019: Changing Ocean, Marine Ecosystems, and Dependent Communities. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate[H.-O. Pörtner, D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama, N.M. Weyer (eds.)]. Cambridge University Press, Cambridge, UK, and New York, NY, USA, pp. 447–588.

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IPCC, 2019: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate[H.-O. Pörtner, D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama, N.M. Weyer (eds.)]. Cambridge University Press, Cambridge, UK, and New York, NY, USA.

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IPCC, 2019b: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate[H.-O. Pörtner, D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama, N.M. Weyer (eds.)]. Cambridge University Press, Cambridge, UK, and New York, NY, USA.

special report on the ocean and cryosphereresources/ipcc/cleaned_content/wg3/Chapter06/html_with_ids.html#references_p140

Bindoff, N.L., W.W.L. Cheung, J.G. Kairo, J. Arístegui, V.A. Guinder, R. Hallberg, N. Hilmi, N. Jiao, M.S. Karim, L. Levin, S. O’Donoghue, S.R. Purca Cuicapusa, B. Rinkevich, T. Suga, A. Tagliabue, and P. Williamson, 2019: Changing Ocean, Marine Ecosystems, and Dependent Communities. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate[H.-O. Pörtner, D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama, N.M. Weyer (eds.)]. Cambridge University Press, Cambridge, UK and New York, NY, USA, pp. 447–588.

special report on the ocean and cryosphereresources/ipcc/cleaned_content/wg3/Chapter06/html_with_ids.html#references_p608

Hock, R., G. Rasul, C. Adler, B. Cáceres, S. Gruber, Y. Hirabayashi, M. Jackson, A. Kääb, S. Kang, S. Kutuzov, A. Milner, U. Molau, S. Morin, B. Orlove, and H. Steltzer, 2019: High Mountain Areas. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate[H.-O. Pörtner, D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama, N.M. Weyer (eds.)]. Cambridge University Press, Cambridge, UK and New York, NY, USA, 131–202 pp.

special report on the ocean and cryosphereresources/ipcc/cleaned_content/wg3/Chapter07/html_with_ids.html#7.4_p3

A summary of conclusions in the IPCC Fifth Assessment Report (AR5) and IPCC Special Reports (Special Report on Climate Change of 1.5°C (SR1.5), Special Report on the Ocean and Cryosphere in a Changing Climate (SROCC) and Special Report on Climate Change and Land (SRCCL)).

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IPCC, 2019a: Summary for Policymakers. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate[Pörtner, H.-O., D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama, N.M. Weyer (eds.)]. Cambridge University Press, Cambridge, UK and New York, NY, USA.

ipcc special report on climate change and landresources/ipcc/cleaned_content/wg3/Chapter12/html_with_ids.html#12.1.1_p2

Table 12.1 presents an overview of the cross-sectoral perspectives addressed in Chapter 12, mapping the chapter’s main themes to the sectoral and global chapters in this report. These mappings reflect the cross-sectoral aspects of mitigation options in the context of sustainable development, sectoral policy interactions, governance, implications in terms of international trade, spillover effects, and competitiveness, and cross-sectoral financing options for mitigation. While some cross-sector technologies are covered in more detail in sectoral chapters, this chapter covers important cross-sectoral linkages and provides synthesis concerning costs and potentials of mitigation options, and co-benefits and trade-offs that can be associated with deployment of mitigation options. Additionally, Chapter 12 covers CDR methods and specific considerations related to land use and food systems, complementing Chapter 7. The literature assessed in the chapter includes both peer-reviewed and grey literature since the Fifth Assessment Report (AR5) of the IPCC, including the IPCC Special Report on Global Warming of 1.5°C (SR1.5), the IPCC Special Report on Climate Change and Land (SRCCL) and the IPCC Special Report on the Ocean and Cryosphere in a Changing Climate (SROCC). Knowledge gaps are identified and reflected where encountered, as well as in a separate section. Finally, a strong link is maintained with sectoral chapters and the relevant global chapters of this report to ensure consistency.

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Bindoff, N.L., W.W.L. Cheung, J.G. Kairo, J. Arístegui, V.A. Guinder, R. Hallberg, N. Hilmi, N. Jiao, M.S. Karim, L. Levin, S. O’Donoghue, S.R. Purca Cuicapusa, B. Rinkevich, T. Suga, A. Tagliabue, and P. Williamson, 2019: Changing Ocean, Marine Ecosystems, and Dependent Communities. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate[Pörtner, H.-O., D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama and N.M. Weyer (eds.)]. Cambridge University Press, Cambridge, UK, and New York, NY, USA, pp. 447–587.

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IPCC, 2019b: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate[H.-O. Pörtner, D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama, N.M. Weyer (eds.)]. Cambridge University Press, Cambridge, UK and New York, NY, USA, 755 pp. https://doi.org/10.1017/9781009157964.

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Bindoff, N.L., W.W.L. Cheung, J.G. Kairo, J. Arístegui, V.A. Guinder, R. Hallberg, N. Hilmi, N. Jiao, M.S. Karim, L. Levin, S. O’Donoghue, S.R. Purca Cuicapusa, B. Rinkevich, T. Suga, A. Tagliabue, and P. Williamson, 2019: Changing Ocean, Marine Ecosystems, and Dependent Communities. In: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate[H.-O. Pörtner, D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama, N.M. Weyer (eds.)]. Cambridge University Press, Cambridge, UK and New York, NY, USA, pp. 447–587. https://doi.org/10.1017/9781009157964.007.

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Hock, R., G. Rasul, C. Adler, B. Cáceres, S. Gruber, Y. Hirabayashi, M. Jackson, A. Kääb, S. Kang, S. Kutuzov, Al. Milner, U. Molau, S. Morin, B. Orlove, and H. Steltzer, 2019: High Mountain Areas. In IPCC Special Report on the Ocean and Cryosphere in a Changing Climate[H.-O. Pörtner, D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama, N.M. Weyer (eds.)]. Cambridge University Press, Cambridge, UK and New York, NY, USA, pp. 131–202.

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IPCC, 2019b: IPCCSpecial Report on the Ocean and Cryosphere in a Changing Climate[H.-O. Pörtner, D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama, N.M. Weyer (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.

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This chapter assesses the evidence for human influence on observed large-scale indicators of climate change that are described in Cross-Chapter Box 2.2 and assessed in Chapter 2. It takes advantage of the longer period of record now available in many observational datasets. The assessment of the human-induced contribution to observed climate change requires an estimate of the expected response to human influence, as well as an estimate of the expected climate evolution due to natural forcings and an estimate of variability internal to the climate system (internal climate variability). For this we need high quality models, primarily climate and Earth system models. Since AR5, a new set of coordinated model results from the World Climate Research Programme (WCRP) Coupled Model Intercomparison Project Phase 6 (CMIP6; Eyring et al., 2016a) has become available. Together with updated observations of large-scale indicators of climate change (Chapter 2), CMIP simulations are a key resource for assessing human influence on the climate system. Pre-industrial control and historical simulations are of most relevance for model evaluation and assessment of internal variability, and these simulations are evaluated to assess fitness-for-purpose for attribution, which is the focus of this chapter (see also (Section 1.5.4). This chapter provides the primary evaluation of large-scale indicators of climate change in this Report, and is complemented by other fitness-for-purpose evaluations in subsequent chapters. CMIP6 also includes an extensive set of idealized and single forcing experiments for attribution (Eyring et al., 2016a; Gillett et al., 2016). In addition to the assessment of model performance and human influence on the climate system during the instrumental era up to the present-day, this chapter also includes evidence from paleo-observations and simulations over past millennia (Kageyama et al., 2018).

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Eyring, V. et al., 2016a: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geoscientific Model Development, 9(5), 1937–1958, doi: 10.5194/gmd-9-1937-2016.

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A central element of this chapter is a comprehensive assessment of the sources of uncertainty of future projections (Section 1.4.3). Uncertainty can be broken down into scenario uncertainty, model uncertainty involving model biases, uncertainty in simulated effective radiative forcing and model response, and the uncertainty arising from internal variability (Cox and Stephenson, 2007; Hawkins and Sutton, 2009). An additional source of projection uncertainty arises from possible future volcanic eruptions and future solar variability. Assessment of uncertainty relies on multi-model ensembles such as the Coupled Model Intercomparison Project Phase 6 (CMIP6, Eyring et al., 2016), single-model initial-condition large ensembles (e.g., Kay et al., 2015; Deser et al., 2020), and ensembles initialized from the observed climate state (decadal predictions, e.g., Smith et al., 2013a; Meehl et al., 2014; Boer et al., 2016; Marotzke et al., 2016). Ensemble evaluation methods include assessment of model performance and independence (e.g., Knutti et al., 2017; Boé, 2018; Abramowitz et al., 2019); emergent and other observational constraints (e.g., Allen and Ingram, 2002; Hall and Qu, 2006; Cox et al., 2018); and the uncertainty assessment of equilibrium climate sensitivity and transient climate response in Chapter 7. Ensemble evaluation is assessed in Box 4.1 through the inclusion of lines of evidence in addition to the projection ensembles, including implications for potential model weighting.

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Eyring, V. et al., 2016: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geoscientific Model Development, 9(5), 1937–1958, doi: 10.5194/gmd-9-1937-2016.

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Eyring, V. et al., 2016a: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geoscientific Model Development, 9(5), 1937–1958, doi: 10.5194/gmd-9-1937-2016.

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Eyring, V. et al., 2016: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geoscientific Model Development, 9(5), 1937–1958, doi: 10.5194/gmd-9-1937-2016.

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Pascoe, C., B.N. Lawrence, E. Guilyardi, M. Juckes, and K.E. Taylor, 2020: Documenting numerical experiments in support of the Coupled Model Intercomparison Project Phase 6 (CMIP6). Geoscientific Model Development, 13(5), 2149–2167, doi: 10.5194/gmd-13-2149-2020.

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The second advance is the use of a hierarchy of models and emulators to update projections of oceanic, cryospheric and sea level change arising from Coupled Model Intercomparison Project Phase 6 (CMIP6) and related projects (Section 1.5.4.3, Table 1.3, and Annex II). 2The CMIP6 included an ice-sheet modelling intercomparison for the first time. Particular modelling advances relevant to this chapter are the increase in ocean resolution in the High Resolution Model Intercomparison Project (HighResMIP) and Ocean Model Intercomparison Project phase 2 (OMIP-2) experiments (Sections 1.5.3.1 and 9.2), projections of future glacier (GlacierMIP) and ice sheet (ISMIP6) and Linear Antarctic Response Model Intercomparison Project (LARMIP-2) response from multi-model studies (Sections 9.5.1 and 9.4, and Box 9.3), and new methods to synthesize ocean and cryosphere models into sea level projections for all Shared Socieo-economic Pathway scenarios (SSPs; Sections 1.6.1, 9.4.1.3, 9.4.2.5 and 9.6.3, and Cross-Chapter Box 1.4) and warming levels (Sections 9.6.3 and 1.6.2, and Cross-Chapter Box 11.1). In particular, sea level projections and the individual contributions (Section 9.6.3.3) are consistent with equilibrium climate sensitivity and surface temperature assessments across this Report (Box 4.1 and Cross-Chapter Box 7.1).

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Eyring, V. et al., 2016: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geoscientific Model Development, 9(5), 1937–1958, doi: 10.5194/gmd-9-1937-2016.

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Figure 12.4 shows changes in six CID indices. These global maps are derived from Coupled Model Intercomparison Project Phase 6 (CMIP6) simulations for different time periods and scenarios (except for extreme total water level where CMIP Phase 5 (CMIP5) is used). The uncertainty due to climate models, time, scenarios and regional downscaling is illustrated in Supplementary Material SM.12.1 to SM.12.6, which show the distribution of the spatial average of the index among models over each land region for CMIP5, CMIP6 and Coordinated Regional Climate Downscaling Experiment (CORDEX) ensembles for the recent past, mid- and end-21st century, and for GWLs of +1.5°C, +2°C and +4°C. The hatching in the figure covers areas where less than 80% of models agree on the sign of change.

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Eyring, V. et al., 2016: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geoscientific Model Development, 9(5), 1937–1958, doi: 10.5194/gmd-9-1937-2016.

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The differences due to regions, RCPs and timeframes are related to the current temperature level and degree of warming (Figure 5.7). The projected effects of climate change are positive where current annual mean temperatures (Tave) are below 10°C, but they become negative with Tave above around 15°C. At Tave> 20°C, even a small degree of warming could result in adverse effects. In maize, negative effects are apparent at almost all temperature zones. A new study using the latest climate scenarios (Coupled Model Intercomparison Project Phase 6, CMIP6) and global gridded crop model ensemble projected that climate change impacts on major crop yields appear sooner than previously anticipated, mainly because of warmer climate projections and improved crop model sensitivities (Jägermeyr et al., 2021).

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Climate change exposes ocean and coastal ecosystems to changing environmental conditions, including ocean warming, SLR, acidification, deoxygenation and other climatic impact-drivers (CIDs), which have distinct regional and temporal characteristics (Gruber, 2011; IPCC, 2018). This section aims to build on the WGI AR6 assessment (Table 3.2) to provide an ecosystem-oriented framing of CIDs. Updating SROCC, projected trends assessed here are based on a new range of scenarios (Shared Socioeconomic Pathways, SSPs), as used in the Coupled Model Intercomparison Project Phase 6 (CMIP6; Section 1.2.2).

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Within the Coupled Model Intercomparison Project Phase 6 emissions database, a range of aviation emissions scenarios for a range of Shared Socio-economic Pathway (SSP) scenarios are available (Figure 10.13). This Figure suggests that by 2050, direct emissions from aviation could be 1.5 to 6.5 (5–95th percentile) times higher than in the 2020 model year under the scenarios that exceed warming of 4°C during the 21st century with a likelihood of 50% or greater (C8). In the C1 (which limit warming to 1.5°C (>50%) during the 2st century with no or limited overshoot) and C2 (which return warming to 1.5°C (>50%) during the 2st century after a high overshoot) scenarios, aviation emissions could still be up to 2.5 times higher in 2050 than in the 2020 model year (95th percentile) but may need to decrease by 10% by 2050 (5th percentile).

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(Section 5.4 covers the future projections of biogeochemical cycles and their feedbacks to the climate system fully utilizing the database of the concentration-driven CMIP6. Since AR5, Earth system models (ESMs) have made progress towards including more complex carbon cycle and associated biogeochemical processes that enable exploring a range of possible future carbon–climate feedbacks and their influences on the climate system. The section addresses uncertainties and limits of our models to predict future dynamics for GHG emissions trajectories, as well as new understanding on processes involved in carbon–climate feedbacks and the possibility for rapid and abrupt changes brought by non-linear dynamics.

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Multiple lines of evidence suggest that WUE has increased in near proportionality to atmospheric CO2 (high confidence) at a rate generally consistent with Earth system models (ESMs), despite variation in the WUE response to CO2 (De Kauwe et al., 2013; Frank et al., 2015; Keeling et al., 2017; Lavergne et al., 2019; Walker et al., 2021). Both field-scale CO2 enrichment experiments and process models show the effect of physiologically induced water savings, particularly under water-limiting conditions (De Kauwe et al., 2013; Farrior et al., 2015; Lu et al., 2016; Roy et al., 2016). Plants can also benefit from reduced drought stress due to enhanced CO2 without ecosystem-scale water savings (Jiang et al., 2021). To some extent, this increased WUE offsets the effects of enhanced vapour pressure deficit (VPD) on plant transpiration (Bobich et al., 2010; Creese et al., 2014; Jiao et al., 2019), but will have limited effect on ameliorating plant water stress during extreme drought events (Xu et al., 2016; Menezes-Silva et al., 2019; L. Liu et al., 2020), when leaf stomata are governed primarily by soil moisture (Roy et al., 2016).

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As summarized in SROCC (Section 5.2.2.4), there is a growing consensus that between 1970 and 2010 the open ocean has very likely lost 0.5–3.3% of its dissolved oxygen in the upper 1000 m depth (Section 2.3.3.6; Helm et al., 2011; Ito et al., 2017; Schmidtko et al., 2017; Bindoff et al., 2019). Regionally, the equatorial and North Pacific, the Southern Ocean and the South Atlantic have shown the greatest oxygen loss of up to 30 mol m–2 per decade (Schmidtko et al., 2017). Warming – via solubility reduction and circulation changes – mixing and respiration are considered the major drivers, with 50% of the oxygen loss for the upper 1000 m of the global oceans attributable to the solubility reduction (Schmidtko et al., 2017). Climate variability also modifies the oxygen loss on interannual and decadal time scales especially for the tropical ocean OMZs (Deutsch et al., 2011, 2014; Llanillo et al., 2013) and the North Pacific subarctic zone (Whitney et al., 2007; Sasano et al., 2018; Cummins and Ross, 2020). However, quantifying the oxygen decline and variability and attributing them to processes in different regions remains challenging (Levin, 2018; Oschlies et al., 2018). Earth system models (ESMs) in CMIP5 and CMIP6 corroborate the decline in ocean oxygen, and project a continuing and accelerating decline with a strong impact of natural climate variability under high-emissions scenarios (Bopp et al., 2013; Long et al., 2016; Kwiatkowski et al., 2020). However, CMIP5 models did not reproduce observed patterns for oxygen changes in the tropical thermocline, and generally simulated only about half the oxygen loss inferred from observations (Oschlies et al., 2018). CMIP6 models have a more realistic simulated mean state of ocean biogeochemistry than CMIP5 models due to improved ocean physical processes and better representation of biogeochemical processes (Séférian et al., 2020). Theyalso exhibit enhanced ocean warming as a result of an increase in the equilibrium climate sensitivity (ECS) of CMIP6 relative to CMIP5 models, which contributes to increased stratification and reduced subsurface ventilation (Sections 4.3.1, 4.3.4, 5.3.3.2, 7.4.2, 7.5.6, 9.2.1, and TS2.4). Consequently, CMIP6 model ensembles reproduce the ocean deoxygenation trend of −0.30 to −1.52 mmol m−3 per decade between 1970–2010 reported in SROCC (Section 5.2.2.4) with a verylikely range, and also project 32–71% greater subsurface (100–600 m) oxygen decline relative to their Representative Concentration Pathway (RCP) analogues in CMIP5, reaching to the likely range of decline of 6.4 ± 2.9 mmol m–3under SSP1–2.6 and 13.3 ± 5.3 mmol m–3under SSP5–8.5, from 1870–1899 to 2080–2099 (Kwiatkowski et al., 2020). It is concluded that the oxygen content of subsurface ocean is projected to transition to historically unprecedented condition with decline over the 21st century (medium confidence).

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This section covers biogeochemical feedbacks on climate change, which represent one of the largest sources of uncertainty in climate change projections. The relevant processes are discussed (Sections 5.4.1 to 5.4.4), prior to discussing the simulation and projection of the carbon cycle in Earth system models (Section 5.4.5), emergent constraints on future projections (Section 5.4.6), non-CO2 feedbacks (Section 5.4.7), combined feedback assessment (Section 5.4.8), possible biogeochemical abrupt changes (Section 5.4.9), long-term carbon cycle projections (Section 5.4.10), and near-term prediction of ocean and land carbon sinks (5.4.11).

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The AR5 assessed with very high confidence that the carbon cycle in the ocean and on land will continue to respond to climate change and rising atmospheric CO2 concentrations created during the 21st century (WGI, Chapter 6, Executive Summary). Since AR5, experiments with the Community Earth System Model version 1 (CESM1) under the RCP8.5 extension scenario to 2300, suggest that both land and ocean carbon–climate feedbacks strengthen in time, land and ocean carbon-concentration feedbacks weaken, and the relative importance of ocean sinks versus land sinks increases (Randerson et al., 2015). Under high emissions scenarios, this relative strengthening of land carbon–climate feedbacks leads the terrestrial biosphere to shift from sink to source at some point after 2100 in all of the CMIP5 ESMs and CMIP5-era Earth system models of intermediate complexity (EMICs) (Tokarska et al., 2016). The strengthening of land and ocean carbon–climate feedbacks projected beyond 2100 under high emissions scenarios offsets the declining climate sensitivity to incremental increases of CO2, leading to a net strengthening of carbon cycle feedbacks, as measured by the gain parameter, from one century to the next (Randerson et al., 2015).

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There is increased confidence in the near-constancy of TCRE because of the variety of methods that have been used to examine this relationship: sensitivity studies with Earth system models of intermediate complexity (EMICs; Herrington and Zickfeld, 2014; Ehlert et al., 2017); theory-based equations used to examine ESM and EMIC output (Goodwin et al., 2015; R.G. Williams et al., 2016, 2017b); and simple analytical models that capture aspects of the TCRE relationship (MacDougall and Friedlingstein, 2015). All studies agree that the near-constancy of the TCRE arises from compensation between the diminishing sensitivity of radiative forcing to CO2 at higher atmospheric concentration and the diminishing ability of the ocean to take up heat and carbon at higher cumulative emissions (Allen et al., 2009; Matthews et al., 2009; Frölicher and Paynter, 2015; Goodwin et al., 2015; Gregory et al., 2015; MacDougall and Friedlingstein, 2015; MacDougall, 2016; Tokarska et al., 2016; Ehlert et al., 2017).

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First, publications since AR5 applied methods that limit the effect of uncertainties in historical, diagnosed emissions in coupled Earth system models (ESMs) on estimates of the remaining carbon budget (Millar et al., 2017b; Tokarska and Gillett, 2018). These new methods express remaining carbon budget estimates relative to a recent reference period instead of relative to the pre-industrial period (Millar et al., 2017b; Tokarska et al., 2019b). Estimates of the full carbon budget since the pre-industrial period can still be obtained by adding estimates of historical CO2 emissions (Table 5.1) to the estimates in Table 5.8. This methodological update resulted, all other aspects being equal, in median estimates of remaining carbon budgets being about 350–450 GtCO2 larger compared to AR5 (IPCC, 2014; Millar et al., 2017b).

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In scenario simulations and idealized simulations with instantaneous CO2 removals applied from an equilibrium state, the removal effectiveness of CDR is found to be slightly dependent on the rate and amount of CDR (Tokarska and Zickfeld, 2015; Jones et al., 2016b; Zickfeld et al., 2021), and to be strongly dependent on the emissions scenario from which CDR is applied (Jones et al., 2016b; Zickfeld et al., 2021). The fraction of CO2 removed remaining out of the atmosphere decreases slightly for larger removals and decreases strongly when CDR is applied from a lower background atmospheric CO2 concentration (Figure 5.34), due to state dependencies and climate–carbon cycle feedbacks that lead to a stronger overall response to CO2 removal (Zickfeld et al., 2021). Based on the high agreement between studies, we assess with medium confidence that the removal effectiveness of CDR is only slightly dependent on the rate and magnitude of removal and is smaller at lower background atmospheric CO2 concentrations. Simulations with Earth system models of intermediate complexity (EMIC) with instantaneous CO2 removal from different equilibrium initial states suggest that the smaller removal effectiveness of CDR at lower background CO2 levels results in greater cooling per unit CO2 removed (Zickfeld et al., 2021). However, there is low confidence in the robustness of this result as climate sensitivity has been shown to exhibit opposite state dependence in EMICs and ESMs (Section 7.4.3.1).

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Anav, A. et al., 2013: Evaluating the Land and Ocean Components of the Global Carbon Cycle in the CMIP5 Earth System Models. Journal of Climate, 26(18), 6801–6843, doi: 10.1175/jcli-d-12-00417.1.

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Arora, V.K. et al., 2013: Carbon–concentration and carbon–climate feedbacks in CMIP5 Earth system models. Journal of Climate, 26(15), 5289–5314, doi: 10.1175/jcli-d-12-00494.1.

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Cabré, A., I. Marinov, and S. Leung, 2015: Consistent global responses of marine ecosystems to future climate change across the IPCC AR5 earth system models. Climate Dynamics, 45(5–6), 1253–1280, doi: 10.1007/s00382-014-2374-3.

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Eby, M. et al., 2013: Historical and idealized climate model experiments: an intercomparison of Earth system models of intermediate complexity. Climate of the Past, 9(3), 1111–1140, doi: 10.5194/cp-9-1111-2013.

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Fisher, R.A. et al., 2018: Vegetation demographics in Earth System Models: A review of progress and priorities. Global Change Biology, 24(1), 35–54, doi: 10. 1111/gcb.13910.

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Good, P., C. Jones, J. Lowe, R. Betts, and N. Gedney, 2013: Comparing tropical forest projections from two denerations of Hadley Centre Earth System Models, HadGEM2-ES and HadCM3LC. Journal of Climate, 26(2), 495–511, doi: 10.1175/jcli-d-11-00366.1.

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Hajima, T., K. Tachiiri, A. Ito, and M. Kawamiya, 2014: Uncertainty of concentration–terrestrial carbon feedback in Earth System Models. Journal of Climate, 27(9), 3425–3445, doi: 10.1175/jcli-d-13-00177.1.

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Hoffman, F.M. et al., 2014: Causes and implications of persistent atmospheric carbon dioxide biases in Earth System Models. Journal of Geophysical Research: Biogeosciences, 119(2), 141–162, doi: 10.1002/2013jg002381.

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Jones, C.D. et al., 2013: Twenty-First-Century Compatible CO2 Emissions and Airborne Fraction Simulated by CMIP5 Earth System Models under Four Representative Concentration Pathways. Journal of Climate, 26(13), 4398–4413, doi: 10.1175/jcli-d-12-00554.1.

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Kloster, S. and G. Lasslop, 2017: Historical and future fire occurrence (1850 to 2100) simulated in CMIP5 Earth System Models. Global and Planetary Change, 150, 58–69, doi: 10.1016/j.gloplacha.2016.12.017.

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Koven, C.D. et al., 2015b: Controls on terrestrial carbon feedbacks by productivity versus turnover in the CMIP5 Earth System Models. Biogeosciences, 12(17), 5211–5228, doi: 10.5194/bg-12-5211-2015.

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Luo, Y. et al., 2016: Toward more realistic projections of soil carbon dynamics by Earth system models. Global Biogeochemical Cycles, 30(1), 40–56, doi: 10.1002/2015gb005239.

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Mongwe, N.P., M. Vichi, and P.M.S. Monteiro, 2018: The seasonal cycle of pCO2 and CO2 fluxes in the Southern Ocean: diagnosing anomalies in CMIP5 Earth system models. Biogeosciences, 15(9), 2851–2872, doi: 10.5194/bg-15-2851-2018.

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Pongratz, J. et al., 2018: Models meet data: Challenges and opportunities in implementing land management in Earth system models. Global Change Biology, 24(4), 1470–1487, doi: 10. 1111/gcb.13988.

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Schwinger, J. et al., 2014: Nonlinearity of Ocean Carbon Cycle Feedbacks in CMIP5 Earth System Models. Journal of Climate, 27(11), 3869–3888, doi: 10.1175/jcli-d-13-00452.1.

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Thornhill, G. et al., 2021: Climate-driven chemistry and aerosol feedbacks in CMIP6 Earth system models. Atmospheric Chemistry and Physics, 21(2), 1105–1126, doi: 10.5194/acp-21-1105-2021.

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Chapters 2, 3 and 4 assess the current evidence basis for climatic changes, their causes, and their potential future under different possible emissions pathways using a combination of observations and state-of-the-art Earth system models (ESMs). The assessment in these chapters focuses on selected large-scale indicators and modes as defined in this Box. These indicators and modes of variability taken together characterize overall changes to the climate system as a whole.

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A well-understood physical constraint on the vertical gradient between the air and sea surface temperature is that it is approximately proportional to the turbulent sensible heat flux in the atmospheric surface layer (Chor et al., 2020). Similarly, the latent heat flux scales with the vertical humidity gradient and, in the global mean and in most oceanic regions, the latent heat flux is substantially larger than the sensible heat flux (Sections 7.2.1 and 9.2.1.3). If GSAT were to warm faster than GMST, the sensible surface heat flux would respond so as to reduce this difference. However, it is the sum of the sensible, latent, and radiative heat fluxes that controls GMST, so the sensible heat flux effect cannot be considered in isolation. Attempts to further constrain the combination of fluxes (e.g., Lorenz et al., 2010; Siler et al., 2019) rely on parameterizations or output from Earth system models (ESMs) or reanalyses and so are not considered independent. Apart from the above global considerations, regional and seasonal effects such as changes to the frequency and intensity of storms, sea state, cloudiness, sea ice cover, vegetation and land use may all affect the GSAT to GMST difference, either directly or by altering the relationships between gradients and energy fluxes. These changing energy flux relationships are monitored through observing the stratification of the upper ocean (Section 9.2.1.3) and the response of upper ocean processes (Cross-Chapter Box 5.3) in ESMs and reanalyses, but such monitoring tasks rival the observational challenge of directly observing SSTs and 2 m air temperature under a wide range of conditions. In summary, because of the lack of physical constraints and the complexity of processes driving changes in the GSAT to GMST temperature differences, there is no simple explanation based on physical grounds alone for how this difference responds to climate change.

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Beusch, L., L. Gudmundsson, and S.I. Seneviratne, 2020: Crossbreeding CMIP6 Earth System Models With an Emulator for Regionally Optimized Land Temperature Projections. Geophysical Research Letters, 47(15), e2019GL086812, doi: 10.1029/2019gl086812.

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New methods for model evaluation that are used in this chapter are described in Section 1.5.4. These include new techniques for process-based evaluation of climate and Earth system models against observations that have rapidly advanced since the publication of AR5 (Eyring et al., 2019) as well as newly developed CMIP evaluation tools that allow a more rapid and comprehensive evaluation of the models with observations (Eyring et al., 2016a, b).

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While (Chapter 9 (Sections 9.4 and 9.5) discusses process understanding for glaciers and ice sheets, as well as evaluation of global and regional-scale glacier and ice-sheet models, our focus here is on the attribution of large-scale changes in glaciers and ice sheets. Land ice in the form of glaciers has been included in CMIP climate and Earth system models as components of the land surface models for many years. However, their representation is simplified and is omitted altogether in the less complex modelling systems. In CMIP3 (Meehl et al., 2007) and CMIP5 (Taylor et al., 2012) land ice area fraction, a component of land surface models, was defined as a time-independent quantity, and in most model configurations was preset at the simulation initialization as a permanent land feature. In CMIP6 considerable progress has been made in improving and evaluating the representation of modelled land ice. For glaciers, an example is the expansion of the Joint UK Land Environment Simulator (JULES) land surface model to enable elevated tiles, and hence more accurately simulate the altitudinal atmospheric effects on glaciers (Shannon et al., 2019). Moreover, standalone glacier models have now been systematically compared in GlacierMIP (Hock et al., 2019a; Marzeion et al., 2020). The Antarctic and Greenland Ice Sheets were absent in global climate models that pre-date CMIP6 (Eyring et al., 2016a), however some preliminary analyses that used results from CMIP5 to drive standalone ice-sheet models were included in AR5 (Church et al., 2013a). For the first time in CMIP, the latest CMIP6 phase includes a coordinated effort to simulate temporally evolving ice sheets within the Ice Sheet Model Intercomparison Project (ISMIP6; Box 9.3; Nowicki et al., 2016). Our understanding of aspects of the global water storage contained in glaciers and ice sheets, and their contribution to sea-level rise, has improved since AR5 and SROCC (Hock et al., 2019b; Meredith et al., 2019) both in models and observations (see assessment of observations and model evaluation for the Greenland Ice Sheet in Sections 2.3.2.4.1 and 9.4.1; Antarctica in Sections 2.3.2.4.2 and 9.4.2; and glaciers in Sections 2.3.2.3 and 9.5.1).

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In summary, Earth system models simulate globally averaged land carbon sinks within the range of observation-based estimates (high confidence), but global-scale agreement masks large regional disagreements. Based on new studies that attribute changes in atmospheric CO2 seasonal cycle to CO2 fertilization, albeit counteracted by other factors, combined with the medium confidence that models represent the processes driving changes in the seasonal cycle, we assess that there is medium confidence that CO2 fertilization is the main driver of the increase in the amplitude of the seasonal cycle of atmospheric CO2. Based on available literature, CO2 fertilization has been the main driver of the observed greening trend, but there is only low confidence in this assessment because of ongoing debate about the relative roles of CO2 fertilization, high latitude warming, and land management, and the low number of models that represent the whole suite of processes involved.

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In summary, CMIP6 models perform generally better for a basket of variables covering mean historical climate across the atmosphere, ocean, and land domains than previous-generation and older models (high confidence). Earth System models characterized by additional biogeochemical feedbacks often perform at least as well as related more constrained, lower-complexity models lacking these feedbacks (medium confidence). In many cases, the models score similarly against both observational references, indicating that model errors are usually larger than observational uncertainties (high confidence). Moreover, synthesizing across Sections 3.3–3.7, we assess that the CMIP6 multi-model mean captures most aspects of observed climate change well (high confidence).

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Eyring, V. et al., 2020: Earth System Model Evaluation Tool (ESMValTool) v2.0 – an extended set of large-scale diagnostics for quasi-operational and comprehensive evaluation of Earth system models in CMIP. Geoscientific Model Development, 13(7), 3383–3438, doi: 10.5194/gmd-13-3383-2020.

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Hewitt, H.T. et al., 2020: Resolving and Parameterising the Ocean Mesoscale in Earth System Models. Current Climate Change Reports, 6(4), 137–152, doi: 10.1007/s40641-020-00164-w.

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Hoffman, F.M. et al., 2014: Causes and implications of persistent atmospheric carbon dioxide biases in Earth System Models. Journal of Geophysical Research: Biogeosciences, 119(2), 141–162, doi: 10.1002/2013jg002381.

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Lauer, A. et al., 2020: Earth System Model Evaluation Tool (ESMValTool) v2.0 – diagnostics for emergent constraints and future projections from Earth system models in CMIP. Geoscientific Model Development, 13(9), 4205–4228, doi: 10.5194/gmd-13-4205-2020.

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Meehl, G.A. et al., 2020: Context for interpreting equilibrium climate sensitivity and transient climate response from the CMIP6 Earth system models. Science Advances, 6(26), eaba1981, doi: 10.1126/sciadv.aba1981.

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Mongwe, N.P., M. Vichi, and P.M.S. Monteiro, 2018: The seasonal cycle of pCO2 and CO2 fluxes in the Southern Ocean: diagnosing anomalies in CMIP5 Earth system models. Biogeosciences, 15(9), 2851–2872, doi: 10.5194/bg-15-2851-2018.

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Pongratz, J. et al., 2018: Models meet data: Challenges and opportunities in implementing land management in Earth system models. Global Change Biology, 24(4), 1470–1487, doi: 10.1111/gcb.13988.

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Russell, J.L. et al., 2018: Metrics for the Evaluation of the Southern Ocean in Coupled Climate Models and Earth System Models. Journal of Geophysical Research: Oceans, 123(5), 3120–3143, doi: 10.1002/2017jc013461.

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Thomas, R.Q., E.N.J. Brookshire, and S. Gerber, 2015: Nitrogen limitation on land: how can it occur in Earth system models?Global Change Biology, 21(5), 1777–1793, doi: 10.1111/gcb.12813.

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Winkler, A.J., R.B. Myneni, G.A. Alexandrov, and V. Brovkin, 2019: Earth system models underestimate carbon fixation by plants in the high latitudes. Nature Communications, 10(1), 885, doi: 10.1038/s41467-019-08633-z.

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Yang, H. et al., 2017: Regional patterns of future runoff changes from Earth system models constrained by observation. Geophysical Research Letters, 44(11), 5540–5549, doi: 10.1002/2017gl073454.

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Similar to the approach used in AR5 (Flato et al., 2013), the primary lines of evidence of this chapter are comprehensive climate models (atmosphere–ocean general circulation models, AOGCMs) and Earth system models (ESMs); ESMs differ from AOGCMs by including representations of various biogeochemical cycles. We also build on results from ESMs of intermediate complexity (EMICs; Claussen et al., 2002; Eby et al., 2013) and other types of models where appropriate. This chapter focuses on a particular set of coordinated multi-model experiments known as model intercomparison projects (MIPs). These frameworks recommend and document standards for experimental design for running AOGCMs and ESMs to minimize the chance of differences in results being misinterpreted. CMIP is an activity of the World Climate Research Programme (WCRP), and the latest phase is CMIP6 (Eyring et al., 2016). To establish robustness of results, it is vital to assess the performance of these models in terms of mean state, variability, and the response to external forcings. That evaluation has been undertaken using the CMIP6 ‘Diagnostic, Evaluation and Characterization of Klima’ (DECK) and historical simulations in Chapter 3 of this Report, which concludes that there is high confidence that the CMIP6 multi-model mean captures most aspects of observed climate change well (Section 3.8.3.1).

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Climate model simulations can be performed in either ‘concentration-driven’ or ‘emissions-driven’ configurations reflecting whether the CO2 concentration is prescribed to follow a pre-defined pathway or is simulated by the Earth system models in response to prescribed emissions of CO2 (Box 6.4, Ciais et al., 2013). The majority of CMIP6 experiments are conducted in concentration-driven configurations in order to enable models without a fully interactive carbon cycle to perform them, and throughout most of this chapter we present results from those simulations unless otherwise stated. Concentrations of other greenhouse gases are always prescribed. However, the SSP5-8.5 scenario has also been performed in emissions-driven configuration (‘esm-ssp585’) by 10 ESMs, and in Section 4.3.1.1 we assess the impact on simulated climate over the 21st century.

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The AR5 assessed with very high confidence that climate models reproduce the general features of the global-scale annual mean surface temperature increase over the historical period, including the more rapid warming in the second half of the 20th century, and the cooling immediately following large volcanic eruptions. Furthermore, because climate and Earth system models are based on physical principles, they were assessed in AR5 to reproduce many important aspects of observed climate. Both aspects were argued to contribute to our confidence in the models’ suitability for their application in quantitative future predictions and projections (Flato et al., 2013). This Report assesses (in Section 3.8.2) with high confidence that for most large-scale indicators of climate change, the recent mean climate simulated by the latest generation climate models underpinning this assessment has improved compared to the models assessed in AR5, and with high confidence that the multi-model mean captures most aspects of observed climate change well. These assessments form the foundation of applying climate and Earth system models to the projections assessed in this chapter. Where appropriate, the assessment of projected changes is accompanied by an assessment of process understanding and model evaluation.

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This section assesses changes in climate beyond 2100. An advance since AR5 is the availability of ESM results for scenarios beyond 2100 and for much longer stabilisation simulations compared with analysis predominantly based on Earth system models of intermediate complexity (EMICs) at the time of AR5 (e.g., Eby et al., 2013; Zickfeld et al., 2013). Long-term commitment of sea level rise due to thermal expansion and ice-sheet loss is assessed in Chapter 9 (Section 9.6.3.5 and Figure 9.30). Here we assess projections of GSAT, global precipitation, and Arctic sea ice. Uncertainties relating to potential long-term changes in AMOC are treated in Section 9.2.3.1.

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Alterskjær, K. et al., 2013: Sea-salt injections into the low-latitude marine boundary layer: The transient response in three Earth system models. Journal of Geophysical Research: Atmospheres, 118(21), 12195–12206, doi: 10.1002/2013jd020432.

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Claussen, M. et al., 2002: Earth system models of intermediate complexity: closing the gap in the spectrum of climate system models. Climate Dynamics, 18(7), 579–586, doi: 10.1007/s00382-001-0200-1.

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Eby, M. et al., 2013: Historical and idealized climate model experiments: an intercomparison of Earth system models of intermediate complexity. Climate of the Past, 9(3), 1111–1140, doi: 10.5194/cp-9-1111-2013.

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Flato, G.M., 2011: Earth system models: an overview. WIREs Climate Change, 2(6), 783–800, doi: 10.1002/wcc.148.

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Meehl, G.A. et al., 2020: Context for interpreting equilibrium climate sensitivity and transient climate response from the CMIP6 Earth system models. Science Advances, 6(26), eaba1981, doi: 10.1126/sciadv.aba1981.

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Nohara, D. et al., 2015: Examination of a climate stabilization pathway via zero-emissions using Earth system models. Environmental Research Letters, 10(9), 095005, doi: 10.1088/1748-9326/10/9/095005.

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Schmidt, H. et al., 2012: Solar irradiance reduction to counteract radiative forcing from a quadrupling of CO2: Climate responses simulated by four earth system models. Earth System Dynamics, 3(1), 63–78, doi: 10.5194/esd-3-63-2012.

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Zarakas, C.M., A.L.S. Swann, M.M. Laguë, K.C. Armour, and J.T. Randerson, 2020: Plant Physiology Increases the Magnitude and Spread of the Transient Climate Response to CO2 in CMIP6 Earth System Models. Journal of Climate, 33(19), 8561–8578, doi: 10.1175/jcli-d-20-0078.1.

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Model-based regional climate projections are all based upon some type of global model, including state-of-the-art Earth system models (ESMs), coupled atmosphere–ocean general circulation models (GCMs) or atmosphere-only general circulation models (AGCMs) (see Section 1.5.3.1). They are collectively referred to as global models.

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Eyring, V. et al., 2016b: ESMValTool (v1.0) – a community diagnostic and performance metrics tool for routine evaluation of Earth system models in CMIP. Geoscientific Model Development, 9(5), 1747–1802, doi: 10.5194/gmd-9-1747-2016.

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Levine, P.A., J.T. Randerson, S.C. Swenson, and D.M. Lawrence, 2016: Evaluating the strength of the land–atmosphere moisture feedback in Earth system models using satellite observations. Hydrology and Earth System Sciences, 20(12), 4837–4856, doi: 10.5194/hess-20-4837-2016.

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McDermid, S.S., L.O. Mearns, and A.C. Ruane, 2017: Representing agriculture in Earth System Models: Approaches and priorities for development. Journal of Advances in Modeling Earth Systems, 9(5), 2230–2265, doi: 10.1002/2016ms000749.

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Nazemi, A. and H.S. Wheater, 2015: On inclusion of water resource management in Earth system models – Part 1: Problem definition and representation of water demand. Hydrology and Earth System Sciences, 19(1), 33–61, doi: 10.5194/hess-19-33-2015.

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Pokhrel, Y.N., N. Hanasaki, Y. Wada, and H. Kim, 2016: Recent progresses in incorporating human land-water management into global land surface models toward their integration into Earth system models. WIREs Water, 3(4), 548–574, doi: 10.1002/wat2.1150.

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Sharma, A. et al., 2020: Urban-Scale Processes in High-Spatial-Resolution Earth System Models. Bulletin of the American Meteorological Society, 101(9), E1555–E1561, doi: 10.1175/bams-d-20-0114.1.

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There is high confidence that land–atmosphere feedbacks play a substantial or dominant role in affecting precipitation deficits in someregions (SREX, Chapter 3; Koster et al., 2011; Gimeno et al., 2012; Taylor et al., 2012; Guillod et al., 2015; Tuttle and Salvucci, 2016; Santanello Jr. et al., 2018; Haslinger et al., 2019; Herrera-Estrada et al., 2019). The sign of the feedbacks can be either positive or negative, as well as local or non-local (Taylor et al., 2012; Guillod et al., 2015; Tuttle and Salvucci, 2016). Earth system models (ESMs) tend to underestimate non-local negative soil-moisture–precipitation feedbacks (Taylor et al., 2012) and also show high variations in their representation in some regions (Berg et al., 2017b). Soil-moisture–precipitation feedbacks contribute to changes in precipitation in climate model projections in some regions, but ESMs display substantial uncertainties in their representation, and there is thus onlylow confidence in these contributions (Berg et al., 2017b; Vogel et al., 2017, 2018).

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For projections at 1.5°C of global warming, evidence is drawn from: L. Xu et al. (2019), based on CMIP5; 11.SM based on CMIP6 for changes in total column and surface soil moisture; and from Naumann et al. (2018) for changes in SPEI-PM, based on EC-Earth simulations driven with SSTs from seven CMIP5 Earth system models. For projections at 2°C of global warming, evidence is drawn from L. Xu et al. (2019) based on CMIP5, and Cook et al. (2020) (SSP1-2.6, 2071–2100 compared to pre-industrial) and the Chapter 11 Supplementary Material (11.SM) based on CMIP6, for changes in total column and surface soil moisture; evidence is also drawn from Naumann et al. (2018) for changes in SPEI-PM. For projections at 4°C of global warming, evidence is mostly drawn from: Cook et al. (2020) (SSP3-7.0, 2071–2100) and the Chapter 11 Supplementary Material (11.SM) based on CMIP6 for changes in total column and surface soil moisture; and from Vicente-Serrano et al. (2020c) for changes in SPEI-PM based on CMIP5. No global-scale studies with regional-scale information are available for the attribution of agricultural and ecological droughts, so this assessment is based on regional detection and attribution or event attribution studies.

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In summary, since AR5 substantial advances have been made in the representation of land surface processes in current-generation Earth System Models (ESMs). Offline hydrological models allow the application of bias-adjusted atmospheric forcings, but there is low confidence of an improved response compared to coupled climate models, given their inherent limitations (Box 10.2). While improvements in the representation of complex land surface feedbacks relevant to the water cycle are needed, there is currentlylow confidence that they will systematically improve the reliability of water cycle projections.

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In AR5, some simulations using a coupled climate–carbon cycle model exhibited an abrupt dieback of the Amazon forest in future climate scenarios (Oyama and Nobre, 2003; Cox et al., 2004; Malhi et al., 2008).However, subsequent work demonstrated that abrupt Amazon dieback does not occur consistently across, or even within, Earth system models (Lambert et al., 2013; Boulton et al., 2017). The occurrence of dieback is highly dependent on both how dry the simulated climate is in the present day (Malhi et al., 2009) as well as the representation of forest structure and competitive dynamics (Levine et al., 2016). Models with a low diversity of plant characteristics and types have a higher tendency for abrupt change (Sakschewski et al., 2016). Abrupt shifts and ecosystem disruptions can occur on the sub-regional level (Pires and Costa, 2013), highlighting the need for higher-resolution modelling studies. Since AR5, CMIP6 projections suggest that a tipping point in the Amazon system may be crossed on a local or regional scale (Staal et al., 2020) but continue to be highly dependent on model biases in precipitation and the simulation of the land surface. Consequently, the timing, and probability, of an abrupt shift remains difficult to ascertain.

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Bathiany, S., M. Claussen, and V. Brovkin, 2014: CO2 -Induced Sahel Greening in Three CMIP5 Earth System Models. Journal of Climate, 27(18), 7163–7184, doi: 10.1175/jcli-d-13-00528.1.

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Bódai, T., G. Drótos, M. Herein, F. Lunkeit, and V. Lucarini, 2020: The Forced Response of the El Niño-Southern Oscillation–Indian Monsoon Teleconnection in Ensembles of Earth System Models. Journal of Climate, 33(6), 2163–2182, doi: 10.1175/jcli-d-19-0341.1.

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Bonan, G.B. and S.C. Doney, 2018: Climate, ecosystems, and planetary futures: The challenge to predict life in Earth system models. Science, 359(6375), eaam8328, doi: 10.1126/science.aam8328.

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Fisher, R.A. et al., 2018: Vegetation demographics in Earth System Models: A review of progress and priorities. Global Change Biology, 24(1), 35–54, doi: 10.1111/gcb.13910.

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Franks, P.J. et al., 2018: Comparing optimal and empirical stomatal conductance models for application in Earth system models. Global Change Biology, 24(12), 5708–5723, doi: 10.1111/gcb.14445.

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Pokhrel, Y.N., N. Hanasaki, Y. Wada, and H. Kim, 2016: Recent progresses in incorporating human land–water management into global land surface models toward their integration into Earth system models. WIREs Water, 3, 548–574, doi: 10.1002/ wat2.1150.

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Yang, H. et al., 2017: Regional patterns of future runoff changes from Earth system models constrained by observation. Geophysical Research Letters, 44(11), 5540–5549, doi: 10.1002/2017gl073454.

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The science assessed in Chapters 2 to 7, such as the carbon budget, short-lived climate forcers (SLCFs) and emissions metrics, are topics in common with WGIII, and relevant for the mitigation of climate change. This includes a consistent presentation of the concepts of carbon budget and net zero emissions targets within chapters, in order to support integration in the Synthesis Report. Emissions-driven emulators (simple climate models), summarized in Cross-Chapter Box 7.1, are used to approximate large-scale climate responses of complex Earth System Models (ESMs) and have been used as tools to explore the expected global surface air temperature (GSAT) response to multiple scenarios consistent with those assessed in WGI for the classification of scenarios in WGIII. Chapter 6 provides information about the impact of climate change on global air pollution, relevant for WGII, including Cross-Chapter Box 6.1 on the implications of the recent coronavirus pandemic (COVID-19) for climate and air quality. Cross-Chapter Box 2.3 in Chapter 2 presents an integrated cross-Working Group discussion of global temperature definitions, with implications for many aspects of climate change science.

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In the 1990s, AOGCMs were state of the art. By the 2010s, Earth system models (ESMs, also known as coupled carbon-cycle climate models) incorporated land surface, vegetation, the carbon cycle, and other elements of the climate system. Since the 1990s, some major modelling centres have deployed ‘unified’ models for both weather prediction and climate modelling, with the goal of a seamless modelling approach that uses the same dynamics, physics and parameterisations at multiple scales of time and space (Section 10.1.2; Cullen, 1993; Brown et al., 2012; NRC, 2012; WMO, 2015). Because weather forecast models make short-term predictions that can be frequently verified, and improved models are introduced and tested iteratively on cycles as short as 18 months, this approach allows major portions of the climate model to be evaluated as a weather model and more frequently improved. However, all climate models exhibit biases of different degrees and types, and the practice of ‘tuning’ parameter values in models to make their outputs match variables such as historical warming trajectories has generated concern throughout their history (Section 1.5.3.2; Randall and Wielicki, 1997; Edwards, 2010; Hourdin et al., 2017). Overall, AR5 WGI assessed that climate models had improved since previous reports (IPCC, 2013b).

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Reconstructions of paleo ocean pH (Section 2.3.3.5) have increased in number and accuracy, providing new constraints on ocean pH across the last centuries (e.g., Wu et al., 2018), the last glacial cycles (e.g., Moy et al., 2019), and the last several million years (e.g., Anagnostou et al., 2020). Such reconstructions inform processes and act as benchmarks for Earth system models of the global carbon cycle over the recent geologic past (Section 5.3.1), including previous high-CO2 warm intervals such as the Pliocene (Cross-Chapter Box 2.4). Particularly relevant to such investigations are reconstructions of atmospheric CO2 (Honisch et al., 2012; Foster et al., 2017) that span the past millions to tens of millions of years.

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A wide range of numerical models is widely used in climate science to study the climate system and its behaviour across multiple temporal and spatial scales. These models are the main tools available to look ahead into possible climate futures under a range of scenarios (Section 1.6). Global Earth system models (ESMs) are the most complex models that contribute to AR6. At the core of each ESM is a GCM (general circulation model) representing the dynamics of the atmosphere and ocean. ESMs are complemented by regional models (Section 10.3.1) and by a hierarchy of models of lower complexity. This section summarizes major developments in these different types of models since AR5. Past IPCC reports have made use of multi-model ensembles generated through various phases of the World Climate Research Programme (WCRP) Coupled Model Intercomparison Project (CMIP). Analysis of the latest CMIP Phase 6 (CMIP6; Eyring et al., 2016) simulations constitute a key line of evidence supporting this Assessment Report (Section 1.5.4). The key characteristics of models participating in CMIP6 are listed in Annex II: Models.

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Earth system models are mathematical formulations of the natural laws that govern the evolution of climate-relevant systems: atmosphere, ocean, cryosphere, land, and biosphere, as well as the carbon cycle (Flato, 2011). They build on the fundamental laws of physics (e.g., Navier–Stokes or Clausius–Clapeyron equations) or empirical relationships established from observations and, when possible, they are constrained by fundamental conservation laws (e.g., mass and energy). The evolution of climate-relevant variables is computed numerically using high-performance computers (André et al., 2014; Balaji et al., 2017), on three-dimensional discrete grids (Staniforth and Thuburn, 2012). The spatial (and temporal) resolution of these grids in both the horizontal and vertical directions determines which processes need to be parameterized or whether they can be explicitly resolved. Developments since AR5 in model resolution, parameterizations and modelling of the land and ocean biosphere and of biogeochemical cycles are discussed below.

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Earth system models of intermediate complexity(EMICs) complement the model hierarchy and fill the gap between conceptual, simple climate models and complex GCMs or ESMs (Claussen et al., 2002). EMICs are simplified; they include processes in a more parameterized, rather than explicitly calculated, form and generally have lower spatial resolution compared to the complex ESMs. As a result, EMICs require much less computational resource and can be integrated for many thousands of years without supercomputers (Hajima et al., 2014). The range of EMICs used in climate change research is highly heterogeneous, ranging from zonally averaged or mixed-layer ocean models coupled to statistical-dynamical models of the atmosphere, to low-resolution three-dimensional ocean models coupled to simplified dynamical models of the atmosphere. An increasing number of EMICs include interactive representations of the global carbon cycle, with varying levels of complexity and numbers of processes considered (Plattner et al., 2008; Zickfeld et al., 2013; MacDougall et al., 2020). Given the heterogeneity of the EMIC community, modellers tend to focus on specific research questions and develop individual models accordingly. As for any type of models assessed in this Report, the set of EMICs undergoes thorough evaluation and fit-for-purpose testing before being applied to address specific climate aspects.

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This section introduces three ways to synthesize climate change knowledge across topics and chapters. These ‘dimensions of integration’ include (i) emissions and concentration scenarios underlying the climate change projections assessed in this Report, (ii) levels of global mean surface warming relative to the 1850–1900 baseline (‘global warming levels’), and (iii) cumulative carbon emissions (Figure 1.24). All three dimensions can, in principle, be used to synthesize physical science knowledge across WGI, and also across climate change impacts, adaptation, and mitigation research. Scenarios, in particular, have a long history of serving as a common reference point within and across IPCC Working Groups and research communities. Similarly, cumulative carbon emissions and global warming levels provide key links between WGI assessments and those of the other WGs; these two dimensions frame the cause–effect chain investigated by WGI. The closest links to WGIII are the emissions scenarios, as WGIII considers drivers of emissions and climate change mitigation options. The links to WGII are the geophysical climate projections from the Earth system models, which are often used as the starting point in the literature on climate impacts and adaptation.

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Anav, A. et al., 2013: Evaluating the Land and Ocean Components of the Global Carbon Cycle in the CMIP5 Earth System Models. Journal of Climate, 26(18), 6801–6843, doi: 10.1175/jcli-d-12-00417.1.

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Balaji, V. et al., 2017: CPMIP: measurements of real computational performance of Earth system models in CMIP6. Geoscientific Model Development, 10(1), 19–34, doi: 10.5194/gmd-10-19-2017.

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Beusch, L., L. Gudmundsson, and S.I. Seneviratne, 2020a: Crossbreeding CMIP6 Earth System Models With an Emulator for Regionally Optimized Land Temperature Projections. Geophysical Research Letters, 47(15), e2019GL086812, doi: 10.1029/2019gl086812.

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Claussen, M. et al., 2002: Earth system models of intermediate complexity: closing the gap in the spectrum of climate system models. Climate Dynamics, 18(7), 579–586, doi: 10.1007/s00382-001-0200-1.

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Eby, M. et al., 2013: Historical and idealized climate model experiments: an intercomparison of Earth system models of intermediate complexity. Climate of the Past, 9(3), 1111–1140, doi: 10.5194/cp-9-1111-2013.

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Eyring, V. et al., 2020: Earth System Model Evaluation Tool (ESMValTool) v2.0 – an extended set of large-scale diagnostics for quasi-operational and comprehensive evaluation of Earth system models in CMIP. Geoscientific Model Development, 13(7), 3383–3438, doi: 10.5194/gmd-13-3383-2020.

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Flato, G., 2011: Earth system models: an overview. WIREs Climate Change, 2(6), 783–800, doi: 10.1002/wcc.148.

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Good, P., C. Jones, J. Lowe, R. Betts, and N. Gedney, 2013: Comparing Tropical Forest Projections from Two Generations of Hadley Centre Earth System Models, HadGEM2-ES and HadCM3LC. Journal of Climate, 26(2), 495–511, doi: 10.1175/jcli-d-11-00366.1.

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Lauer, A. et al., 2020: Earth System Model Evaluation Tool (ESMValTool) v2.0 – diagnostics for emergent constraints and future projections from Earth system models in CMIP. Geoscientific Model Development, 13(9), 4205–4228, doi: 10.5194/gmd-13-4205-2020.

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Meehl, G.A. et al., 2020: Context for interpreting equilibrium climate sensitivity and transient climate response from the CMIP6 Earth system models. Science Advances, 6(26), eaba1981, doi: 10.1126/sciadv.aba1981.

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Pfister, P.L. and T.F. Stocker, 2017: State-Dependence of the Climate Sensitivity in Earth System Models of Intermediate Complexity. Geophysical Research Letters, 44(20), 10643–10653, doi: 10.1002/2017gl075457.

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Pongratz, J. et al., 2018: Models meet data: Challenges and opportunities in implementing land management in Earth system models. Global Change Biology, 24(4), 1470–1487, doi: 10. 1111/gcb.13988.

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Kirkevåg, A. et al., 2018: A production-tagged aerosol module for Earth system models, OsloAero5.3 – extensions and updates for CAM5.3-Oslo. Geoscientific Model Development, 11(10), 3945–3982, doi: 10.5194/gmd-11-3945-2018.

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Meehl, G.A. et al., 2020: Context for interpreting equilibrium climate sensitivity and transient climate response from the CMIP6 Earth system models. Science Advances, 6(26), eaba1981, doi: 10.1126/sciadv.aba1981.

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Thornhill, G.D. et al., 2021a: Climate-driven chemistry and aerosol feedbacks in CMIP6 Earth system models. Atmospheric Chemistry and Physics, 21(2), 1105–1126, doi: 10.5194/acp-21-1105-2021.

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Vira, J., P. Hess, J. Melkonian, and W.R. Wieder, 2020: An improved mechanistic model for ammonia volatilization in Earth system models: Flow of Agricultural Nitrogen version 2 (FANv2). Geoscientific Model Development, 13(9), 4459–4490, doi: 10.5194/gmd-13-4459-2020.

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Regional changes in temperature, rainfall, and climate extremes have been found to correlate well with the forced changes in GSAT within Earth System Models (ESMs; Section 4.6.1; Giorgetta et al., 2013; Tebaldi and Arblaster, 2014; Seneviratne et al., 2016). While this so-called ‘pattern scaling’ has important limitations arising from, for instance, localized forcings, land-use changes, or internal climate variability (Deser et al., 2012; Luyssaert et al., 2014), changes in GSAT nonetheless explain a substantial fraction of inter-model differences in projections of regional climate changes over the 21st century (Tebaldi and Knutti, 2018). This Chapter’s assessments of TCR and ECS thus provide constraints on future global and regional climate change (Chapters 4 and 11).

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The assessment of ERFs in AR5 was preliminary because ERFs were only available for a few forcing agents, so for many forcing agents the Report made the assumption that ERF and SARF were equivalent. This section discusses the body of work published since AR5. This work has computed ERFs across many more forcing agents and models; closely examined the methods of computation; quantified the processes involved in causing adjustments; and examined how well ERFs predict the ultimate temperature response. This work is assessed to have led to a much-improved understanding and increased confidence in the quantification of radiative forcing across the Report. These same techniques allow for an evaluation of radiative forcing within Earth system models (ESMs) as a key test of their ability to represent both historical and future temperature changes (Sections 3.3.1 and 4.3.4).

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Climate model emulators are simple physically based models that are used to approximate large-scale climate responses of complex Earth system models (ESMs). Due to their low computational cost they can populate or span wide uncertainty ranges that ESMs cannot. They need to be calibrated to do this and, once calibrated, they can aid inter-ESM comparisons and act as ESM extrapolation tools to reflect and combine knowledge from ESMs and many other lines of evidence (Geoffroy et al., 2013a; Good et al., 2013; Smith et al., 2018a). In AR6, the term ‘climate model emulator’ (or simply ‘emulator’) is preferred over ‘simple’ or ‘reduced-complexity climate model’ to reinforce their use as specifically calibrated tools (Cross-Chapter Box 7.1, Figure 1). Nonetheless, simple physically based climate models have a long history of use in previous IPCC reports (Section 1.5.3.4). Climate model emulators can include carbon and other gas cycles and can combine uncertainties along the cause–effect chain, from emissions to temperature response. AR5 (M. Collins et al., 2013) used the MAGICC6 emulator (Meinshausen et al., 2011a) in a probabilistic setup (Meinshausen et al., 2009) to explore the uncertainty in future projections. A simple impulse response emulator (Good et al., 2011) was also used to ensure a consistent set of ESM projections could be shown across a range of scenarios. Chapter 8 in AR5 WGI (Myhre et al., 2013b) employed a two-layer emulator for quantifying global temperature-change potentials (GTP). In AR5 WGIII (Clarke et al., 2014), MAGICC6 was also used for the classification of scenarios, and in AR5 Synthesis Report (IPCC, 2014) this information was used to estimate carbon budgets. In SR1.5, two emulators were used to provide temperature projections of scenarios: the MAGICC6 model, which was used for the scenario classification, and the FaIR1.3 model (Millar et al., 2017; Smith et al., 2018a).

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In contrast to most ESMs, the majority of Earth system models of intermediate complexity (EMICs) do not exhibit state-dependence, or have a net feedback parameter that decreases with increasing temperature (Pfister and Stocker, 2017). This is unsurprising since EMICs usually do not include process-based representations of water-vapour and cloud feedbacks. Although this shows that care must be taken when interpreting results from current generation EMICs, Pfister and Stocker (2017) also suggest that non-linearities in feedbacks can take a long time to emerge in model simulations due to slow adjustment time scales associated with the ocean; longer simulations also allow better estimates of equilibrium warming (Bloch-Johnson et al., 2020). This implies that multi-century simulations (Rugenstein et al., 2020) could increase confidence in ESM studies examining state-dependence.

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Before AR6, the assessment of ECS relied on either CO2 -doubling experiments using global atmospheric models coupled with mixed-layer ocean or standardized CO2 -quadrupling (abrupt 4xCO2 ) experiments using fully coupled ocean–atmosphere models or Earth system models (ESMs). The TCR has similarly been diagnosed from ESMs in which the CO2 concentration is increased at 1% yr–1(1pctCO2 , an approximately linear increase in ERF over time) and is in practice estimated as the average over a 20-year period centred at the time of atmospheric CO2 doubling, that is, year 70. In AR6, the assessments of ECS and TCR are made based on multiple lines of evidence, with ESMs representing only one of several sources of information. The constraints on these climate metrics are based on radiative forcing and climate feedbacks assessed from process understanding (Section 7.5.1), climate change and variability seen within the instrumental record (Section 7.5.2), paleoclimate evidence (Section 7.5.3), emergent constraints (Section 7.5.4), and a synthesis of all lines of evidence (Section 7.5.5). In AR5, these lines of evidence were not explicitly combined in the assessment of climate sensitivity, but as demonstrated by Sherwood et al. (2020) their combination narrows the uncertainty ranges of ECS compared to that assessed in AR5. ECS values found in CMIP6 models, some of which exhibit values higher than 5°C (Meehl et al., 2020; Zelinka et al., 2020), are discussed in relation to the AR6 assessment in section 7.5.6.

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Arora, V.K. et al., 2013: Carbon–concentration and carbon–climate feedbacks in CMIP5 earth system models. Journal of Climate, 26(15), 5289–5314, doi: 10.1175/jcli-d-12-00494.1.

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Dorheim, K., R. Link, C. Hartin, B. Kravitz, and A. Snyder, 2020: Calibrating Simple Climate Models to Individual Earth System Models: Lessons Learned From Calibrating Hector. Earth and Space Science, 7(11), e2019EA000980, doi: 10.1029/2019ea000980.

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Meehl, G.A. et al., 2020: Context for interpreting equilibrium climate sensitivity and transient climate response from the CMIP6 Earth system models. Science Advances, 6(26), eaba1981, doi: 10.1126/sciadv.aba1981.

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Pfister, P.L. and T.F. Stocker, 2017: State-Dependence of the Climate Sensitivity in Earth System Models of Intermediate Complexity. Geophysical Research Letters, 44(20), 10643–10653, doi: 10.1002/2017gl075457.

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Thornhill, G.D. et al., 2021a: Climate-driven chemistry and aerosol feedbacks in CMIP6 Earth system models. Atmospheric Chemistry and Physics, 21(2), 1105–1126, doi: 10.5194/acp-21-1105-2021.

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Zarakas, C.M., A.L.S. Swann, M.M. Laguë, K.C. Armour, and J.T. Randerson, 2020: Plant Physiology Increases the Magnitude and Spread of the Transient Climate Response to CO2 in CMIP6 Earth System Models. Journal of Climate, 33(19), 8561–8578, doi: 10.1175/jcli-d-20-0078.1.

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The North Atlantic sea level change dipole is forced by a reduction in heat loss from the ocean north of 40°N (i.e., net heat uptake), which in all Earth system models leads to a weakening of the AMOC, although the magnitude has a large model spread (Section 9.2.3.1; Gregory et al., 2016; Huber and Zanna, 2017). The reduced northward transport of warm, salty water (Section 9.2.2) causes further ocean dynamic sea level change, whose details are model-dependent. North of 40°N, this redistribution leads to a sea level rise, predominantly halosteric, reinforcing the thermosteric effect of heat uptake (Couldrey et al., 2021). Comparison of observed Atlantic OHC for 1955–2017 with a reconstruction assuming no change in circulation indicates that the thermosteric sea level change resulting from southward redistribution of heat may be detectable (Zanna et al., 2019). This redistribution causes a tendency for SST cooling north of 40°N and anomalous heat input from the atmosphere, and thus a positive feedback on AMOC weakening (Winton et al., 2013; Gregory et al., 2016; Todd et al., 2020; Couldrey et al., 2021). Many climate and ocean models agree that the AMOC weakening is associated with pronounced thermosteric sea level rise along the American coast around 40°N (Figures 9.12 and 9.26), leading to a relatively large ocean dynamic sea level rise in this region (Yin, 2012; Bouttes et al., 2014; Slangen et al., 2014b; Little et al., 2019; Lyu et al., 2020a).

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The SROCC noted increased use of coupled climate–ice sheet models for simulating the Greenland ice sheet, but it also noted that remaining deficiencies in coupling between models of climate and ice sheets (e.g., low spatial resolution) limited the adequate representation of the feedbacks between them. Some Earth system models (ESMs) now incorporate multi-layer snow models and full energy balance models (Punge et al., 2012; Cullather et al., 2014; van Kampenhout et al., 2017, 2020; Alexander et al., 2019) or use elevation classes to compensate for their coarser resolution (Lipscomb et al., 2013; Sellevold et al., 2019; Gregory et al., 2020; Muntjewerf et al., 2020a, b). Resulting SMB simulations compare better with regional climate models and observations (Alexander et al., 2019; van Kampenhout et al., 2020), but the remaining shortcomings lead to problems reproducing a present-day ice-sheet state close to observations. In summary, there is medium confidence in quantitative simulations of the present-day state of the Greenland Ice Sheet in ESMs.

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Hewitt, H.T. et al., 2020: Resolving and Parameterising the Ocean Mesoscale in Earth System Models. Current Climate Change Reports, 6(4), 137–152, doi: 10.1007/s40641-020-00164-w.

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Perego, M., S. Price, and G. Stadler, 2014: Optimal initial conditions for coupling ice sheet models to Earth system models. Journal of Geophysical Research: Earth Surface, 119(9), 1894–1917, doi: 10.1002/2014jf003181.

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Russell, J.L. et al., 2018: Metrics for the Evaluation of the Southern Ocean in Coupled Climate Models and Earth System Models. Journal of Geophysical Research: Oceans, 123(5), 3120–3143, doi: 10.1002/2017jc013461.

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Sun, Q., M.M. Whitney, F.O. Bryan, and Y.- Tseng, 2017: A box model for representing estuarine physical processes in Earth system models. Ocean Modelling, 112, 139–153, doi: 10.1016/j.ocemod.2017.03.004.

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High winds associated with severe storms can destroy trees and houses, break plant stems and knock fruits, nuts and grains to the ground, with tolerance thresholds depending on crop species and developmental stage (Seidl et al., 2017; Lai, 2018; Elsner et al., 2019; Grotjahn, 2021). Severe storms particularly threaten energy infrastructure, with maximum wind speed associated with treefall and breaking of above-ground electrical transmission lines (Ward, 2013; Nik et al., 2020). The profile of heavy wind gusts is also required in the design of skyscrapers (C.-H. Wang et al., 2013) and bridges (Mondoro et al., 2018). Severe storms are difficult to simulate at the relatively coarse spatial scales of Earth system models, thus scientists often project changes by noting areas with increased convective available potential energy (CAPE) and strong low-level wind shear as these are conducive to tornado formation (Diffenbaugh et al., 2013; Tippett et al., 2016; Glazer et al., 2021).

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van den Hurk, B.J.J.M. et al., 2018: The match between climate services demands and Earth System Models supplies. Climate Services, 12, 59–63, doi: 10.1016/j.cliser.2018.11.002.

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There is medium confidence that climate change will reduce global fisheries’ productivity (Section 3.4.4.2.3), with more significant reductions in tropical and subtropical regions and gains in the poleward areas (Bindoff et al., 2019; Oremus et al., 2020). Through an ensemble of marine ecosystem models and Earth System Models, mean global animal biomass in the ocean has been estimated to decrease by 5% under the RCP2.6 emissions scenario and 17% under RCP8.5 by 2100, with an average decline of 5% for every 1°C of warming (Lotze et al., 2019), affecting food provision, revenue distribution, and potentially hindering the rebuilding of depleted fish stocks (Britten et al., 2017). The projected declining rates result in a 5.3–7% estimated global decrease in marine fish catch potential by 2050 (Cheung et al., 2019), particularly accentuated in tropical marine ecosystems and affecting many low-income countries (Barange and Cochrane, 2018; Bindoff et al., 2019; Cross-Chapter Box MOVING PLATE this chapter). Projections indicate that by 2060 the number of exclusive economic zones (EEZs) with new transboundary stocks will increase to 46 under strong mitigation RCP2.6, and up to 60 EEZs under the RCP8.5 GHG emissions scenario (Pinsky et al., 2018). Similarly, by combining six intercompared marine ecosystem models, Bryndum-Buchholz et al. (2019) projected that under the RCP8.5 scenario a total marine animal biomass decline of 15–30% would occur in the North and South Atlantic and Pacific and the Indian Ocean by 2100. In contrast, polar ocean basins would experience a 20–80% increase. In the eastern Bering Sea, simulations based on RCP8.5 predict declines of pollock (>70%) and cod (>35%) stocks by the end of the century (Holsman et al., 2020). Temperate tunas (albacore, Atlantic bluefin and southern bluefin) and the tropical bigeye tuna are expected to decline in the tropics and shift poleward by the end of the century under RCP8.5, while skipjack and yellowfin tunas are projected to increase abundance in tropical areas of the eastern Pacific but decrease in the equatorial western Pacific (medium confidence) (Erauskin-Extramiana et al., 2019). In the western and central Pacific, redistribution of tropical tuna due to climate change is projected to affect license revenues from purse seine fishing and shift more fishing into high seas areas (Bell et al., 2018; Table 15.5). For the east Atlantic, observational evidence indicates that not only will tuna distribution change with temperature anomalies, but also fishing effort distribution (Rubio et al., 2020a). There is medium confidence that climate change will create new fishing opportunities when exploited fish stocks shift their distribution into new fishing regions in enclosed seas, such as the Mediterranean and the Black Sea (Hidalgo et al., 2018; Pinsky et al., 2018). However, in general, where land barriers constrain the latitudinal shifts, the expected impacts of climate change are population declines and reduced productivity (high confidence) (Oxenford and Monnereau, 2018). Besides direct impacts on the abundance of fisheries-targeted species, climate-change-induced proliferation of invasive species could also affect fisheries’ productivity (low confidence) (Mellin et al., 2016; Goldsmith et al., 2019).

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Shifting marine fisheries will affect national economies (high confidence) (Bindoff et al., 2019). It has been suggested that, without government subsides, fishing is already non-profitable in 54% of the international waters (Sala et al., 2018). Projections are that fishing maximum revenue potential from landed catches will decrease further by 10.4% (±4.2%) by 2050 relative to 2000 under RCP8.5, close to 35% greater than the decrease projected for the global maximum catch potential (7.7±4.4%); (Lam et al., 2016). The global revenue potential loss for that period ranges from USD 6 to 15 billion (depending on the model), but impacts may be amplified at the regional scale for fisheries-dependent and low-income countries. The maximum revenue potential percentage decrease in the EEZ under RCP8.5 is estimated to be over 2.3 times larger than that of the high seas (Lam et al., 2016). Ocean acidification is also expected to drive large global economic impacts (medium confidence) (Cooley et al., 2015; Fernandes et al., 2017; Macko et al., 2017; Hansel et al., 2020), and there is high confidence that the integrated economic consequences of all interacting climate change-related factors would result in even larger losses. Changes in the frequency and intensity of extreme events will also alter marine ecosystems and productivity. Marine heatwaves can lead to severe and persistent impacts, from mass mortality of benthic communities to decline in fisheries catch (IPCC, 2021, Box 9.2). These events have very likely doubled in frequency between 1982 and 2016 and have also become more intense and longer (Smale et al., 2019; Laufkotter et al., 2020); for all future scenarios Earth System Models project even more frequent, intense and longer-lasting marine heatwaves (Eyring et al., 2021; IPCC, 2021, Box 9.2).

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Kloster, S. and G. Lasslop, 2017: Historical and future fire occurrence (1850 to 2100) simulated in CMIP5 Earth System Models. Global and Planetary Change, 150, 58–69, doi:10.1016/j.gloplacha.2016.12.017.

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Mean open-ocean surface pH is projected to decline by 0.08 ± 0.003 (very likely range), 0.17 ± 0.003, 0.27 ± 0.005 and 0.37 ± 0.007 pH units in 2081–2100 relative to 1995–2014, for SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5, respectively (Figure 3.5; WGI AR6 Section 4.3.2; Kwiatkowski et al., 2020; Lee et al., 2021). Projected changes in surface pH are relatively uniform in contrast with those of other surface-ocean variables, but they are largest in the Arctic Ocean (Figure 3.6; WGI AR6 Section 5.3.4.1; Canadell et al., 2021). Similar declines in the concentration of carbonate ions are projected by Earth system models (ESMs; Bopp et al., 2013; Gattuso et al., 2015; Kwiatkowski et al., 2020). The North Pacific, the Southern Ocean and Arctic Ocean regions will become undersaturated for calcium carbonate minerals first (Orr et al., 2005; Pörtner et al., 2014). Concurrent impacts on the seasonal amplitude of carbonate chemistry variables are anticipated (i.e., increased amplitude for pCO2 and hydrogen ions, decreased amplitude for carbonate ions; McNeil and Sasse, 2016; Kwiatkowski and Orr, 2018; Kwiatkowski et al., 2020).

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Earth system models project distinct regional evolutions of the different CIDs over the 21st century (very high confidence) (Figures 3.5, 3.6, 3.7; Kwiatkowski et al., 2020). Tropical and subtropical oceans are characterised by projected warming and acidification, accompanied by declining nitrate concentrations in equatorial upwelling regions. The North Atlantic is characterised by a high exposure to acidification and declining nitrate concentrations. The North Pacific is characterised by high sensitivity to compound changes, with high rates of warming, acidification, deoxygenation and nutrient depletion. In contrast, the development of compound hazards is limited in the Southern Ocean, where rates of warming and nutrient depletion are lower. The Arctic Ocean is characterised by the highest rates of acidification and warming, strong nutrient depletion, and it will likely become practically sea ice free in the September mean for the first time before the year 2050 in all SSP scenarios (high confidence) (Figures 3.5, 3.6, 3.7; Sections 3.2.2, 3.2.3).

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Bopp, L., et al., 2017: Ocean (de)oxygenation from the last glacial maximum to the twenty-first century: insights from Earth system models. Philos. Trans. Royal Soc. A Math. Phys. Eng. Sci. , 375 (2102), 20160323, doi:10.1098/rsta.2016.0323.

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Wrightson, L. and A. Tagliabue, 2020: Quantifying the impact of climate change on marine diazotrophy: insights from Earth system models. Front. Mar. Sci. , 7, 635, doi:10.3389/fmars.2020.00635.

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Meehl, G.A., et al., 2020: Context for interpreting equilibrium climate sensitivity and transient climate response from the CMIP6 Earth system models. Sci. Adv. , 6 (26), eaba1981, doi:10.1126/sciadv.aba1981.

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Expertise and resources for using tools and approaches for integrated risk management vary between the developed and developing countries (high confidence) (e.g., Section 4.7.2). Exploration of adaptation scenarios can be derived from Earth System Models (high confidence) (e.g., Sections 4.7.1.2, 11.7.3.1). However, the feasibility of possible adaptations and the degree to which they are likely to be effective (Box 17.1) will require further exploration as success will depend on appropriate enabling conditions, including institutional support and capacity, available financial resources and knowledge, and suitable conditions for stakeholder participation (high confidence) (Section 17.4). The current levels of uncertainty surrounding the effectiveness of many adaptation options (Section 17.5.2; Cross-Chapter Box PROGRESS in this Chapter) means that decision-making approaches applicable to deep uncertainty (Cross-Chapter Box DEEP in this Chapter; Section 17.3.1) will apply in many if not most cases (medium confidence). An early step in identifying suitable integrated pathways for managing climate risks, establishing ‘no regrets’ anticipatory options in a timely manner, and avoiding path dependencies is to jointly map the steps for adapting to sectoral risks and determine suitable ways to avoid maladaptations arising (high confidence) (Section 17.3.1, Cross-Working Group Box URBAN in Chapter 6 and Cross-Chapter Boxes DEEP in this Chapter). The application of Dynamic Adaptive Pathway planning has been successfully used in this way in Australasia (Section 11.7.3) and Europe (Sections 13.6.2.2, 13.10.2) (Lawrence et al., 2019 a; Haasnoot et al., 2020a). Current experience suggests that synergies between sectors can save resources and effort (limited evidence) (Section 13.11.2). Iterative processes can then enhance adaptation programmes by including more detailed modelling, and updated knowledge as the experience is acquired (Section 17.3.1).

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Jones, C., et al., 2013: Twenty-first-century compatible CO2 emissions and airborne fraction simulated by CMIP5 earth system models under four representative concentration pathways. J. Clim. , 26 (13), 4398–4413.

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Tompkins, A.M. and L. Caporaso, 2016a: Assessment of malaria transmission changes in Africa, due to the climate impact of land use change using Coupled Model Intercomparison Project Phase 5 earth system models. Geospat. Health, 11 (1 Suppl), 380, doi:10.4081/gh.2016.380.

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Tompkins, A.M. and L. Caporaso, 2016b: Assessment of malaria transmission changes in Africa, due to the climate impact of land use change using Coupled Model Intercomparison Project Phase 5 earth system models. Geospat. Health, 11, doi:10.4081/gh.2016.380.

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Tompkins, A. M. and L. Caporaso, 2016: Assessment of malaria transmission changes in Africa, due to the climate impact of land use change using Coupled Model Intercomparison Project Phase 5 earth system models. Geospat. Health, 11 (1 Suppl), 380, doi:10.4081/gh.2016.380.

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Pongratz, J. et al., 2018: Models meet data: Challenges and opportunities in implementing land management in Earth system models. Glob. Change Biol. , 24(4) , 1470–1487, doi:10.1111/gcb.13988.

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The IPCC Special Report on Climate Change and Land (SRCCL) expanded beyond the 1.5°C report to provide more in-depth information on climate change interactions with food security, desertification and degradation. There was high confidence that climate risks, both for slow changes and extreme events, are interlinked with ecosystem services, health and food security, often cascading and potentially reinforcing effects. Climate change already affects all dimensions of food security, namely availability, access, utilisation and stability, by disrupting food production, quality, storage, transport and retail. These effects exacerbate competition for land and water resources, leading to increased deforestation, biodiversity reduction and loss of wetlands. With high certainty, limiting global warming would lower future risks related to land, such as water scarcity, fire, vegetation shifts, degradation, desertification and food insecurity and malnutrition, particularly for those most vulnerable today: small-scale food producers in low-income countries, Indigenous communities, women, and the urban poor. SRCCL assessed a range of adaptation pathways to increase food resilience.

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Species diversity and ecosystem function influence each other reciprocally, while the latter forms the necessary basis for ecosystem services (Hooper et al., 2012; Mokany et al., 2016). Drivers of impacts on biodiversity, ecosystem function and ecosystem services have been assessed in reports by the IPCC, the Food and Agriculture Organization (FAO), the Intergovernmental Platform on Biodiversity and Ecosystem Services (IPBES) and the Global Environmental Outlook (Settele et al., 2014; FAO, 2018; IPBES, 2018b; IPBES, 2018e; IPBES, 2018c; IPBES, 2018d; IPBES, 2019; UNEP, 2019; Secretariat of the Convention on Biological Diversity, 2020). Most recently, the IPCC Special Report on Climate Change and Land (SRCCL) provided an assessment on land degradation and desertification, GHG emissions and food security in the context of global warming (IPCC, 2019c), and the IPBES–IPCC joint report on biodiversity and climate change provided a synthesis of the current understanding of the interactions, synergies and feedbacks between biodiversity and climate change (Pörtner et al., 2021). This chapter builds on and expands the results of these assessments.

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The AR6 IPCC Special Report on Climate Change and Land (SRCCL) (IPCC, 2019b) added the dimensions of pace, intensity and scale of climate impacts and adaptation or mitigation responses and adverse consequences. Relevant land-based adverse consequences include those on lives, livelihoods, health and well-being, economic, social and cultural assets and investments, infrastructure, services (including ecosystem services), ecosystems and species.

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The IPCC Special Report on Climate Change and Land (IPCC, 2019a) states that agriculture, food production and deforestation are major drivers of climate change and calls for coordinated action to tackle climate change that can simultaneously improve land, food security and nutrition, and help to end hunger. There are five land challenges identified including climate change mitigation, adaptation, desertification, land degradation and food security. This report identified three major categories of climate response options that show promise for achieving mitigation and increasing capacity for adaptation while addressing poverty: SLM options, value chain management and risk management options (IPCC, 2019a). For example, programmes supporting no-till agriculture and residue retention allow small-scale farmers to participate in mitigation and adaptation activities, with long-term benefits to soil health and food, energy and water security (Wright et al., 2014). Likewise, the installation of a solar powered drip irrigation system simultaneously reduces emission, improves water security and increases farmers’ income (Locatelli et al., 2015). Response options in terms of SLM options, and value chain and risk management involve interlinkages between land-based climate strategies, synergies and trade-offs (see Chapter 6). On the other hand, a key trade-off is the potential for maladaptation, where one adaptation intervention at one time, location or sector could increase the vulnerability at another time, location or sector, or increase the vulnerability of the target group to future climate change (medium evidence, high agreement ) (Eriksen et al., 2011). A cause of increasing concern to adaptation planners is the understanding of maladaptation has changed subtly to recognise that it arises inadvertently, from poorly planned adaptation actions, but also from carefully deliberated decisions where wider considerations place greater emphasis on singular or short-term outcomes ahead of broader, longer-term threats, or discount, or fail to consider, the full range of interactions arising from the planned actions across scales (Eriksen et al., 2021). Research identifies the challenge of avoiding maladaptation as one of reducing long-term structural vulnerability. Accordingly, one can consider CCD and maladaptation as two sides of the same coin. Scholars of ‘sustainable adaptation’ define it as adaptation that contributes to socially and environmentally sustainable development pathways, which takes into account both social justice and environmental integrity (Eriksen et al., 2011). The parallels in maladaptation include the underlying drivers of vulnerability, namely socio-environmental processes such as conflict, marginalisation, economic restructuring, exploitation, institutional fragility and so forth (Antwi-Agyei et al., 2018b; Neef et al., 2018).

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The AR4 was the first IPCC report to explicitly discuss the value of IK and LK in adaptation and mitigation processes. AR5 recognised the importance of creating synergies across disciplines in the production of knowledge, acknowledging the importance of ‘non-scientific’ sources such as IK, which may not follow discipline conventions but nevertheless reflects the outcomes of learning across generations (Burkett et al., 2014). This also explains the importance of including IK and LK and diverse stakeholder interests and values in local decision making processes (Jones et al., 2014). Such processes should be done in partnership with IK and LK knowledge holders and, when possible, be led by them (Inuit Tapiriit Kanatami, 2018). Recent IPCC reports have included distinct sections dedicated to IK and LK (e.g., IPCC, 2019b). The IPCC Special Report on Climate Change and Land (SRCCL) includes a section on ‘Local and Indigenous knowledge for addressing land degradation’ (2019a) and the IPCC Special Report on Ocean and Cryosphere (SROCC) describes LK as ‘what non-Indigenous communities, both rural and urban, use on a daily and lifelong basis,’ a type of knowledge which is recognised as ‘multi-generational, embedded in community practices and cultures, and adaptive to changing conditions’ (2019b). The IPCC Special Report on Global Warming of 1.5°C emphasised the high vulnerability of Indigenous Peoples to climate change. It stated that disadvantaged and vulnerable populations, including Indigenous Peoples and certain local communities, are at disproportionately higher risk of suffering adverse consequences with global warming of 1.5°C and beyond (IPCC, 2018b). The report also assessed evidence in relation to the importance of including IK and LK in adaptation options, explaining their role in early warning systems and arguing that they are part of a range of approaches to catalyse wide-scale values and are consistent with adapting to and limiting global warming to 1.5°C (IPCC, 2018b).

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Previous assessments have consistently recognised this linkage. The Paris Agreement includes mitigation and adaptation as key areas of action, through NDCs and communicating adaptation actions and plans. The Agreement explicitly recognises that mitigation co-benefits resulting from adaptation can count towards NDC targets. The IPCC Fifth Assessment Report (IPCC 2014) emphasised that sustainable development is helpful in going beyond a narrow focus on separate mitigation and adaptation options and their specific co-benefits. The IPCC Special Report on climate change and land addresses GHG emissions from land-based ecosystems with a focus on the vulnerability of land-based systems to climate change. The report identifies the potential of changes to land use and land management practices to mitigate and adapt to climate change, and to generate co-benefits that help meet other SDGs (Jian et al. 2019).

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The IPCC Special Report on Climate Change and Land (IPCC 2019) emphasises the need for governance in order to avoid conflict between sustainable development and land-use management. It states: ‘Measuring progress towards goals is important in decision-making and adaptive governance to create common understanding and advance policy effectiveness’. The report concludes that measurable indicators are very useful in linking land-use policies, the NDCs and the SDGs.

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Cities are also gaining traction within the work of the IPCC. The IPCC Special Report on Global Warming of 1.5°C (SR1.5 Chapter 4) identified four systems that urgently need to change in fundamental and transformative ways: urban infrastructure, land use and ecosystems, industry, and energy. Urban infrastructure was singled out but urban systems form a pivotal part of the other three systems requiring change (IPCC 2018a) (see ‘infrastructure’ in Glossary). The IPCC Special Report on Climate Change and Land (SRCCL) identified cities not only as spatial units for land-based mitigation options but also places for managing demand for natural resources including food, fibre, and water (IPCC 2019).

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This section complements Chapter 7 by reviewing recent estimates of food system emissions and assessing options beyond the agriculture, forestry and land use sectors to mitigate food systems GHG emissions. A food system approach enables identification of cross-sectoral mitigation opportunities including both technological and behavioural options. Further, a system approach permits evaluation of policies that do not necessarily directly target primary producers or consumers, but other food system actors, with possibly higher mitigation efficiency. A food system approach was introduced in the IPCC Special Report on Climate Change and Land (SRCCL) (Mbow et al. 2019). Besides major knowledge gaps in the quantification of food system GHG emissions (Section 12.4.2), the SRCCL authors identified as major knowledge gaps the understanding of the dynamics of dietary change (including behavioural patterns, the adoption of plant-based dietary patterns, and interaction with human health and nutrition of sustainable healthy diets and associated feedbacks); and instruments and mechanisms to accelerate transitions towards sustainable and healthy food systems.

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The Paleocene–Eocene Thermal Maximum (PETM) was an episode of global warming exceeding pre-industrial temperatures by 4°C–8°C (McInerney and Wing, 2011; Dunkley Jones et al., 2013) that occurred 55.9–55.7 Ma. The PETM involved a large pulse of geologic CO2 released into the ocean–atmosphere system in 3–20 kyr (Zeebe et al., 2016; Gutjahr et al., 2017; Kirtland Turner et al., 2017; Kirtland Turner, 2018; Gingerich, 2019; Section 5.2.1.1). In response, observationally constrained model simulations report an increase in atmospheric CO2 concentrations ranging from about 900 ppm to >2000 ppm (Chapter 2; Gutjahr et al., 2017; Cui and Schubert, 2018; Anagnostou et al., 2020). The PETM thus provides a test for our understanding of the ocean’s response to the increase in carbon (and heat) emissions over geologically short time scales.

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Ajayi, S. et al., 2020: Evaluation of Paleocene–Eocene Thermal Maximum Carbon Isotope Record Completeness – An Illustration of the Potential of Dynamic Time Warping in Aligning Paleo-Proxy Records. Geochemistry, Geophysics, Geosystems, 21(3), e2019GC008620, doi: 10.1029/2019gc008620.

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Dickson, A.J., A.S. Cohen, and A.L. Coe, 2012: Seawater oxygenation during the Paleocene–Eocene Thermal Maximum. Geology, 40(7), 639–642, doi: 10.1130/g32977.1.

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Dickson, A.J. et al., 2014: The spread of marine anoxia on the northern Tethys margin during the Paleocene–Eocene Thermal Maximum. Paleoceanography, 29(6), 471–488, doi: 10.1002/2014pa002629.

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Dunkley Jones, T. et al., 2013: Climate model and proxy data constraints on ocean warming across the Paleocene–Eocene Thermal Maximum. Earth-Science Reviews, 125, 123–145, doi: 10.1016/j.earscirev.2013.07.004.

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Haynes, L.L. and B. Hönisch, 2020: The seawater carbon inventory at the Paleocene–Eocene Thermal Maximum. Proceedings of the National Academy of Sciences, 117(39), 24088–24095, doi: 10.1073/pnas.2003197117.

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Jones, S.M., M. Hoggett, S.E. Greene, and T. Dunkley Jones, 2019: Large Igneous Province thermogenic greenhouse gas flux could have initiated Paleocene–Eocene Thermal Maximum climate change. Nature Communications, 10(1), 5547, doi: 10.1038/s41467-019-12957-1.

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Kirtland Turner, S., 2018: Constraints on the onset duration of the Paleocene–Eocene Thermal Maximum. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2130), 20170082, doi: 10.1098/rsta.2017.0082.

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Penman, D.E., B. Hönisch, R.E. Zeebe, E. Thomas, and J.C. Zachos, 2014: Rapid and sustained surface ocean acidification during the Paleocene–Eocene Thermal Maximum. Paleoceanography, 29(5), 357–369, doi: 10.1002/2014pa002621.

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Winguth, A.M.E., E. Thomas, and C. Winguth, 2012: Global decline in ocean ventilation, oxygenation, and productivity during the Paleocene–Eocene Thermal Maximum: Implications for the benthic extinction. Geology, 40(3), 263–266, doi: 10.1130/g32529.1.

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Yao, W., A. Paytan, and U.G. Wortmann, 2018: Large-scale ocean deoxygenation during the Paleocene–Eocene Thermal Maximum. Science, 361(6404), 804–806, doi: 10.1126/science.aar8658.

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Zachos, J.C. et al., 2005: Rapid Acidification of the Ocean During the Paleocene–Eocene Thermal Maximum. Science, 308(5728), 1611–1615, doi: 10.1126/science.1109004.

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Carmichael, M.J. et al., 2017: Hydrological and associated biogeochemical consequences of rapid global warming during the Paleocene–Eocene Thermal Maximum. Global and Planetary Change, 157, 114–138, doi: 10.1016/j.gloplacha.2017.07.014.

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Harper, D.T. et al., 2020: The Magnitude of Surface Ocean Acidification and Carbon Release During Eocene Thermal Maximum 2 (ETM-2) and the Paleocene–Eocene Thermal Maximum (PETM). Paleoceanography and Paleoclimatology, 35(2), e2019PA003699, doi: 10.1029/2019pa003699.

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Kirtland Turner, S., 2018: Constraints on the onset duration of the Paleocene–Eocene Thermal Maximum. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2130), 20170082, doi: 10.1098/rsta.2017.0082.

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Paleoclimate proxy evidence from past high-CO2 time periods much warmer than present (the early Eocene and Paleocene–Eocene Thermal Maximum, PETM; Cross-Chapter Box 2.1) show that the feedback parameter increases as temperature increases (Anagnostou et al., 2016, 2020; Shaffer et al., 2016). However, such temperature-dependence of feedbacks was not found in the warm Pliocene relative to the cooler Pleistocene (Martínez-Botí et al., 2015), although the temperature changes are relatively small at this time, making temperature-dependence challenging to detect given the uncertainties in reconstructing global mean temperature and forcing. Overall, the paleoclimate proxy record provides medium confidence that the net feedback parameter, α , was less negative in these past warm periods than in the present day.

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There has long been a consensus (Charney et al., 1979) supporting an ECS estimate of 1.5°C–4.5°C. In this regard it is worth remembering the many debates challenging an ECS of this magnitude. These started as early as Ångström (1900) criticizing the results of Arrhenius (1896) arguing that the atmosphere was already saturated in infrared absorption such that adding more CO2 would not lead to warming. The assertion of Ångström was understood half a century later to be incorrect. History has seen a multitude of studies (e.g., Svensmark, 1998; Lindzen et al., 2001; Schwartz, 2007) mostly implying lower ECS than the range assessed as very likely here. However, there are also examples of the opposite, such as very large ECS estimates based on the Pleistocene records (Snyder, 2016), which have been shown to be overestimated due to a lack of accounting for orbital forcing and long-term ice-sheet feedbacks (Schmidt et al., 2017b), or suggestions that global climate instabilities may occur in the future (Steffen et al., 2018; Schneider et al., 2019). There is, however, no evidence for unforced instabilities of such magnitude occurring in the paleo-record temperatures of the past 65 million years (Westerhold et al., 2020), possibly short of the Paleocene–Eocene Thermal Maximum (PETM) excursion (Section 5.3.1.1) that occurred at more than 10°C above present-day levels (Anagnostou et al., 2020). Looking back, the resulting debates have led to a deeper understanding, strengthened the consensus, and have been scientifically valuable.

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Inglis, G.N. et al., 2020: Global mean surface temperature and climate sensitivity of the early Eocene Climatic Optimum (EECO), Paleocene–Eocene Thermal Maximum (PETM), and latest Paleocene. Climate of the Past, 16(5), 1953–1968, doi: 10.5194/cp-16-1953-2020.

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Winguth, A., C. Shellito, C. Shields, and C. Winguth, 2010: Climate Response at the Paleocene–Eocene Thermal Maximum to Greenhouse Gas Forcing – A Model Study with CCSM3. Journal of Climate, 23(10), 2562–2584, doi: 10.1175/2009jcli3113.1.

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The most recent interval characterized by atmospheric CO2 levels similar to modern (i.e., 360–420 ppm) was the mid-Pliocene Warm Period (MPWP, 3.3–3.0 Ma; Martínez-Botí et al., 2015; de la Vega et al., 2020) (Chapter 2). The relatively high atmospheric CO2 concentration during the MPWP are related to vigorous ocean circulation and a rather inefficient marine biological carbon pump (Burls et al., 2017), which would have reduced deep ocean carbon storage. After the MPWP, atmospheric CO2 concentrations declined gradually at a rate of 30 ppm Myr–1 (Figure 5.3; de la Vega et al., 2020), as an increase in ocean stratification led to enhanced ocean carbon storage, allowing for major, sustained advances in Northern Hemisphere ice sheets, 2.7 Ma (Sigman et al., 2004; DeConto et al., 2008).

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GMST estimated for each of the reference periods based on proxy evidence (Section 2.3.1.1) can be compared with climate projections over coming centuries to place the range of possible futures into a longer-term context (Cross-Chapter Box 2.1, Figure 1). Here, the very likely range of GMST for the warmer world reference periods are compared with the very likely range of GSAT projected for the end the 21st century (2080–2100; Table 4.5) and the likely range for the end of the 23rd century (2300; Table 4.9) under multiple Shared Socio-economic Pathway (SSP) scenarios. From this comparison, there is medium confidence in the following: GMST estimated for the warmest long-term period of the Last Interglacial about 125 ka (125,000 years ago; 0.5°C–1.5°C relative to 1850–1900) overlaps with the low end of the range of temperatures projected under SSP1-2.6 including its negative emissions extension to the end of the 23rd century (1.0°Cto 2.2°C). GMST estimated for a period of prolonged warmth during the mid-Pliocene Warm Period about 3 Ma [2.5°C to 4.0°C] is similar to temperatures projected under SSP2-4.5 for the end of the 23rd century (2.3°C to 4.6°C). GMST estimated for the Miocene Climatic Optimum [5°C to 10°C] and Early Eocene Climatic Optimum [10°C to 18°C], about 15 and 50 Ma, respectively, overlap with the range projected for the end of the 23rd century under SSP5-8.5 (6.6°C to 14.1°C).

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The Pliocene Epoch is one of the best-documented examples of a warmer world during which the slow responding components of the climate system were approximately in balance with concentrations of atmospheric CO2, similar to present (e.g., Haywood et al., 2016). It provides a means to constrain Earth’s equilibrium climate sensitivity (Section 7.5.3) and to assess climate model simulations (Section 7.4.4.1.2). During the Pliocene, continental configurations were similar to present (Cross-Chapter Box 2.4 Figure 1a), and many plant and animal species living then also exist today. These similarities increase reliability of paleo-environmental reconstructions compared with those for older geological periods. Within the well-studied mid-Pliocene Warm Period (MPWP, also called the mid-Piacenzian Warm Period, 3.3–3.0 Ma), the interglacial period KM5c (3.212–3.187 Ma) has become a focus of research because its orbital configuration, and therefore insolation forcing, was similar to present (global mean insolation = –0.022 W m–2 relative to modern; Haywood et al., 2013), allowing for the climatic state associated with relatively high atmospheric CO2 to be assessed with fewer confounding variables.

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Haywood, A.M., H.J. Dowsett, and A.M. Dolan, 2016: Integrating geological archives and climate models for the mid-Pliocene warm period. Nature Communications, 7, 10646, doi: 10.1038/ncomms10646.

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Huang, X. et al., 2019a: Northwestward Migration of the Northern Edge of the East Asian Summer Monsoon During the Mid-Pliocene Warm Period: Simulations and Reconstructions. Journal of Geophysical Research: Atmospheres, 124(3), 1392–1404, doi: 10.1029/2018jd028995.

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Pontes, G.M. et al., 2020: Drier tropical and subtropical Southern Hemisphere in the mid-Pliocene Warm Period. Scientific Reports, 10(1), 13458, doi: 10.1038/s41598-020-68884-5.

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Yan, Q. et al., 2016: Enhanced intensity of global tropical cyclones during the mid-Pliocene warm period. Proceedings of the National Academy of Sciences, 113(46), 12963–12967, doi: 10.1073/pnas.1608950113.

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In addition to the multivariate assessments of simulations of the recent historical period, simulations of selected periods of the Earth’s more distant history can be used to benchmark climate models by exposing them to climate forcings that are radically different from the present and recent past (Harrison et al., 2015, 2016; Kageyama et al., 2018; Tierney et al., 2020a). These time periods provide an out-of-sample test of models because they are not in general used in the process of model development. They encompass a range of climate drivers, such as volcanic and solar forcing for the Last Millennium, orbital forcing for the mid-Holocene and Last Interglacial, and changes in greenhouse gases and ice sheets for the LGM, mid-Pliocene Warm Period, and early Eocene (Sections 2.2 and 2.3). These drivers led to climate changes, including in surface temperature (Section 2.3.1.1) and the hydrological cycle (Section 2.3.1.3.1), which are described by paleoclimate proxies that have been synthesized to support evaluations of models on a global and regional scale. However, the more sparse, indirect, and regionally incomplete climate information available from paleo-archives motivates a different form of the multivariate analysis of simulations covering these periods versus the equivalent for the historical period, as described below.

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AR5 found that reconstructions and simulations of past climates both show similar responses in terms of large-scale patterns of climate change, such as polar amplification (Flato et al., 2013; Masson-Delmotte et al., 2013). However, for several regional signals (e.g., the north–south temperature gradient in Europe and regional precipitation changes), the magnitude of change seen in the proxies relative to the pre-industrial period was underestimated by the models. When benchmarking CMIP5/PMIP3 models against reconstructions of the mid-Holocene and LGM, AR5 found only a slight improvement compared with earlier model versions across a range of variables. For the Last Interglacial, it was noted that the magnitude of observed annual mean warming in the Northern Hemisphere was only reached in summer in the models. For the mid-Pliocene Warm Period, it was noted that both proxies and models showed a polar amplification of temperature compared with the pre-industrial period, but a formal model evaluation was not carried out.

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Polar amplification in the LGM, mid-Pliocene Warm Period, and Early Eocene Climatic Optimum (EECO) simulations is assessed in Section 7.4.4.1.2. Here we focus on the mid-Holocene and the LGM, which have been a part of AMIP or CMIP through several assessment cycles, and as such serve as a reference to quantify regional model-data agreement from one IPCC assessment to another. We compare the results from 15 CMIP6 models using the PMIP4 protocol (CMIP6-PMIP4), with non-CMIP6 models using the PMIP4 protocol, with PMIP3 models, and with regional temperature and precipitation changes from proxies for the mid-Holocene (Figure 3.44b). For six out of seven variables shown, the CMIP6 multi-model mean captures the correct sign of the change. For five out of seven of them the CMIP6 ensemble mean is within the reconstructed range. For the other two variables (changes in the mean temperature of the warmest month over North America and in the mean annual precipitation over West Africa) nearly all PMIP4 and PMIP3 models are outside the reconstructed range. CMIP6 models show regional patterns of temperature changes similar to the PMIP3 ensemble (Brierley et al., 2020), but the slight mid-Holocene cooling in PMIP4 compared with PMIP3, probably associated with lower imposed mid-Holocene carbon dioxide concentrations (Otto-Bliesner et al., 2017), improves the regional model performance for summer and winter temperatures (Figure 3.44b). However, this cooling also results in a CMIP6 mid-Holocene GSAT that lies further from the assessed range (Figure 3.44a). All models show an expansion of the monsoon areas from the pre-industrial to the mid-Holocene simulations in the Northern Hemisphere, but this expansion in some cases is only large enough to cancel out the bias in the pre-industrial control simulations (Section 3.3.3.2; Brierley et al., 2020). There is a slight improvement in representing the northward expansion of the West African monsoon region in PMIP4 compared with PMIP3 (Figures 3.11 and 3.44b).

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Paleoclimate proxy data provide observational evidence of large-scale patterns of surface warming in response to past forcings, and allow an evaluation of the modelled response to these forcings (Sections 3.3.1.1 and 3.8.2.1). In particular, paleoclimate data provide evidence for long-term changes in polar amplification during time periods in which the primary forcing was a change in atmospheric CO2, although data sparsity means that for some time periods this evidence may be limited to a single hemisphere or ocean basin, or the evidence may come primarily from the mid-latitudes as opposed to the polar regions. In this context, there has been a modelling and data focus on the Last Glacial Maximum (LGM) in the context of PMIP4 (Cleator et al., 2020; Tierney et al., 2020b; Kageyama et al., 2021), the mid-Pliocene Warm Period (MPWP) in the context of PlioMIP2 (Cross-Chapter Box 2.4; Salzmann et al., 2013; Haywood et al., 2020; McClymont et al., 2020), the Early Eocene Climatic Optimum (EECO) in the context of DeepMIP (Hollis et al., 2019; Lunt et al., 2021), and there is growing interest in the Miocene (Goldner et al., 2014b; Steinthorsdottir et al., 2021; for definitions of time periods see Cross-Chapter Box 2.1). For all these time periods, in addition to the CO2 forcing there are long-term feedbacks associated with ice sheets (Section 7.4.2.6), and in particular for the Early Eocene there is a forcing associated with paleogeographic change (Farnsworth et al., 2019). However, because these non-CO2 effects can all be included as boundary conditions in model simulations, these time periods allow an assessment of the patterns of modelled response to known forcing (although uncertainty in the forcing increases further back in time). Because these changes to boundary conditions can be complex to implement in models, and because long simulations (typically longer than 500 years) are required to approach equilibrium, these simulations have been carried out mostly by pre-CMIP6 models, with relatively few (or none for the Early Eocene) fully coupled CMIP6 models in the ensembles.

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The AR5 stated that paleoclimate proxies indicate a reduction in the longitudinal SST gradient across the equatorial Pacific during the Mid-Pliocene Warm Period (MPWP; Masson-Delmotte et al., 2013; see Cross-Chapter Box 2.1 and Cross-Chapter Box 2.4 in this Report). This assessment was based on SST reconstructions between two sites situated very close to the equator in the heart of the western Pacific warm pool and eastern Pacific cold tongue, respectively. Multiple SST reconstructions based on independent paleoclimate proxies generally agreed that during the Pliocene the SST gradient between these two sites was reduced compared with the modern long-term mean (Wara et al., 2005; Dekens et al., 2008; Fedorov et al., 2013).

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In the pre-Quaternary (prior to about 2.5 million years ago), the forcings and response are generally of the same sign and similar magnitude as future projections of climate change (Burke et al., 2018; Tierney et al., 2020a). Similar uncertainties as for the LGM apply, but in this case a major uncertainty relates to the forcing, because prior to the ice-core record there are only indirect estimates of CO2 concentration. However, advances in pre-ice-core CO2 reconstruction (e.g., Foster and Rae, 2016; Super et al., 2018; Witkowski et al., 2018) mean that the estimates of pre-Quaternary CO2 have less uncertainty than at the time of AR5, and these time periods can now contribute to an assessment of climate sensitivity (Table 7.11). The mid-Pliocene Warm Period (MPWP; Cross-Chapter Box 2.1 and Cross-Chapter Box 2.4) has been targeted for constraints on ECS (Martínez-Botí et al., 2015; Sherwood et al., 2020), due to the fact that CO2 concentrations were relatively high at this time (350–425 ppm) and because the MPWP is sufficiently recent that topography and continental configuration are similar to modern-day. As such, a comparison of the MPWP with the pre-industrial climate provides probably the closest natural geological analogue for the modern day that is useful for assessing constraints on ECS, despite the effects of different geographies not being negligible (global surface temperature patterns; ocean circulation). Furthermore, the global surface temperature of the MPWP was such that non-linearities in feedbacks (Section 7.4.3) were relatively modest. Within the MPWP, the KM5c interglacial has been identified as a particularly useful time period for assessing ECS (Haywood et al., 2013, 2016b) because Earth’s orbit during that time was very similar to that of the modern day.

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Perhaps the simplest class of emergent constraints regress past equilibrium paleoclimate temperature change against modelled ECS to obtain a relationship that can be used to translate a past climate change to ECS. The advantage is that these are constraints on the sum of all feedbacks, and furthermore unlike constraints on the instrumental record they are based on climate states that are at, or close to, equilibrium. So far, these emergent constraints have been limited to the Last Glacial Maximum (LGM; Cross-Chapter Box 2.1) cooling (Hargreaves et al., 2012; Schmidt et al., 2014; Renoult et al., 2020) and warming in the mid-Pliocene Warm Period (MPWP; Cross-Chapter Box 2.1 and Cross-Chapter Box 2.4; Hargreaves and Annan, 2016; Renoult et al., 2020) due to the availability of sufficiently large multi-model ensembles for these two cases. The paleoclimate emergent constraints are limited by structural uncertainties in the proxy-based global surface temperature and forcing reconstructions (Section 7.5.3), possible differences in equilibrium sea surface temperature patterns between models and the real world, and a small number of model simulations participating, which has led to divergent results. For example, Hopcroft and Valdes (2015) repeated the study based on the LGM by Hargreaves et al. (2012) using another model ensemble and found that the emergent constraint was not robust, whereas studies using multiple available ensembles retain useful constraints (Schmidt et al., 2014; Renoult et al., 2020). Also, the results are somewhat dependent on the applied statistical methods (Hargreaves and Annan, 2016). However, Renoult et al. (2020) explored this and found 95th percentiles of ECS below 6°C for LGM and Pliocene individually, regardless of statistical approach, and by combining the two estimates the 95th percentile dropped to 4.0°C. The consistency between the cold LGM and warm MPWP emergent constraint estimates increases confidence in these estimates, and further suggests that the dependence of feedback on climate mean state (Section 7.4.3) as represented in PMIP models used in these studies is reasonable.

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Haywood, A.M., H.J. Dowsett, and A.M. Dolan, 2016a: Integrating geological archives and climate models for the mid-Pliocene warm period. Nature Communications, 7(1), 10646, doi: 10.1038/ncomms10646.

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During the mid-Pliocene Warm Period (MPWP), GMST was 2.5°C–4°C warmer than 1850–1900 (medium confidence) and GMSL was between 5 and 25 m higher than today (medium confidence) (Table 9.6 and Section 2.3.3.3). The AR5 (Masson-Delmotte et al., 2013) concluded that ice-sheet models consistently produce near-complete deglaciation of the Greenland and West Antarctic ice sheets, and multi-meter loss of the East Antarctic Ice Sheet (EAIS) in response to MPWP climate conditions. Studies since AR5 have yielded a consistent but broader range, due in part to larger ensembles exploring more parameters (DeConto and Pollard, 2016; Yan et al., 2016; DeConto et al., 2021). Partly on the basis of these studies, SROCC proposed a ‘plausible’ upper bound on GMSL of 25 m (low confidence) with evidence suggesting an Antarctic contribution of anywhere between 5.4 and 17.8 m.

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An indicative metric for the equilibrium sea level response can be provided by comparing paleo GSAT and GMSL during past multimillennial warm periods (Sections 2.3.1.1, 2.3.3.3 and 9.6.2; Figure 9.9). However, caution is needed as the present and past warm periods differ in astronomical and other forcings (Cross-chapter Box 2.1) and in terms of polar amplification. The Last Interglacial (likely 5–10 m higher GMSL than today and 0.5°C–1.5°C warmer than 1850–1900; Section 9.6.2; Table 9.6) is consistent with the Clark et al. (2016) projections for the 10,000-year commitment associated with 1.5°C of warming. Similarly, the Mid-Pliocene Warm Period (very likely 5–25 m higher GMSL than today and very likely 2.5°C–4°C warmer) (Section 9.6.2; Table 9.6) is consistent with the range of 10,000-year commitments associated with 2.5–4°C of warming, but GMSL reconstructions provide only a weak, broad constraint on model-based projections. An additional paleo constraint comes from the Early Eocene Climatic Optimum, which indicates that 10–18°C of warming is associated with ice-free conditions and a likely GMSL rise of 70–76 m (Sections 2.3.3 and 9.6.2). Together with model-based projections (Clark et al., 2016; Van Breedam et al., 2020), this period suggests that commitment to ice-free conditions would occur for peak warming of about 7°C–13°C (medium agreement, limited evidence).

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Model-based estimates indicate that wetland CH4 emissions were reduced by 24–40% during the Last Glacial Maximum (LGM) when compared to pre-industrial, while CH4 emissions related to biomass burning (wildfires) decreased by 35–75% (Valdes et al., 2005; Hopcroft et al., 2017; Kleinen et al., 2020). N2O emissions decreased by about 30% during the LGM based on data-constrained model estimates (Schilt et al., 2014; H. Fischer et al., 2019) owing to a combination of a weaker hydrological cycle and a generally better ventilated intermediate depth ocean relative to present, reducing (de)nitrification processes (Galbraith et al., 2013; Fischer et al., 2019).

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Geochemical and micropaleontological evidence suggest that intermediate-depth OMZs almost vanished during the Last Glacial Maximum (LGM) (Jaccard et al., 2014). However, multiple lines of evidence suggest with medium confidence that the deep (>1500 m) ocean became depleted in O2 (concentrations were possibly lower than 50 μmol kg–1) globally (Jaccard and Galbraith, 2012; Hoogakker et al., 2015, 2018; Gottschalk et al., 2016, 2020a; Anderson et al., 2019) as a combined result of sluggish ventilation of the ocean subsurface (Gottschalk et al., 2016, 2020a; Skinner et al., 2017) and a generally more efficient marine biological carbon pump (Buchanan et al., 2016; Yamamoto et al., 2019; Galbraith and Skinner, 2020).

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Yokoyama, Y. et al., 2018: Rapid glaciation and a two-step sea level plunge into the Last Glacial Maximum. Nature, 559(7715), 603–607, doi: 10.1038/s41586-018-0335-4.

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Reconstructions of past temperature from paleoclimate proxies (Section 2.3.1.1 and Cross-Chapter Box 2.1) have been used to evaluate modelled past climate temperature change patterns. The AR5 found that CMIP5 (Taylor et al., 2012) models were able to reproduce the large-scale patterns of temperature during the Last Glacial Maximum (LGM) (Flato et al., 2013) and simulated a polar amplification broadly consistent with reconstructions for warm (Pliocene and Eocene) and cold (LGM) periods (Masson-Delmotte et al., 2013). Since AR5, a better understanding of temperature proxies and their uncertainties and in some cases the forcing applied to model simulations has led to better agreement between models and reconstructions over a wide range of past climates. For the Pliocene and Eocene warm periods, understanding of uncertainties in temperature proxies (Hollis et al., 2019; McClymont et al., 2020) and the boundary conditions used in climate simulations (Haywood et al., 2016; Lunt et al., 2017) has improved, and some models now agree better with temperature proxies for these time periods compared to models assessed in AR5 (Sections 7.4.4.1.2, 7.4.4.2.2 and Cross-Chapter Box 2.4; Zhu et al., 2019; Haywood et al., 2020; Lunt et al., 2021). For the Last Interglacial (LIG), improved temporal resolution of temperature proxies (Capron et al., 2017) and better appreciation of the importance of freshwater forcing (Stone et al., 2016) have clarified the reasons behind apparent model-data inconsistencies. Regional LIG temperature responses simulated by CMIP6 are within the uncertainty ranges of reconstructed temperature responses, except in regions where unresolved changes in regional ocean circulation, meltwater, or vegetation changes may cause model mismatches (Otto-Bliesner et al., 2021). For the LGM, the CMIP5 and CMIP6 ensembles compare similarly to new sea surface temperature (SST) and surface air temperature (SAT) proxy reconstructions (Figure 3.2a; Cleator et al., 2020; Tierney et al., 2020b). The very cold CMIP6 LGM simulation by the Community Earth System Model Version 2.1 (CESM2.1) is an exception related to the high equilibrium climate sensitivity (ECS) of that model (Section 7.5.6; Kageyama et al., 2021a; Zhu et al., 2021). Figure 3.2a illustrates the wide range of simulated global LGM temperature responses in both ensembles. CMIP6 models tend to underestimate the cooling over land, but agree better with oceanic reconstructions. For the mid-Holocene, the regional biases found in CMIP5 simulations are similar to those in pre-industrial and historical simulations (Harrison et al., 2015; Ackerley et al., 2017), suggesting common causes. CMIP5 models underestimate Arctic warming in the mid-Holocene (Yoshimori and Suzuki, 2019). CMIP6 models simulate a mid-latitude, subtropical, and tropical cooling compared to the pre-industrial period, whereas temperature proxies indicate a warming (see Section 2.3.1.1.2; Brierley et al., 2020; Kaufman et al., 2020), although accounting for seasonal effects in the proxies may reduce the discrepancy (Bova et al., 2021). Over the past millennium, reconstructed and simulated temperature anomalies, internal variability, and forced response agree well over Northern Hemisphere continents, but those statistics disagree strongly in the Southern Hemisphere, where models seem to overestimate the response (PAGES 2k-PMIP3 group, 2015). That disagreement is partly explained by the lower quality of the reconstructions in the Southern Hemisphere, but model and/or forcing errors may also contribute (Neukom et al., 2018). Figure 3.2b shows that land/sea warming contrast behaves coherently in model simulations across multiple periods, with a slight non-linearity in land warming due to a smaller contribution of snow cover to temperature response in warmer climates. A multivariate assessment of paleoclimate model simulations is carried out in Section 3.8.2.

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Paleoclimate records also allow for model evaluation under conditions different from present-day. The AR5 concluded that models can successfully reproduce to first-order patterns of past precipitation changes during the Last Glacial Maximum (LGM) and mid-Holocene, though simulated precipitation changes during the mid-Holocene tended to be underestimated (Flato et al., 2013). Further analysis of CMIP5 models confirmed these results but has also revealed systematic offsets from the paleoclimate record (DiNezio and Tierney, 2013; Hargreaves and Annan, 2014; Harrison et al., 2014, 2015; Bartlein et al., 2017; Scheff et al., 2017; Tierney et al., 2017). Harrison et al. (2014) concluded that CMIP5 models do not perform better in simulating rainfall during the LGM and mid-Holocene than earlier model versions despite higher resolution and complexity. However, prescribing changes in vegetation and dust was found to improve the match to the paleoclimate record (Pausata et al., 2016; Tierney et al., 2017) suggesting that vegetation feedbacks in the CMIP5 models may be too weak (low confidence) (Hopcroft et al., 2017). Brierley et al. (2020) compared the latitudinal gradient of annual precipitation changes in the European–African sector simulated by CMIP6 models for the mid-Holocene with pollen-based reconstructions and showed that models generally reproduce the direction of changes seen in the reconstructions (Figure 3.11). They do not show a robust signal in area averaged rainfall over most European regions where quantitative reconstructions exist, which is not incompatible with reconstructions. Over the Sahara/Sahel and West Africa regions, where reconstructions suggest positive anomalies during the mid-Holocene, both CMIP5 and CMIP6 models also simulate a rainfall increase, but it is much weaker (see also (Section 3.3.3.2). Overall, however, large discrepancies remain between simulations and reconstructions.

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Despite some improvements (Richter et al., 2014; Nnamchi et al., 2015), biases in the mean state are so large that the mean east–west temperature gradient at the equator along the thermocline remains opposite to observed in two thirds of the CMIP5 models (Section 3.5.1.2.2), which clearly affects the simulation of the Atlantic Niño and associated dynamics (Muñoz et al., 2012; Ding et al., 2015; Deppenmeier et al., 2016). The interhemispheric SST gradient is also systematically underestimated in models, with a too cold mean state in the northern part of the tropical Atlantic ocean and too warm conditions in the South Atlantic basin. The seasonality is poorly reproduced and the wind–SST coupling is weaker than observed so that altogether, and despite AMM-like variability in 20th century climate simulations, AMM is not the dominant Atlantic mode in all CMIP5 models (Liu et al., 2013; Amaya et al., 2017). These biases in mean state translate into biases in modelling the mean ITCZ (Flato et al., 2013). Similar biases were found in experiments using CMIP5 models but with different climate background states, such as Last Glacial Maximum, mid-Holocene and future scenario simulations (Brierley and Wainer, 2018). Analyses of CMIP6 show encouraging results in the representation of Atlantic Niño and AMM modes of variability in terms of amplitude and seasonality. Some models now display reduced biases in the spatial structure of the modes and related explained variance but persistent errors still remain on average in the timing of the modes and in the coupled nature of the modes, that is, the strength of the link between ocean (SST, mixed layer depth) and atmospheric (wind) anomalies (Richter and Tokinaga, 2020), as well as in the Atlantic Ocean equatorial east–west temperature gradient (Section 3.5.1.2.2, Figure 3.24).

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AR5 found significant differences between models in the simulation of mean climate in the CMIP5 ensemble when measured against meteorological reanalyses and observations (Flato et al., 2013), see also Stouffer et al. (2017). The AR5 determined that for the diagnostic fields analysed, the models usually compared similarly against two different reference datasets, suggesting that model errors were generally larger than observational uncertainties or other differences between the observational references. In agreement with previous assessments, the CMIP5 multi-model mean generally performed better than individual models (Annan and Hargreaves, 2011; Rougier, 2016). The AR5 considered 13 atmospheric fields in its assessment for the instrumental period but did not assess multi-variate model performance in other climate domains (e.g., ocean, land, and sea ice). The AR5 found only modest improvement regarding the simulation of climate for two periods of the Earth’s history (the Last Glacial Maximum and the mid-Holocene) between CMIP5 and previous paleoclimate simulations. Similarly, for the modern period only modest, incremental progress was found between CMIP3 and CMIP5 regarding the simulation of precipitation and radiation. The representation of clouds also showed improvement, but remained a key challenge in climate modelling (Flato et al., 2013).

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Overall, the PMIP multi-model means agree very well (within 0.5°C of the assessed range) with GSAT reconstructed from proxies across multiple time periods, spanning a range from 6°C colder than pre-industrial (Last Glacial Maximum) to 14°C warmer than pre-industrial (Early Eocene Climate Optimum) (high confidence). During the orbitally-forced mid-Holocene, the CMIP6 multi-model mean captures the sign of the regional changes in temperature and precipitation in most regions assessed, and there have been some regional improvements compared to AR5 (medium confidence). The limited number of CMIP6 simulations of the LGM hinders model evaluation of the multi-model mean, but for both LGM and mid-Holocene, models tend to underestimate the magnitude of large changes (high confidence). Some long-standing model-data discrepancies, such as a dry bias in North Africa in the mid-Holocene, have not improved in CMIP6 compared with PMIP3 (high confidence).

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Brierley, C.M. and I. Wainer, 2018: Inter-annual variability in the tropical Atlantic from the Last Glacial Maximum into future climate projections simulated by CMIP5/PMIP3. Climate of the Past, 14(10), 1377–1390, doi: 10.5194/cp-14-1377-2018.

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Cleator, S.F., S.P. Harrison, N.K. Nichols, I.C. Prentice, and I. Roulstone, 2020: A new multi-variable benchmark for Last Glacial Maximum climate simulations. Climate of the Past, 16, 699–712, doi: 10.5194/cp-2019-55.

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DiNezio, P.N. et al., 2011: The response of the Walker circulation to Last Glacial Maximum forcing: Implications for detection in proxies. Paleoceanography, 26(3), PA3217, doi: 10.1029/2010pa002083.

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Kageyama, M. et al., 2017: The PMIP4 contribution to CMIP6 – Part 4: Scientific objectives and experimental design of the PMIP4-CMIP6 Last Glacial Maximum experiments and PMIP4 sensitivity experiments. Geoscientific Model Development, 10(11), 4035–4055, doi: 10.5194/gmd-10-4035-2017.

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Kageyama, M. et al., 2021a: The PMIP4 Last Glacial Maximum experiments: preliminary results and comparison with the PMIP3 simulations. Climate of the Past, 17(3), 1065–1089, doi: 10.5194/cp-17-1065-2021.

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Liu, S., D. Jiang, and X. Lang, 2018: A multi-model analysis of moisture changes during the last glacial maximum. Quaternary Science Reviews, 191, 363–377, doi: 10.1016/j.quascirev.2018.05.029.

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Lora, J.M., 2018: Components and mechanisms of hydrologic cycle changes over North America at the Last Glacial Maximum. Journal of Climate, 31(17), 7035–7051, doi: 10.1175/jcli-d-17-0544.1.

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Oster, J.L., D.E. Ibarra, M.J. Winnick, and K. Maher, 2015: Steering of westerly storms over western North America at the Last Glacial Maximum. Nature Geoscience, 8(3), 201–205, doi: 10.1038/ngeo2365.

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Otto-Bliesner, B.L. et al., 2007: Last Glacial Maximum ocean thermohaline circulation: PMIP2 model intercomparisons and data constraints. Geophysical Research Letters, 34(12), L12706, doi: 10.1029/2007gl029475.

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Yan, M., B. Wang, and J. Liu, 2016: Global monsoon change during the Last Glacial Maximum: a multi-model study. Climate Dynamics, 47(1–2), 359–374, doi: 10.1007/s00382-015-2841-5.

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Zhu, J. et al., 2021: Assessment of Equilibrium Climate Sensitivity of the Community Earth System Model Version 2 Through Simulation of the Last Glacial Maximum. Geophysical Research Letters, 48(3), e2020GL091220, doi: 10.1029/2020gl091220.

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Lowry, D.P. and C. Morrill, 2019: Is the Last Glacial Maximum a reverse analog for future hydroclimate changes in the Americas?Climate Dynamics, 52(7–8), 4407–4427, doi: 10.1007/s00382-018-4385-y.

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Morrill, C., D.P. Lowry, and A. Hoell, 2018: Thermodynamic and Dynamic Causes of Pluvial Conditions During the Last Glacial Maximum in Western North America. Geophysical Research Letters, 45(1), 335–345, doi: 10.1002/2017gl075807.

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Greve, P., M.L. Roderick, and S.I. Seneviratne, 2017: Simulated changes in aridity from the last glacial maximum to 4xCO2. Environmental Research Letters, 12(11), 114021, doi: 10.1088/1748-9326/aa89a3.

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The AR5 reported a decreasing trend of global land monsoon precipitation over the last half-century, with primary contributions from the weakened summer monsoon systems in the Northern Hemisphere (NH). Since AR5, several studies have documented long-term variations and changes in the South and South East Asian summer monsoon (SAsiaM) rainfall. The SAsiaM strengthened during past periods of enhanced summer insolation in the NH, such as the early-to-mid Holocene warm period around 9000 to 6000 years before the present (BP) (Masson-Delmotte et al., 2013; Mohtadi et al., 2016; Braconnot et al., 2019) and weakened during cold periods (high confidence), such as the Last Glacial Maximum (LGM) and Younger Dryas (Shakun et al. , 2007; Cheng et al. , 2012; Dutt et al. , 2015; Chandana et al. , 2018; Hong et al. , 2018; E. Zhang et al. , 2018). These long-time scale changes in monsoon intensity are tightly linked to orbital forcing and changes in high-latitude climate (Braconnot et al. , 2008; Battisti et al. , 2014; Araya-Melo et al. , 2015; Rachmayani et al. , 2016; Bosmans et al. , 2018; E. Zhang et al. , 2018). A weakening trend of the SAsiaM during the last 200 years has been documented based on tree ring oxygen isotope chronology from the northern Indian subcontinent (Xu et al., 2018) and South East Asia (Xu et al., 2013), oxygen isotopes in speleothems from northern India (Sinha et al., 2015), and tree ring width chronologies from the Indian core monsoon region (Shi et al., 2017). Nevertheless, the detection of century-long decreases in regional monsoon rainfall is obscured by the presence of multi-decadal time scale precipitation variations (Turner and Annamalai, 2012; Knutson and Zeng, 2018) which are evident in long-term rain guage records extending back to the early 1800s (Sontakke et al., 2008) and emerge in long-term climate simulations (Braconnot et al., 2019).

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Analysis of observed trends in the characteristics of ARs has been limited. Gershunov et al. (2017) and Sharma and Déry (2019) have shown a rising trend in land-falling AR activity over the west coast of North American since 1948. (Gonzales et al., 2019) have also documented a seasonally-asymmetric warming of ARs affecting the West Coast of the USA since 1980, which has hydrological implications for the timing and magnitude of regional runoff. Longer-term paleoclimate analysis of ARs is even more limited, although Lora et al. (2017) reported that in the last glacial maximum, AR landfalls over the North American west coast were shifted southward compared to the present conditions.

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D’Agostino, R., P. Lionello, O. Adam, and T. Schneider, 2017: Factors controlling Hadley circulation changes from the Last Glacial Maximum to the end of the 21st century. Geophysical Research Letters, 44(16), 8585–8591, doi: 10.1002/2017gl074533.

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DiNezio, P.N. et al., 2011: The response of the Walker circulation to Last Glacial Maximum forcing: Implications for detection in proxies. Paleoceanography, 26(3), PA3217, doi: 10.1029/2010pa002083.

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Donohoe, A., J. Marshall, D. Ferreira, and D. Mcgee, 2013: The relationship between ITCZ location and cross-equatorial atmospheric heat transport: From the seasonal cycle to the last glacial maximum. Journal of Climate, 26(11), 3597–3618, doi: 10.1175/jcli-d-12-00467.1.

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Gasse, F., 2000: Hydrological changes in the African tropics since the Last Glacial Maximum. Quaternary Science Reviews, 19(1–5), 189–211, doi: 10.1016/s0277-3791(99)00061-x.

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Kageyama, M. et al., 2013: Climatic impacts of fresh water hosing under last glacial Maximum conditions: A multi-model study. Climate of the Past, 9(2), 935–953, doi: 10.5194/cp-9-935-2013.

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Lora, J.M., 2018: Components and mechanisms of hydrologic cycle changes over North America at the Last Glacial Maximum. Journal of Climate, 31(17), 7035–7051, doi: 10.1175/jcli-d-17-0544.1.

last glacial maximumresources/ipcc/cleaned_content/wg1/Chapter08/html_with_ids.html#references_p989

Lowry, D.P. and C. Morrill, 2019: Is the Last Glacial Maximum a reverse analog for future hydroclimate changes in the Americas?Climate Dynamics, 52(7), 4407–4427, doi: 10.1007/s00382-018-4385-y.

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McGee, D., A. Donohoe, J. Marshall, and D. Ferreira, 2014: Changes in ITCZ location and cross-equatorial heat transport at the Last Glacial Maximum, Heinrich Stadial 1, and the mid-Holocene. Earth and Planetary Science Letters, 390, 69–79, doi: 10.1016/j.epsl.2013.12.043.

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Morrill, C., D.P. Lowry, and A. Hoell, 2018: Thermodynamic and Dynamic Causes of Pluvial Conditions During the Last Glacial Maximum in Western North America. Geophysical Research Letters, 45(1), 335–345, doi: 10.1002/2017gl075807.

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Novello, V.F. et al., 2017: A high-resolution history of the South American Monsoon from Last Glacial Maximum to the Holocene. Scientific Reports, 7(1), 1–8, doi: 10.1038/srep44267.

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Oster, J.L., D.E. Ibarra, M.J. Winnick, and K. Maher, 2015: Steering of westerly storms over western North America at the Last Glacial Maximum. Nature Geoscience, 8(3), 201–205, doi: 10.1038/ngeo2365.

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Tierney, J.E. and P.B. DeMenocal, 2013: Abrupt Shifts in Horn of Africa Hydroclimate Since the Last Glacial Maximum. Science, 342(6160), 843–846, doi: 10.1126/science.1240411.

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Yang, S. et al., 2015: Warming-induced northwestward migration of the East Asian monsoon rain belt from the Last Glacial Maximum to the mid-Holocene. Proceedings of the National Academy of Sciences, 112(43), 13178–13183, doi: 10.1073/pnas.1504688112.

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Constraints on the timing and rates of past climate changes have improved since AR5. Analytical methods have increased the precision and reduced sample-size requirements for key radiometric dating techniques, including radiocarbon (Gottschalk et al., 2018; Lougheed et al., 2018) and uranium–thorium dating (Cheng et al., 2013). More accurate ages of many paleoclimate records are also facilitated by recent improvements in the radiocarbon calibration datasets (IntCal20, Reimer et al., 2020). A recent compilation of global cosmogenic nuclide-based exposure dates (Balco, 2020b) allows for a more rigorous assessment of the evolution of glacial landforms since the Last Glacial Maximum (Balco, 2020a).

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Cleator, S.F., S.P. Harrison, N.K. Nichols, I.C. Prentice, and I. Roulstone, 2020: A new multivariable benchmark for Last Glacial Maximum climate simulations. Climate of the Past, 16(2), 699–712, doi: 10.5194/cp-16-699-2020.

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Joos, F., S. Gerber, I.C. Prentice, B.L. Otto-Bliesner, and P.J. Valdes, 2004: Transient simulations of Holocene atmospheric carbon dioxide and terrestrial carbon since the Last Glacial Maximum. Global Biogeochemical Cycles, 18(2), GB2002, doi: 10.1029/2003gb002156.

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Khan, N.S. et al., 2019: Inception of a global atlas of sea levels since the Last Glacial Maximum. Quaternary Science Reviews, 220, 359–371, doi: 10.1016/j.quascirev.2019.07.016.

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Rind, D. and D. Peteet, 1985: Terrestrial Conditions at the Last Glacial Maximum and CLIMAP Sea-Surface Temperature Estimates: Are They Consistent?Quaternary Research, 24(01), 1–22, doi: 10.1016/0033-5894(85)90080-8.

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Murray, L.T. et al., 2014: Factors controlling variability in the oxidative capacity of the troposphere since the Last Glacial Maximum. Atmospheric Chemistry and Physics, 14(7), 3589–3622, doi: 10.5194/acp-14-3589-2014.

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In AR5 (Boucher et al., 2013), there was a recognition that climate feedbacks could be state-dependent (Colman and McAvaney, 2009), but modelling studies that explored this (e.g., Manabe and Bryan, 1985; Voss and Mikolajewicz, 2001; Stouffer and Manabe, 2003; Hansen et al., 2005b) were not assessed in detail. Also in AR5 (Masson-Delmotte et al., 2013), it was assessed that some models exhibited weaker sensitivity to Last Glacial Maximum (LGM; Cross-Chapter Box 2.1) forcing than to 4×CO2 forcing, due to state-dependence in shortwave cloud feedbacks.

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The Last Glacial Maximum (LGM) also gives an opportunity to evaluate model simulation of polar amplification under CO2 forcing, albeit under colder conditions than today (Kageyama et al., 2021). Terrestrial SAT and marine SST proxies exhibit clear polar amplification in the Northern Hemisphere, and the PMIP4 models capture this well (Figure 7.13c,f,i,l), particularly for SAT. There is less proxy data in the mid- to high latitudes of the Southern Hemisphere, but here the models exhibit polar amplification of both SST and SAT. LGM regional model-data agreement is also assessed in (Chapter 3 Section 3.8.2).

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AR5 stated that data and modelling of the Last Glacial Maximum (LGM; Cross-Chapter Box 2.1) indicated that it was very unlikely that ECS lay outside the range 1°C–6°C (Masson-Delmotte et al., 2013). Furthermore, AR5 reported that climate records of the last 65 million years indicated an ECS 95% confidence interval of 1.1 to 7.0 °C.

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Abe-Ouchi, A. et al., 2015: Ice-sheet configuration in the CMIP5/PMIP3 Last Glacial Maximum experiments. Geoscientific Model Development, 8(11), 3621–3637, doi: 10.5194/gmd-8-3621-2015.

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Annan, J.D. and J.C. Hargreaves, 2013: A new global reconstruction of temperature changes at the Last Glacial Maximum. Climate of the Past, 9(1), 367–376, doi: 10.5194/cp-9-367-2013.

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Baggenstos, D. et al., 2019: Earth’s radiative imbalance from the Last Glacial Maximum to the present. Proceedings of the National Academy of Sciences, 116(30), 14881–14886, doi: 10.1073/pnas.1905447116.

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Cleator, S.F., S.P. Harrison, N.K. Nichols, I.C. Prentice, and I. Roulstone, 2020: A new multivariable benchmark for Last Glacial Maximum climate simulations. Climate of the Past, 16(2), 699–712, doi: 10.5194/cp-16-699-2020.

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Hargreaves, J.C., J.D. Annan, M. Yoshimori, and A. Abe-Ouchi, 2012: Can the Last Glacial Maximum constrain climate sensitivity?Geophysical Research Letters, 39(24), 1–5, doi: 10.1029/2012gl053872.

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Hopcroft, P.O. and P.J. Valdes, 2015: How well do simulated last glacial maximum tropical temperatures constrain equilibrium climate sensitivity?Geophysical Research Letters, 42(13), 5533–5539, doi: 10.1002/2015gl064903.

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Kageyama, M. et al., 2021: The PMIP4 Last Glacial Maximum experiments: preliminary results and comparison with the PMIP3 simulations. Climate of the Past, 17(3), 1065–1089, doi: 10.5194/cp-17-1065-2021.

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Schneider von Deimling, T., A. Ganopolski, H. Held, and S. Rahmstorf, 2006: How cold was the Last Glacial Maximum?Geophysical Research Letters, 33(14), L14709, doi: 10.1029/2006gl026484.

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Waelbroeck, C. et al., 2009: Constraints on the magnitude and patterns of ocean cooling at the Last Glacial Maximum. Nature Geoscience, 2(2), 127–132, doi: 10.1038/ngeo411.

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Zhu, J. and C.J. Poulsen, 2021: Last Glacial Maximum (LGM) climate forcing and ocean dynamical feedback and their implications for estimating climate sensitivity. Climate of the Past, 17(1), 253–267, doi: 10.5194/cp-17-253-2021.

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Zhu, J. et al., 2021: Assessment of Equilibrium Climate Sensitivity of the Community Earth System Model Version 2 Through Simulation of the Last Glacial Maximum. Geophysical Research Letters, 48(3), e2020GL091220, doi: 10.1029/2020gl091220.

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At the Last Glacial Maximum (LGM) geological proxies and GIA models indicate that GMSL was 125–134 m below present (Section 2.3.3.3 and Figures 9.17 and 9.18). New studies have not changed AR5’s conclusions regarding the size or timing of the LGM and last glacial termination, but have further examined the LGM sea level budget. Based on a synthesis of multiple prior studies, (Simms et al., 2019) estimated central 67% probability contributions to the LGM lowstand (i.e., lowest levels during the LGM) of 76 ± 7 m from the North American Laurentide Ice Sheet, 18 ± 5 m from the Eurasian Ice Sheet, 10 ± 2 m from Antarctica, 4 ± 1 m from Greenland, 5.5 ± 0.5 m from glaciers, and 2.4 ± 0.3 m due to an increase in ocean density. Of the residual, up to about 1.4 m may be ascribed to groundwater, leaving a shortfall of 16 ± 10 m yet to be allocated among land ice reservoirs or lakes.

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Anderson, J.B., S.S. Shipp, A.L. Lowe, J.S. Wellner, and A.B. Mosola, 2002: The Antarctic Ice Sheet during the Last Glacial Maximum and its subsequent retreat history: A review. Quaternary Science Reviews, 21(1–3), 49–70, doi: 10.1016/s0277-3791(01)00083-x.

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Baggenstos, D. et al., 2019: Earth’s radiative imbalance from the Last Glacial Maximum to the present. Proceedings of the National Academy of Sciences, 116(30), 14881–14886, doi: 10.1073/pnas.1905447116.

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Bentley, M.J. et al., 2014: A community-based geological reconstruction of Antarctic Ice Sheet deglaciation since the Last Glacial Maximum. Quaternary Science Reviews, 100, 1–9, doi: 10.1016/j.quascirev.2014.06.025.

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Du, J., B.A. Haley, and A.C. Mix, 2020: Evolution of the Global Overturning Circulation since the Last Glacial Maximum based on marine authigenic neodymium isotopes. Quaternary Science Reviews, 241, 106396, doi: 10.1016/j.quascirev.2020.106396.

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Fyke, J.G. et al., 2011: A new coupled ice sheet/climate model: description and sensitivity to model physics under Eemian, Last Glacial Maximum, late Holocene and modern climate conditions. Geoscientific Model Development, 4(1), 117–136, doi: 10.5194/gmd-4-117-2011.

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Golledge, N.R. et al., 2013: Glaciology and geological signature of the Last Glacial Maximum Antarctic ice sheet. Quaternary Science Reviews, 78, 225–247, doi: 10.1016/j.quascirev.2013.08.011.

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Khan, S.A. et al., 2016: Geodetic measurements reveal similarities between post–Last Glacial Maximum and present-day mass loss from the Greenland ice sheet. Science Advances, 2(9), e1600931, doi: 10.1126/sciadv.1600931.

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Lambeck, K., H. Rouby, A. Purcell, Y. Sun, and M. Sambridge, 2014: Sea level and global ice volumes from the Last Glacial Maximum to the Holocene. Proceedings of the National Academy of Sciences, 111(43), 15296–15303, doi: 10.1073/pnas.1411762111.

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Obase, T., A. Abe-Ouchi, K. Kusahara, H. Hasumi, and R. Ohgaito, 2017: Responses of Basal Melting of Antarctic Ice Shelves to the Climatic Forcing of the Last Glacial Maximum and CO2 Doubling. Journal of Climate, 30(10), 3473–3497, doi: 10.1175/jcli-d-15-0908.1.

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Simpson, M.J.R., G.A. Milne, P. Huybrechts, and A.J. Long, 2009: Calibrating a glaciological model of the Greenland ice sheet from the Last Glacial Maximum to present-day using field observations of relative sea level and ice extent. Quaternary Science Reviews, 28, 1631–1657, doi: 10.1016/j.quascirev.2009.03.004.

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Chen, A.-A., N.-L. Wang, Z.-M. Guo, Y.-W. Wu, and H.-B. Wu, 2018: Glacier variations and rising temperature in the Mt. Kenya since the Last Glacial Maximum. Journal of Mountain Science, 15(6), 1268–1282, doi: 10.1007/s11629-017-4600-z.

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Müller, J. and F. Joos, 2020: Global peatland area and carbon dynamics from the Last Glacial Maximum to the present – a process-based model investigation. Biogeosciences, 17 (21), 5285–5308, doi:10.5194/bg-17-5285-2020.

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Müller, J. and F. Joos, 2021: Committed and projected future changes in global peatlands – continued transient model simulations since the Last Glacial Maximum. Biogeosciences, 18 (12), 3657–3687, doi:10.5194/bg-18-3657-2021.

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Yu, Z. et al., 2010: Global peatland dynamics since the Last Glacial Maximum. Geophysical Research Letters, 37 (13), doi:10.1029/2010GL043584.

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Das, S. et al., 2019: Identifying climate refugia for 30 Australian rainforest plant species, from the last glacial maximum to 2070. Landscape Ecology, 34 (12), 2883–2896, doi:10.1007/s10980-019-00924-6.

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Worth, J. R. P. et al., 2014: Environmental niche modelling fails to predict Last Glacial Maximum refugia: niche shifts, microrefugia or incorrect palaeoclimate estimates?Glob. Ecol. Biogeogr. , 23 (11), 1186–1197, doi:10.1111/geb.12239.

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Byrne, M.P., A.G. Pendergrass, A.D. Rapp, and K.R. Wodzicki, 2018: Response of the Intertropical Convergence Zone to Climate Change: Location, Width, and Strength. Current Climate Change Reports, 4(4), 355–370, doi: 10.1007/s40641-018-0110-5.

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Liu, Y. et al., 2015b: Obliquity pacing of the western Pacific Intertropical Convergence Zone over the past 282,000 years. Nature Communications, 6(10018), 1–7, doi: 10.1038/ncomms10018.

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Pottapinjara, V., M.S. Girishkumar, R. Murtugudde, K. Ashok, and M. Ravichandran, 2019: On the Relation between the Boreal Spring Position of the Atlantic Intertropical Convergence Zone and Atlantic Zonal Mode. Journal of Climate, 32(15), 4767–4781, doi: 10.1175/jcli-d-18-0614.1.

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Tian, B., 2015: Spread of model climate sensitivity linked to double-Intertropical Convergence Zone bias. Geophysical Research Letters, 42(10), 4133–4141, doi: 10.1002/2015gl064119.

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Ridley, H.E. et al., 2015: Aerosol forcing of the position of the intertropical convergence zone since ad 1550. Nature Geoscience, 8(3), 195–200, doi: 10.1038/ngeo2353.

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The CMIP6 models perform reasonably well in capturing large-scale features of precipitation extremes, including intense precipitation extremes in the intertropical convergence zone (ITCZ), and weak precipitation extremes in dry areas in the tropical regions (Li et al., 2021) but a double-ITCZ bias over the equatorial central and eastern Pacific that appeared in CMIP5 models remains (Section 3.3.2.3). There are also regional biases in the magnitude of precipitation extremes (Kim et al., 2020). The models also have difficulties in reproducing detailed regional patterns of extreme precipitation, such as over the north-east USA (Agel and Barlow, 2020), though they performed better for summer extremes over the USA (Akinsanola et al., 2020). The comparison between climatologies in the observations and in model simulations shows that the CMIP6 and CMIP5 models that have similar horizontal resolutions also have similar model evaluation scores, and their error patterns are highly correlated (Wehner et al., 2020). In general, extreme precipitation in CMIP6 models tends to be somewhat larger than in CMIP5 models (Li et al., 2021), reflecting smaller spatial scales of extreme precipitation represented by slightly higher-resolution models (Gervais et al., 2014). This is confirmed by Kim et al. (2020), who showed that Rx1day and Rx5day simulated by CMIP6 models tend to be closer to point estimates of HadEX3 data (Dunn et al., 2020) than those simulated by CMIP5. Figure 11.14 shows the multi-model ensemble bias in mean Rx1day over the period 1979–2014 from 21 available CMIP6 models when compared with observations and reanalyses. Measured by global land root-mean-square error, the model performance is generally consistent across different observed/reanalysis data products for the extreme precipitation metric (Figure 11.14). The magnitude of extreme area mean precipitation simulated by the CMIP6 models is consistently smaller than the point estimates of HadEX3, but the model values are more comparable to those of areal-mean values (Figure 11.14) of the ERA5 reanalysis or REGEN (Contractor et al., 2020b). Taylor-plot-based performance metrics reveal strong similarities in the patterns of extreme precipitation errors over land regions between CMIP5 and CMIP6 (Srivastava et al., 2020; Wehner et al., 2020) and between annual mean precipitation errors and Rx1day errors for both generations of models (Wehner et al., 2020).

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Mcbride, J. et al., 2015: The 2014 Record Dry Spell at Singapore: An Intertropical Convergence Zone (ITCZ) Drought. Bulletin of the American Meteorological Society, 96(12), S126–S130, doi: 10.1175/bams-d-15-00117.1.

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Bischoff, T. and T. Schneider, 2014: Energetic Constraints on the Position of the Intertropical Convergence Zone. Journal of Climate, 27(13), 4937–4951, doi: 10.1175/jcli-d-13-00650.1.

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Boos, W.R. and R.L. Korty, 2016: Regional energy budget control of the intertropical convergence zone and application to mid-Holocene rainfall. Nature Geoscience, 9(12), 892–897, doi: 10.1038/ngeo2833.

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Byrne, M.P., A.G. Pendergrass, A.D. Rapp, and K.R. Wodzicki, 2018: Response of the Intertropical Convergence Zone to Climate Change: Location, Width, and Strength. Current Climate Change Reports, 4(4), 355–370, doi: 10.1007/s40641-018-0110-5.

intertropical convergence zoneresources/ipcc/cleaned_content/wg1/Chapter08/html_with_ids.html#references_p290

Chiang, J.C.H. and C.M. Bitz, 2005: Influence of high latitude ice cover on the marine Intertropical Convergence Zone. Climate Dynamics, 25(5), 477–496, doi: 10.1007/s00382-005-0040-5.

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Hari, V., G. Villarini, S. Karmakar, L.J. Wilcox, and M. Collins, 2020: Northward Propagation of the Intertropical Convergence Zone and Strengthening of Indian Summer Monsoon Rainfall. Geophysical Research Letters, 47(23), e2020GL089823, doi: 10.1029/2020gl089823.

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Ridley, H.E. et al., 2015: Aerosol forcing of the position of the intertropical convergence zone since AD 1550. Nature Geoscience, 8(3), 195–200, doi: 10.1038/ngeo2353.

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Schneider, T., T. Bischoff, and G.H. Haug, 2014: Migrations and dynamics of the intertropical convergence zone. Nature, 513(7516), 45–53, doi: 10.1038/nature13636.

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Talib, J., S.J. Woolnough, N.P. Klingaman, and C.E. Holloway, 2018: The Role of the Cloud Radiative Effect in the Sensitivity of the Intertropical Convergence Zone to Convective Mixing. Journal of Climate, 31(17), 6821–6838, doi: 10.1175/jcli-d-17-0794.1.

intertropical convergence zoneresources/ipcc/cleaned_content/wg1/Chapter08/html_with_ids.html#references_p1605

Tian, B., 2015: Spread of model climate sensitivity linked to double-Intertropical Convergence Zone bias. Geophysical Research Letters, 42(10), 4133–4141, doi: 10.1002/2015gl064119.

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Despite the documented progress of higher resolution, the model evaluation carried out in subsequent chapters shows that improvements between CMIP5 and CMIP6 remain modest at the global scale (Section 3.8.2; Bock et al., 2020). Lower resolution alone does not explain all model biases, for example, a low blocking frequency (Davini and D’Andrea, 2020) or a wrong shape of the Intertropical Convergence Zone (Tian and Dong, 2020). Model performance depends on model formulation and parameterizations as much as on resolution (Chapters 3, 8 and 10).

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Haug, G.H., K.A. Hughen, D.M. Sigman, L.C. Peterson, and U. Röhl, 2001: Southward Migration of the Intertropical Convergence Zone Through the Holocene. Science, 293(5533), 1304–1308, doi: 10.1126/science.1059725.

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Hwang, Y.-T. and D.M.W. Frierson, 2013: Link between the double-Intertropical Convergence Zone problem and cloud biases over the Southern Ocean. Proceedings of the National Academy of Sciences, 110(13), 4935–4940, doi: 10.1073/pnas.1213302110.

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Tian, B., 2015: Spread of model climate sensitivity linked to double-Intertropical Convergence Zone bias. Geophysical Research Letters, 42(10), 4133–4141, doi: 10.1002/2015gl064119.

intertropical convergence zoneresources/ipcc/cleaned_content/wg2/Chapter12/html_with_ids.html#12.3.1.1_p6

The rainy season in CA will likely experience more pronounced MSD by the end of this century, with a signal for reduced minimum precipitation by mid-century for the June July August (JJA) and September October November (SON) quarters, and a broader second peak is projected, consistent with the future south displacement of the Intertropical Convergence Zone (ITCZ) (high confidence) (Fuentes-Franco et al., 2015; Hidalgo et al., 2017; Maurer et al., 2017; Imbach et al., 2018; Naumann et al., 2018; Ribalaygua et al., 2018; Corrales-Suastegui et al., 2020).

global vegetation modelsresources/ipcc/cleaned_content/wg1/Chapter05/html_with_ids.html#5.2.1.1_p5

Progress since AR5 and SRCCL (IPCC, 2019a) allows more accurate estimates of gross and net fluxes due to the availability of more models, model advancement in terms of inclusiveness of land-use practices, and advanced land-use forcings (Ciais et al., 2013; Klein Goldewijk et al., 2017; Hurtt et al., 2020). In addition, important terminological discrepancies were resolved. First, synergistic effects of land-use change and environmental changes have been identified as a key reason for the large discrepancies between model estimates of the LULUCF flux, explaining up to 50% of differences (Pongratz et al., 2014; Stocker and Joos, 2015; Gasser et al., 2020). Another reason for discrepancies relates to natural fluxes being considered as part of the LULUCF flux when occurring on managed land in the United Nations Framework Convention on Climate Change (UNFCCC) national GHG inventories; these fluxes are considered part of the natural terrestrial sink in global vegetation models and excluded in bookkeeping models (Grassi et al., 2018). LULUCF fluxes following national GHG inventories or Food and Agriculture Organization of the United Nations (FAO) datasets, including recent estimates (Tubiello et al., 2021), are thus excluded from our global assessment, but their comparison against the academic approach is available elsewhere – at the global scale (Jia et al., 2019) and European level (Petrescu et al., 2020).

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Land-use-related component fluxes can be verified by the growing databases of global satellite-based biomass observations in combination with information on remotely sensed land cover change. However, they differ from bookkeeping and modelling with Dynamic Global Vegetation Models (DGVMs) in excluding legacy emissions from pre-satellite-era land-use change and land management, and neglecting soil carbon changes, often focusing on gross deforestation, not regrowth (Jia et al., 2019).

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Thurner, M. et al., 2017: Evaluation of climate-related carbon turnover processes in global vegetation models for boreal and temperate forests. Global Change Biology, 23(8), 3076–3091, doi: 10. 1111/gcb.13660.

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Cantú, A.G. et al., 2018: Evaluating changes of biomass in global vegetation models: the role of turnover fluctuations and ENSO events. Environmental Research Letters, 13(7), 075002, doi: 10.1088/1748-9326/aac63c.

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Sitch, S. et al., 2008: Evaluation of the terrestrial carbon cycle, future plant geography and climate–carbon cycle feedbacks using five Dynamic Global Vegetation Models (DGVMs). Global Change Biology, 14(9), 2015–2039, doi: 10.1111/j.1365-2486.2008.01626.x.

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Shifts in terrestrial biome and changes in ecosystem processes in response to climate change are most frequently projected with dynamic global vegetation models (DGVMs) or land-surface models that form part of ESMs, which use gridded climate variables, atmospheric CO2 concentration and information on soil properties as input variables. Since AR5, most DGVMs have been upgraded to capture carbon–nitrogen cycle interactions (e.g., (Le Quéré et al., 2018), many also include a representation of wildfire and fire–vegetation interactions (Rabin et al., 2017) and a small number now also account for land management (e.g., wood removal from forests and crop fertilisation harvest of irrigation (Arneth et al., 2017). Other forms of disturbance, such as tree mortality, in response to, for example, episodic weather extremes or insect pest outbreaks, are relatively poorly represented or not at all, although they demonstrably impact calculated carbon cycling (Pugh et al., 2019a). Simulated biome shifts are generally in agreement in projecting broad patterns on a global scale but vary greatly regarding the simulated trends in historical and future carbon uptake or losses, both regionally and globally (Chang et al., 2017; Canadell et al., 2021).

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Kelley, D. I. et al., 2013: A comprehensive benchmarking system for evaluating global vegetation models. Biogeosciences, 10 (5), 3313–3340, doi:10.5194/bg-10-3313-2013.

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Sitch, S. et al., 2008: Evaluation of the terrestrial carbon cycle, future plant geography and climate-carbon cycle feedbacks using five Dynamic Global Vegetation Models (DGVMs). Global Change Biology, 14 (9), 2015–2039, doi:10.1111/j.1365-2486.2008.01626.x.

mauna loa observatoryresources/ipcc/cleaned_content/wg1/Chapter05/html_with_ids.html#5.2.1.2_p1

Atmospheric CO2 concentration measurements in remote locations began in 1957 at the South Pole Observatory (SPO) and in 1958 at Mauna Loa Observatory (MLO), Hawaii, USA (Keeling, 1960) (Figure 5.6a). Since then, measurements have been extended to multiple locations around the world (Bacastow et al., 1980; Conway et al., 1994; Nakazawa et al., 1997). In addition, high-density global observations of total column CO2 measurements by dedicated GHG-observing satellites began in 2009 (Yoshida et al., 2013; O’Dell et al., 2018). Annual mean CO2 growth rates are observed to be 1.56 ± 0.18 ppm yr–1 (average and range from 1 standard deviation of annual values) over the 61 years of atmospheric measurements (1959–2019), with the rate of CO2 accumulation almost tripling from an average of 0.82 ± 0.29 ppm yr–1 during the decade of 1960–1969 to 2.39 ± 0.37 ppm yr–1 during the decade of 2010–2019 (Chapter 2). The latter agrees well with that derived for total column (XCO2) measurements by the Greenhouse Gases Observing Satellite (GOSAT; Figure 5.6b). The interannual oscillations in monthly mean CO2 growth rates (Figure 5.6b) show a close relationship with the El Niño–Southern Oscillation (ENSO) cycle (Figure 5.6b) due to the ENSO-driven changes in terrestrial and ocean CO2 sources and sinks on the Earth’s surface (Section 5.2.1.4).

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In situ observations of CO2 generally depict a rising amplitude of the seasonal cycle over the past half century, especially north of about 45°N (Figure 2.30). For example, an amplitude increase of 6 ± 2.6% per decade has been observed at the Barrow surface observatory in Alaska over 1961–2011 (Graven et al., 2013), with slightly slower increases thereafter. Aircraft data north of 45°N exhibit an amplitude increase of 57 ± 7% at 500 mb versus an increase of 26 ± 18% for 35°N–45°N between field campaigns in 1958–1961 and 2009–2011 (Graven et al., 2013). Increases in amplitude for the period 1980–2012 are apparent at eight surface observatories north of 50°N (Piao et al., 2018), related primarily to a larger drawdown in June and July. Trends in seasonal cycle amplitude at lower latitudes are smaller (if present at all); for instance, the increase at the Mauna Loa observatory in Hawaii since the early 1960s is only about half as large as at Barrow (Graven et al., 2013), and only one other low-latitude observatory has a significant increase from 1980–2012 (Piao et al., 2018). There is a weak signal of an increase in amplitude at the Sinhagad observatory in western India in recent years (Chakraborty et al., 2020). Generally speaking, larger increases in the Arctic and boreal regions are indicative of changes in vegetation and carbon cycle dynamics in northern ecosystems (Forkel et al., 2016), though increased carbon uptake can also result from other factors such as warmer- and wetter-than-normal conditions.

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Cross-Chapter Box 5.2, Figure 1 shows the decadal CH4 budget derived from the Global Carbon Project (GCP)-CH4 synthesis for 1980s, 1990s and 2000s (Kirschke et al., 2013), and for 2010–2017 (Saunois et al., 2020). The imbalance of the sources and sinks estimated by atmospheric inversions (dark blue bars) can be used to explain the changes in CH4 concentration increase rates between the decades (Table 5.2).

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This section evaluates concentration-driven historical simulations of changes in land and ocean cumulative carbon uptake, against observation-based estimates from the Global Carbon Project (GCP; Le Quéré et al., 2018a). For each model, common historical land-use changes were prescribed (Jones et al., 2016a).

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Since AR5, atmospheric inversion studies have further tested or constrained models, while new datasets have been used to constrain specific parts of the terrestrial carbon cycle such as plant respiration (Huntingford et al., 2017). Figure 3.31 compares historical emissions-driven CMIP6 simulations of global mean atmospheric CO2 concentration and net ocean and land carbon fluxes to the assessed CO2 concentration and fluxes from the Global Carbon Project (Friedlingstein et al., 2019). For 2014, the CMIP6 models simulate a range of CO2 concentrations centred around the observed value of 397 ppmv, with a range of 381 to 412 ppmv. GSAT anomalies simulated over the historical period are very similar in models that simulate or prescribe changes in atmospheric CO2 concentrations (Figures 3.31b and 3.4a). Most models simulate realistic temporal evolution of the global net ocean and land carbon fluxes, although model spread is larger over land (Figure 3.31c,d; see also Sections 3.6.2 and 5.4.5.2, and Figure 5.24). Although literature published soon after AR5 highlighted the importance of representing nitrogen limitation on plant growth (Peng and Dan, 2015; R.Q. Thomas et al., 2015), more recent studies note that models without nitrogen limitation can still be consistent with the latest estimates of historical carbon cycle changes (Arora et al., 2020; Meyerholt et al., 2020). Uncertainties in the photosynthetic response to atmospheric CO2 concentrations at global scales, shifts in carbon allocation and turnover, land-use change (Hoffman et al., 2014; Wieder et al., 2019), and water limitation are also important influences on land carbon fluxes.

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Andrew, R. and G. Peters, 2021: The Global Carbon Project’s fossil CO2 emissions dataset. doi:10.5281/zenodo.5569235.

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dTotal non-AFOLU emissions are the sum of total CO2-eq emissions values for energy, industrial sources, waste and other emissions with data from the Global Carbon Project for CO2, including international aviation and shipping, and from the PRIMAP database for CH4 and N2O averaged over 2007–2014, as that was the period for which data were available.

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Land use can affect regional extremes, in particular hot extremes, in several ways (high confidence). This includes effects of land management (e.g., cropland intensification, irrigation, double cropping) as well as of land cover changes (deforestation; Sections 11.3.2 and 11.6). Some of these processes are not well represented (e.g., effects of forest cover on diurnal temperature cycle) or not integrated (e.g., irrigation) in climate models (Sections 11.3.2 and 11.3.3). Overall, the effects of land-use forcing may be particularly relevant in the context of low-emissions scenarios, which include large land-use modifications, for instance those associated with the expansion of biofuels, bioenergy with carbon capture and storage, or re-/afforestation to ensure negative emissions, as well as with the expansion of food production (e.g., SR1.5, Chapter 3; Cross-Chapter Box 5.1 in this Report; van Vuuren et al., 2011; Hirsch et al., 2018). There are also effects on the water cycle through freshwater use (Section 11.6 and Cross-Chapter Box 5.1).

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To hold global temperature rise to well below 2°C and pursue efforts to limit it to 1.5°C as required by the Paris Agreement requires major changes in land use and management. There are many opportunities for NbS, which can provide climate change mitigation and adaptation in ways that protect and restore biodiversity and provide a wide range of benefits to people (Cross-Chapter Box NATURAL in this chapter). There are also new technologies and approaches to develop the bioeconomy in ways which will provide many benefits (Cross-Working Group Box BIOECONOMY in Chapter 5). Nevertheless, renewable energy is a large and essential element of climate change mitigation and there are adverse impacts on biodiversity associated with some types of renewable energy, including wind and solar technologies (Rehbein et al., 2020). However, one of the most serious conflicts emerging is that between land-based approaches to mitigation and the protection of biodiversity, particularly as a result of afforestation strategies and potentially large areas devoted to bioenergy, including bioenergy with carbon capture and storage (BECCS). It is important to recognise the impacts of climate change mitigation at the same time as assessing the direct impacts of climate change, and ensure that adaptation and mitigation are joined up.

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Pour, N., P. A. Webley and P. J. Cook, 2017: A Sustainability Framework for Bioenergy with Carbon Capture and Storage (BECCS) Technologies. Energy Procedia, 114, 6044–6056.

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Finally, while limiting global warming to 1.5°C would minimise the increase in risks in the various water use sectors and keep adaptation effective, many mitigation measures can potentially impact future water security. For example, bioenergy with carbon capture and storage (BECCS) and afforestation and reforestation can have a considerable water footprint if done at inappropriate locations (Section 4.7.6, see also Canadell et al., 2021). Therefore, minimising the risks to water security from climate change will require a full-systems view that considers the direct impacts of mitigation measures on water resources and their indirect effect via limiting climate change (high confidence).

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Muratori, M., et al., 2016: Global economic consequences of deploying bioenergy with carbon capture and storage (BECCS). Environ. Res. Lett. , 11 (9), 95004, doi:10.1088/1748-9326/11/9/095004.

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Evidence of the interactions between ecosystems and resilience come from a range of sources including both regional and sectoral examples (Box 18.2; Tables 18.7–18.8. For example, regional examples suggest that the use of land to produce biofuels could increase the resilience of production systems and address mitigation needs (Box 2.2). Nevertheless, the potential of bioenergy with carbon capture and storage (BECCS) to induce maladaptation needs deeper analysis (Hoegh-Guldberg et al., 2019). Climate Smart Forestry (CSF) in Europe provides an example of the use of sustainable forest management to unlock the EU’s forest sector potential (Nabuurs et al., 2017). This is in response to diverse climate impacts ranging from pressure on spruce stocks in Norway and the Baltics, on regional biodiversity in the Mediterranean region, and the opportunity to use afforestation and reforestation to store carbon in forests (Nabuurs et al., 2019). CSF considers the full value chain from forest to wood products and energy and uses a wide range of measures to provide positive incentives to firmly integrate climate objectives into the forestry sector. CSF has three main objectives; (i) reducing and/or removing greenhouse gas emissions; (ii) adapting and building forest resilience to climate change; and (iii) sustainably increasing forest productivity and incomes (Verkerk et al., 2020).

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Muratori, M., et al., 2020: EMF-33 insights on bioenergy with carbon capture and storage (BECCS). Clim Change, doi:10.1007/s10584-020-02784-5.

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There are concerns about large-scale conversion of non-forest land into forest plantations for the sole purpose of increasing carbon sinks through bioenergy with carbon capture and storage (BECCS) (Heck et al., 2018; Hanssen et al., 2020; Cross-Chapter Box in Chapter 2), which may actually result in negative carbon sink (Jackson et al., 2002; Mureva et al., 2018) and significant loss of overall biodiversity (Abreu et al., 2017). Such large-scale afforestation may also lead to the dispossession of previous users, such as smallholders and pastoralists. Hence, when NbS include forest plantations or other large-scale conversion of land use, there is a risk that they result in maladaptation and malmitigation, including climate injustice (Seddon et al., 2019; Cousins, 2021).

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Hanssen, S., et al., 2020: The climate change mitigation potential of bioenergy with carbon capture and storage. Nat. Clim. Change, 10, 1023–1029.

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The array of evidence of technology learning that has accumulated both before and since AR5 (Thomassen et al. 2020) has prompted investigations about the factors that enable rapid technology learning. From the wide variety of factors considered, unit size has generated the strongest and most robust results. Smaller unit sizes, sometimes referred to as ‘granularity’, tend to be associated with faster learning rates (medium confidence) (Sweerts et al. 2020; Wilson et al. 2020). Examples include solar PV, batteries, heat pumps, and to some extent wind power. The explanatory mechanisms for these observations are manifold and well established: more iterations are available with which to make improvements (Trancik 2006); mass production can be more powerful than economies of scale (Dahlgren et al. 2013); project management is simpler and less risky (Wilson et al. 2020); the ease of early retirement can enable risk-taking for innovative designs (Sweerts et al. 2020); and they tend to be less complicated (Malhotra and Schmidt 2020; Wilson et al. 2020). Small technologies often involve iterative production processes with many opportunities for learning by doing, and have much of the most advanced technology in the production equipment than in the product itself. In contrast, large unit scale technologies – such as full-scale nuclear power, carbon capture and storage (CCS), low-carbon steel making, and negative emissions technologies such as bioenergy with carbon capture and storage (BECCS) – are often primarily built on site and include thousands to millions of parts, such that complexity and system integration issues are paramount (Nemet 2019). Despite the accumulating evidence of the benefits of granularity, these studies are careful to acknowledge the role of other factors in explaining learning. In a study of 41 energy technologies (Figure 2.23), unit size explained 22% of the variation in learning rates (Sweerts et al. 2020) and a study of 31 low-carbon technologies showed that unit size explained 33% (Wilson et al. 2020). Attributing that amount of variation to a single factor is rare in studies of technological change. The large residual has motivated studies, which find that small-scale technologies provide opportunities for rapid change, but they do not make rapid change inevitable; a supportive context, including supportive policy and complementary technologies, can stimulate more favourable technology outcomes ( high confidence).

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The measures required tolimit warming to 2°C (>67%) or lower can result in large-scale transformation of the land surface (high confidence). Pathways limiting warming to 2°C (>67%) or lower are projected to reach net zero CO2 emissions in the AFOLU sector between the 2020s and 2070, with an increase of forest cover of about 322 million ha (–67 to 890 million ha) in 2050 in pathways limiting warming to 1.5°C (>50%) with no or limited overshoot. Cropland area to supply biomass for bioenergy (including bioenergy with carbon capture and storage – BECCS) is around 199 (56–482) million ha in 2050 in pathways limiting warming to 1.5°C (>50%) with no or limited overshoot. The use of bioenergy can lead to either increased or reduced emissions, depending on the scale of deployment, conversion technology, fuel displaced, and how/where the biomass is produced ( high confidence). {3.4}

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Many factors influence the deployment of technologies in the IAMs. Since AR5, there has been fervent debate on the large-scale deployment of bioenergy with carbon capture and storage (BECCS) in scenarios (Fuss et al. 2014; Geden 2015; Anderson and Peters 2016; Smith et al. 2016; van Vuuren et al. 2017; Galik 2020; Köberle 2019). Hence, many recent studies explore mitigation pathways with limited BECCS deployment (Grubler et al. 2018; van Vuuren et al. 2019; Riahi et al. 2021; Soergel et al. 2021a). While some have argued that technology diffusion in IAMs occurs too rapidly (Gambhir et al. 2019), others argued that most models prefer large-scale solutions resulting in a relatively slow phase-out of fossil fuels (Carton 2019). While IAMs are particularly strong on supply-side representation, demand-side measures still lag in detail of representation despite progress since AR5 (Grubler et al. 2018; Lovins et al. 2019; van den Berg et al. 2019; O’Neill et al. 2020b; Hickel et al. 2021; Keyßer and Lenzen 2021). The discount rate has a significant impact on the balance between near-term and long-term mitigation. Lower discount rates <4% (than used in IAMs) may lead to more near-term emissions reductions – depending on the stringency of the target (Emmerling et al. 2019; Riahi et al. 2021). Models often use simplified policy assumptions (O’Neill et al. 2020b) which can affect the deployment of technologies (Sognnaes et al. 2021). Uncertainty in technologies can lead to more or less short-term mitigation (Grant et al. 2021; Bednar et al. 2021). There is also a recognition to put more emphasis on what drives the results of different IAMs (Gambhir et al. 2019) and suggestions to focus more on what is driving differences in result across IAMs (Nikas et al. 2021). As noted by Weyant (2017, p. 131), ‘IAms can provide very useful information, but this information needs to be carefully interpreted and integrated with other quantitative and qualitative inputs in the decision-making process.’

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The IMPs are selected to have different mitigation strategies, which can be illustrated looking at the energy system and emission pathways (Figure 3.7 and Figure 3.8). The mitigation strategies show the different options in emission reduction (Figure 3.7). Each panel shows the key characteristics leading to total GHG emissions, consisting of residual (gross) emissions (fossil CO2 emissions, CO2 emissions from industrial processes, and non-CO2 emissions) and removals (net land-use change, bioenergy with carbon capture and storage – BECCS, and direct air carbon capture and storage – DACCS), in addition to avoided emissions through the use of carbon capture and storage on fossil fuels. The IMP-Neg and IMP-GS scenarios were shown to illustrate scenarios with a significant role of CDR. The energy supply (Figure 3.8) shows the phase-out of fossil fuels in the IMP-LD, IMP-Ren and IMP-SP cases, but a less substantial decrease in the IMP-Neg case. The IMP-GS case needs to make up its slow start by (i) rapid reductions mid-century and (ii) massive reliance on net negative emissions by the end of the century. The CurPol and ModAct cases both result in relatively high emissions, showing a slight increase and stabilisation compared to current emissions, respectively.

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Focusing on cumulative emissions, the right-hand panel of Figure 3.12b shows that for high-end scenarios (C6–C7), most emissions originate from fossil fuels, with a smaller contribution from net deforestation. For C5 and lower, there is also a negative contribution to emissions from both AFOLU emissions and energy systems. For the energy systems, these negative emissions originate from bioenergy with carbon capture and storage (BECCS), while for AFOLU, they originate from reforestation and afforestation. For C3–C5, reforestation has a larger CDR contribution than BECCS, mostly due to considerably lower costs (Rochedo et al. 2018). For C1 and C2, the tight carbon budgets imply in many scenarios more CDR use (Riahi et al. 2021). Please note that net negative emissions are not so relevant for peak-temperature targets, and thus the C1 category, but CDR can still be used to offset the remaining positive emissions (Riahi et al. 2021). While positive CO2 emissions from fossil fuels are significantly reduced, inertia and hard-to-abate sectors imply that in many C1–C3 scenarios, around 800–1000 GtCO2 of net positive cumulative CO2 emissions remain. This is consistent with literature estimates that current infrastructure is associated with 650 GtCO2 (best estimate) if operated until the end of its lifetime (Tong et al. 2019). These numbers are considerably above the estimated carbon budgets for 1.5°C estimated in AR6 WGI, hence explaining CDR reliance (either to offset emissions immediately or later in time).

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Hanssen, S.V. et al., 2020: The climate change mitigation potential of bioenergy with carbon capture and storage. Nat. Clim. Change, 10(11) , 1023–1029, doi:10.1038/s41558-020-0885-y.

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Muratori, M. et al., 2020: EMF-33 insights on bioenergy with carbon capture and storage (BECCS). Clim. Change,, doi:10.1007/s10584-020-02784-5.

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Venton, D., 2016: Core Concept: Can bioenergy with carbon capture and storage make an impact?Proc. Natl. Acad. Sci. , 113(47) , 13260 LP–13262, doi:10.1073/pnas.1617583113.

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Ai, Z., N. Hanasaki, V. Heck, T. Hasegawa, and S. Fujimori, 2021: Global bioenergy with carbon capture and storage potential is largely constrained by sustainable irrigation. Nat. Sustain. , 4(10) , 884–891, doi:10.1038/s41893-021-00740-4.

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Decarbonisation options for shipping and aviation still require R&D, though advanced biofuels, ammonia, and synthetic fuels are emerging as viable options (medium confidence). Increased efficiency has been insufficient to limit the emissions from shipping and aviation, and natural gas-based fuels are likely inadequate to meet stringent decarbonisation goals for these segments ( high confidence). High energy density, low-carbon fuels are required, but they have not yet reached commercial scale. Advanced biofuels could provide low-carbon jet fuel (medium confidence). The production of synthetic fuels using low-carbon hydrogen with CO2 captured through direct air capture (DAC) or bioenergy with carbon capture and storage (BECCS) could provide jet and marine fuels but these options still require demonstration at scale (low confidence). Ammonia produced with low-carbon hydrogen could also serve as a marine fuel (medium confidence). Deployment of these fuels requires reductions in production costs. {10.2, 10.3, 10.4, 10.5, 10.6, 10.8}

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Muratori, M. et al., 2020: EMF-33 insights on bioenergy with carbon capture and storage (BECCS). Clim. Change, 163(3) , 1621–1637, doi:10.1007/s10584-020-02784-5.

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Biofuels come in many forms, including ones that are nearly identical to fossil fuels but sourced from biogenic sources. Solid biomass, either direct from wood chips, lignin or processed pellets, is the most commonly used renewable fuel in industry today and is occasionally used in cement kilns and boilers. Biomethane, biomethanol, and bioethanol are all commercially made today using fermentation and anaerobic digestion techniques and are mostly ‘drop-in’ compatible with fossil fuel equivalents. In principle they cycle carbon in and out of the atmosphere, but their lifecycle GHG intensities are typically not GHG neutral due to land-use changes, soil carbon depletion, fertiliser use, and other dynamics (Hepburn et al. 2019), and are highly case specific. Most commercial biofuel feedstocks come from agricultural (e.g., corn) and food waste sources, and the feedstock is limited; to meet higher levels of biomass use a transition to using higher cellulose feedstocks like straw, switchgrass and wood waste, available in much larger quantities, must be fully commercialised and deployed. Significant efforts have been made to make ethanol from cellulosic biomass, which promises much higher quantities, lower costs, and lower intensities, but commercialisation efforts, with a few exceptions, have largely not succeeded (Padella et al. 2019). The IEA estimates, however, that up to 20% of today’s fossil methane use, including by industry, could be met with biomethane (IEA 2020g) by 2040, using a mixture of feedstocks and production techniques. Biofuel use may also be critical for producing negative emissions when combined with carbon capture and storage (i.e., bioenergy with carbon capture and storage – BECCS). Most production routes for biofuels, biochemicals and biogas generate large side streams of concentrated CO2 which is easily captured, and which could become a source of negative emissions (Sanchez et al. 2018) (Section11.3.6). Finally, it should be noted that biofuel combustion can, if inadequately controlled, have substantial negative local air quality effects, with implications for SDGs 3, 7 and 11.

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Bysequestering carbon in biomass and soils, soil carbon management, and other terrestrial strategies could offset hard-to-reduce emissions in other sectors. However, large-scale bioenergy deployment could increase risks of desertification, land degradation, and food insecurity (IPCC 2019a), and higher water withdrawals (Hasegawa et al. 2018; Fuhrman et al. 2020), though this may be at least partially offset by innovation in agriculture, diet shifts and plant-based proteins contributing to meeting demand for food, feed, fibre and bioenergy (or bioenergy with carbon capture and storage (BECCS) with CCS) (Havlik et al. 2014; Popp et al. 2017; Köberle et al. 2020) (Chapters 5 and 7).

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Net-zero energy systems will share common characteristics, but the approach in every country will depend on national circumstances. Common characteristics of net-zero energy systems will include: (i) electricity systems that produce no net CO2 or remove CO2 from the atmosphere; (ii) widespread electrification of end uses, including light-duty transport, space heating, and cooking; (iii) substantially lower use of fossil fuels than today; (iv) use of alternative energy carriers such as hydrogen, bioenergy, and ammonia to substitute for fossil fuels in sectors less amenable to electrification; (v) more efficient use of energy than today; (vi) greater energy system integration across regions and across components of the energy system; and (vii) use of CO2 removal (e.g., direct air carbon capture and storage (DACCS) and bioenergy with carbon capture and storage (DACCS, BECCS)) to offset any residual emissions. (high confidence) {6.6}

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Brack, D. and R. King, 2020: Net-zero and Beyond: What Role for Bioenergy with Carbon Capture and Storage?Chatham House, London, UK, 25 pp.

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Fridahl, M., and M. Lehtveer, 2018: Bioenergy with carbon capture and storage (BECCS): Global potential, investment preferences, and deployment barriers. Energy Res. Soc. Sci. , 42, 155–165, doi:10.1016/j.erss.2018.03.019.

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Haikola, S., A. Hansson, and J. Anshelm, 2019: From polarization to reluctant acceptance–bioenergy with carbon capture and storage (BECCS) and the post-normalization of the climate debate. J. Integr. Environ. Sci. , 16(1) , 45–69, doi:10.1080/1943815X.2019.1579740.

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Hanssen, S.V. et al., 2020: The climate change mitigation potential of bioenergy with carbon capture and storage. Nat. Clim. Change, 10(11) , 1023–1029, doi:10.1038/s41558-020-0885-y.

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Melara, A.J., U. Singh, and L.M. Colosi, 2020: Is aquatic bioenergy with carbon capture and storage a sustainable negative emission technology? Insights from a spatially explicit environmental life-cycle assessment. Energy Convers. Manag. , 224, 113300, doi:10.1016/j.enconman.2020.113300.

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Muratori, M., K. Calvin, M. Wise, P. Kyle, and J. Edmonds, 2016: Global economic consequences of deploying bioenergy with carbon capture and storage (BECCS). Environ. Res. Lett. , 11(9) , 095004, doi:10.1088/1748-9326/11/9/095004.

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Muratori, M. et al., 2020a: EMF-33 insights on bioenergy with carbon capture and storage (BECCS). Clim. Change, 163(3) , 1621–1637, doi:10.1007/s10584-020-02784-5.

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Pour, N., P.A. Webley, and P.J. Cook, 2018: Potential for using municipal solid waste as a resource for bioenergy with carbon capture and storage (BECCS). Int. J. Greenh. Gas Control, 68, 1–15, doi:10.1016/j.ijggc.2017.11.007.

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Torvanger, A., 2019: Governance of bioenergy with carbon capture and storage (BECCS): accounting, rewarding, and the Paris agreement. Clim. Policy, 19, 329–341, doi:10.1080/14693062.2018.1509044.

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Measures are categorised as supply-side activities in: (i) forests and other ecosystems (Section 7.4.2); (ii) agriculture (Section 7.4.3); (iii) bioenergy and other land-based energy technologies (Section 7.4.4); as well as (iv) demand-side activities (Section 7.4.5 and Figure 7.11). Several information boxes are dispersed within the section and provide supporting material, including case studies exploring a range of topics from climate-smart forestry in Europe (Box 7.2), agroforestry in Brazil (Box 7.3), climate-smart village approaches (Box 7.4), farm systems approaches (Box 7.5), mitigation within Indian agriculture (Box 7.6), and bioenergy and BECCS mitigation calculations (Box 7.7). Novel measures, including enhanced weathering and novel foods are covered in Chapter 12, this report. In addition, as mitigation within AFOLU concerns land management and use of land resources, AFOLU measures impact other sectors. Accordingly, AFOLU measures are also discussed in other sectoral chapters within this report, notably demand-side solutions (Chapter 5), bioenergy and bioenergy with carbon capture and storage (BECCS) (Chapter 6), the use of wood products and biomass in buildings (Chapter 9), and CDR measures, food systems and land related impacts, risks and opportunities of mitigation measures (Chapter 12).

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Ai, Z., N. Hanasaki, V. Heck, T. Hasegawa, and S. Fujimori, 2021: Global bioenergy with carbon capture and storage potential is largely constrained by sustainable irrigation. Nat. Sustain. , 4(10) , 884–891, doi:10.1038/s41893-021-00740-4.

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Cabral, R.P., M. Bui, and N. Mac Dowell, 2019: A synergistic approach for the simultaneous decarbonisation of power and industry via bioenergy with carbon capture and storage (BECCS). Int. J. Greenh. Gas Control, 87, 221–237, doi:10.1016/j.ijggc.2019.05.020.

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Donnison, C. et al., 2020: Bioenergy with Carbon Capture and Storage (BECCS): Finding the win–wins for energy, negative emissions and ecosystem services—size matters. GCB Bioenergy, 12(8) , 586–604, doi:10.1111/gcbb.12695.

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Hanssen, S.V. et al., 2020: The climate change mitigation potential of bioenergy with carbon capture and storage. Nat. Clim. Change, 10(11) , 1023–1029, doi:10.1038/s41558-020-0885-y.

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Fridahl, M., 2017: Socio-political prioritization of bioenergy with carbon capture and storage. Energy Policy, 104 (May 2017), pp. 89-99, doi:10.1016/j.enpol.2017.01.050.

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Babin, A., C. Vaneeckhaute, and M.C. Iliuta, 2021: Potential and challenges of bioenergy with carbon capture and storage as a carbon-negative energy source: A review. Biomass and Bioenergy, 146, 105968, doi:10.1016/j.biombioe.2021.105968.

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Bellamy, R., J. Lezaun, and J. Palmer, 2019: Perceptions of bioenergy with carbon capture and storage in different policy scenarios. Nat. Commun. , 10(1) , 743, doi:10.1038/s41467-019-08592-5.

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Fridahl, M., 2017: Socio-political prioritization of bioenergy with carbon capture and storage. Energy Policy, 104, 89–99, doi:10.1016/J.ENPOL.2017.01.050.

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Fridahl, M., R. Bellamy, A. Hansson, and S. Haikola, 2020: Mapping Multi-Level Policy Incentives for Bioenergy With Carbon Capture and Storage in Sweden. Front. Clim. , 2, 25, doi:10.3389/fclim.2020.604787.

bioenergy with carbon capture and storageresources/ipcc/cleaned_content/wg3/Chapter12/html_with_ids.html#references_p389

García-Freites, S., C. Gough, and M. Röder, 2021: The greenhouse gas removal potential of bioenergy with carbon capture and storage (BECCS) to support the UK’s net-zero emission target. Biomass and Bioenergy, 151, 106164, doi:10.1016/j.biombioe.2021.106164.

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Hanssen, S.V. et al., 2019: Biomass residues as twenty-first century bioenergy feedstock—a comparison of eight integrated assessment models. Clim. Chang. 2019 1633, 163(3) , 1569–1586, doi:10.1007/S10584-019-02539-X. Hanssen, S. V et al., 2020: The climate change mitigation potential of bioenergy with carbon capture and storage. Nat. Clim. Change, 10(11) , 1023–1029, doi:10.1038/s41558-020-0885-y.

bioenergy with carbon capture and storageresources/ipcc/cleaned_content/wg3/Chapter12/html_with_ids.html#references_p803

Muratori, M. et al., 2020: EMF-33 insights on bioenergy with carbon capture and storage (BECCS). Clim. Change, 163(3) , 1621–1637, doi:10.1007/s10584-020-02784-5.

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Tanzer, S.E., K. Blok, and A. Ramírez, 2020: Can bioenergy with carbon capture and storage result in carbon negative steel?Int. J. Greenh. Gas Control, 100, 103104, doi:10.1016/j.ijggc.2020.103104.

bioenergy with carbon capture and storageresources/ipcc/cleaned_content/wg3/Chapter12/html_with_ids.html#references_p1076

Torvanger, A., 2019: Governance of bioenergy with carbon capture and storage (BECCS): accounting, rewarding, and the Paris agreement. Clim. Policy, 19(3) , 329–341, doi:10.1080/14693062.2018.1509044.

bioenergy with carbon capture and storageresources/ipcc/wg3/Chapter03/html_with_ids.html#executive-summary_p15

The measures required tolimit warming to 2°C (>67%) or lower can result in large-scale transformation of the land surface (high confidence). Pathways limiting warming to 2°C (>67%) or lower are projected to reach net zero CO2 emissions in the AFOLU sector between the 2020s and 2070, with an increase of forest cover of about 322 million ha (–67 to 890 million ha) in 2050 in pathways limiting warming to 1.5°C (>50%) with no or limited overshoot. Cropland area to supply biomass for bioenergy (including bioenergy with carbon capture and storage – BECCS) is around 199 (56–482) million ha in 2050 in pathways limiting warming to 1.5°C (>50%) with no or limited overshoot. The use of bioenergy can lead to either increased or reduced emissions, depending on the scale of deployment, conversion technology, fuel displaced, and how/where the biomass is produced ( high confidence). {3.4}

bioenergy with carbon capture and storageresources/ipcc/wg3/Chapter03/html_with_ids.html#3.2.2_p6

Many factors influence the deployment of technologies in the IAMs. Since AR5, there has been fervent debate on the large-scale deployment of bioenergy with carbon capture and storage (BECCS) in scenarios (Fuss et al. 2014; Geden 2015; Anderson and Peters 2016; Smith et al. 2016; van Vuuren et al. 2017; Galik 2020; Köberle 2019). Hence, many recent studies explore mitigation pathways with limited BECCS deployment (Grubler et al. 2018; van Vuuren et al. 2019; Riahi et al. 2021; Soergel et al. 2021a). While some have argued that technology diffusion in IAMs occurs too rapidly (Gambhir et al. 2019), others argued that most models prefer large-scale solutions resulting in a relatively slow phase-out of fossil fuels (Carton 2019). While IAMs are particularly strong on supply-side representation, demand-side measures still lag in detail of representation despite progress since AR5 (Grubler et al. 2018; Lovins et al. 2019; van den Berg et al. 2019; O’Neill et al. 2020b; Hickel et al. 2021; Keyßer and Lenzen 2021). The discount rate has a significant impact on the balance between near-term and long-term mitigation. Lower discount rates <4% (than used in IAMs) may lead to more near-term emissions reductions – depending on the stringency of the target (Emmerling et al. 2019; Riahi et al. 2021). Models often use simplified policy assumptions (O’Neill et al. 2020b) which can affect the deployment of technologies (Sognnaes et al. 2021). Uncertainty in technologies can lead to more or less short-term mitigation (Grant et al. 2021; Bednar et al. 2021). There is also a recognition to put more emphasis on what drives the results of different IAMs (Gambhir et al. 2019) and suggestions to focus more on what is driving differences in result across IAMs (Nikas et al. 2021). As noted by Weyant (2017, p. 131), ‘IAms can provide very useful information, but this information needs to be carefully interpreted and integrated with other quantitative and qualitative inputs in the decision-making process.’

bioenergy with carbon capture and storageresources/ipcc/wg3/Chapter03/html_with_ids.html#3.2.5_p7

The IMPs are selected to have different mitigation strategies, which can be illustrated looking at the energy system and emission pathways (Figure 3.7 and Figure 3.8). The mitigation strategies show the different options in emission reduction (Figure 3.7). Each panel shows the key characteristics leading to total GHG emissions, consisting of residual (gross) emissions (fossil CO2 emissions, CO2 emissions from industrial processes, and non-CO2 emissions) and removals (net land-use change, bioenergy with carbon capture and storage – BECCS, and direct air carbon capture and storage – DACCS), in addition to avoided emissions through the use of carbon capture and storage on fossil fuels. The IMP-Neg and IMP-GS scenarios were shown to illustrate scenarios with a significant role of CDR. The energy supply (Figure 3.8) shows the phase-out of fossil fuels in the IMP-LD, IMP-Ren and IMP-SP cases, but a less substantial decrease in the IMP-Neg case. The IMP-GS case needs to make up its slow start by (i) rapid reductions mid-century and (ii) massive reliance on net negative emissions by the end of the century. The CurPol and ModAct cases both result in relatively high emissions, showing a slight increase and stabilisation compared to current emissions, respectively.

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Focusing on cumulative emissions, the right-hand panel of Figure 3.12b shows that for high-end scenarios (C6–C7), most emissions originate from fossil fuels, with a smaller contribution from net deforestation. For C5 and lower, there is also a negative contribution to emissions from both AFOLU emissions and energy systems. For the energy systems, these negative emissions originate from bioenergy with carbon capture and storage (BECCS), while for AFOLU, they originate from reforestation and afforestation. For C3–C5, reforestation has a larger CDR contribution than BECCS, mostly due to considerably lower costs (Rochedo et al. 2018). For C1 and C2, the tight carbon budgets imply in many scenarios more CDR use (Riahi et al. 2021). Please note that net negative emissions are not so relevant for peak-temperature targets, and thus the C1 category, but CDR can still be used to offset the remaining positive emissions (Riahi et al. 2021). While positive CO2 emissions from fossil fuels are significantly reduced, inertia and hard-to-abate sectors imply that in many C1–C3 scenarios, around 800–1000 GtCO2 of net positive cumulative CO2 emissions remain. This is consistent with literature estimates that current infrastructure is associated with 650 GtCO2 (best estimate) if operated until the end of its lifetime (Tong et al. 2019). These numbers are considerably above the estimated carbon budgets for 1.5°C estimated in AR6 WGI, hence explaining CDR reliance (either to offset emissions immediately or later in time).

bioenergy with carbon capture and storageresources/ipcc/wg3/Chapter03/html_with_ids.html#references_p279

Hanssen, S.V. et al., 2020: The climate change mitigation potential of bioenergy with carbon capture and storage. Nat. Clim. Change, 10(11) , 1023–1029, doi:10.1038/s41558-020-0885-y.

bioenergy with carbon capture and storageresources/ipcc/wg3/Chapter03/html_with_ids.html#references_p515

Muratori, M. et al., 2020: EMF-33 insights on bioenergy with carbon capture and storage (BECCS). Clim. Change,, doi:10.1007/s10584-020-02784-5.

bioenergy with carbon capture and storageresources/ipcc/wg3/Chapter03/html_with_ids.html#references_p777

Venton, D., 2016: Core Concept: Can bioenergy with carbon capture and storage make an impact?Proc. Natl. Acad. Sci. , 113(47) , 13260 LP–13262, doi:10.1073/pnas.1617583113.

peat drainageresources/ipcc/cleaned_content/wg1/Chapter05/html_with_ids.html#5.2.1.1_p4

The global net flux from land-use change and land management is composed of carbon fluxes from land-use conversions, land management and changes therein (Pongratz et al., 2018) and is equivalent to the LULUCF fluxes from the agriculture, forestry and other land use (AFOLU) sector (Jia et al., 2019). It consists of gross emissions (loss of biomass and soil carbon in clearing or logging, harvested product decay, emissions from peat drainage and burning, degradation) and gross removals (CO2 uptake in natural vegetation regrowing after harvesting or agricultural abandonment, afforestation). The LULUCF flux relates to direct human interference with terrestrial vegetation, as opposed to the natural carbon fluxes occurring due to interannual variability or trends in environmental conditions (in particular, climate, CO2, and nutrient deposition) (Houghton, 2013).

peat drainageresources/ipcc/cleaned_content/wg3/Chapter07/html_with_ids.html#7.2.2.2_p8

Carbon emissions from peat burning have been estimated based on the Global Fire Emission Database (GFED4s; van der Werf et al. 2017). These were included in the bookkeeping model estimates and added 2.0 GtC over 1960–2019 (e.g., causing the peak in South-East Asia in 1998) (Figure 7.5). Within the Global Carbon Budget (Friedlingstein et al. 2020), peat drainage from agriculture accounted for an additional 8.6 GtC from 1960–2019 according to FAOSTAT (Conchedda and Tubiello, 2020) used by two of the bookkeeping models (Hansis et al. 2015; Gasser et al. 2020).

holocene changesresources/ipcc/cleaned_content/wg1/Chapter02/html_with_ids.html#references_p749

Lamy, F. et al., 2010: Holocene changes in the position and intensity of the southern westerly wind belt. Nature Geoscience, 3(10), 695–699, doi: 10.1038/ngeo959.

global mean surface air temperatureresources/ipcc/cleaned_content/wg1/Chapter03/html_with_ids.html#references_p96

Brown, P.T., W. Li, and S.-P. Xie, 2015: Regions of significant influence on unforced global mean surface air temperature variability in climate models. Journal of Geophysical Research: Atmospheres, 120(2), 480–494, doi: 10.1002/2014jd022576.

global mean surface air temperatureresources/ipcc/cleaned_content/wg1/Chapter07/html_with_ids.html#7.4.2.2_p2

The specific humidity (WV) feedback, also known as the water-vapour feedback, quantifies the change in radiative flux at the TOA due to changes in atmospheric water vapour concentration associated with a change in global mean surface air temperature. According to theory, observations and models, the water vapour increase approximately follows the Clausius–Clapeyron relationship at the global scale with regional differences dominated by dynamical processes (Section 8.2.1; Sherwood et al., 2010a; Chung et al., 2014; Romps, 2014; R. Liu et al., 2018; Schröder et al., 2019). Greater atmospheric water vapour content, particularly in the upper troposphere, results in enhanced absorption of LW and SW radiation and reduced outgoing radiation. This is a positive feedback. Atmospheric moistening has been detected in satellite records (Section 2.3.1.3.3), it is simulated by climate models (Section 3.3.2.2), and the estimates agree within model and observational uncertainty (Soden et al., 2005; Dessler, 2013; Gordon et al., 2013; Chung et al., 2014). The estimate of this feedback inferred from satellite observations is α WV= 1.85 ± 0.32 W m–2°C–1(R. Liu et al., 2018). This is consistent with the value α WV= 1.77 ± 0.20 W m–2°C–1(one standard deviation) obtained with CMIP5 and CMIP6 models (Zelinka et al., 2020).

pco2resources/ipcc/cleaned_content/wg2/Chapter05/html_with_ids.html#references_p650

Durland, E., G. Waldbusser and C. Langdon, 2019: Comparison of larval development in domesticated and naturalized stocks of the Pacific oyster Crassostrea gigas exposed to high pCO2 conditions. Mar. Ecol. Prog. Ser. , 621, 107–125, doi:10.3354/meps12983.

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Duarte, C., et al., 2018: The energetic physiology of juvenile mussels, Mytilus chilensis (Hupe): the prevalent role of salinity under current and predicted pCO2 scenarios. Environ. Pollut. , 242, 156–163, doi:10.1016/j.envpol.2018.06.053.